Modeling Of Forecasting Inflation On Nepal Essay

Modeling Of Forecasting Inflation On Nepal Essay
CHAPTER FOUR
MODELING OF FORECASTING INFLATION IN NEPAL
4.1Introduction
Inflation is a burning economic problem in the developing countries like Nepal that brings adverse
effects like loss of purchasing power of national currency, leading to the aggravation of social
conditions and living standards. This also leads to uncertainty making domestic and foreign
investors reluctant to invest in the economy. Additionally, inflation broadens the country's terms of
trade causing domestic goods and services more expensive in the market. That is why; the monetary
authority of every economy should have the objective of maintaining stable price.
Inflation forecasting plays a central role in monetary policy formulation. Recent international
empirical evidence suggests that with the decline in inflation of recent years, a fairly widespread
phenomenon, the combined dynamics of this variable and its potential predictors, such as money or
different measures of the output gap, has changed, and inflation has become more unpredictable.
Univariate models tend to show a better forecasting capacity than those based on various inflation
theories, such as the Phillips curve. Traditionally, in industrialized countries the Phillips curve has
played a predominant role in inflation forecasting, and according to Stock and Watson (1999),
Atkenson and Ohanian (2001) and Canova, (2002), it would seem to perform better in terms of
forecasting error than other alternative models. In recent years there have
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Factor Affecting Performance of Stock Market
Abstract
This study examines the effects of foreign direct investment, market capitalization and adjusted on
stock market using time series data from 1991 to 2011. A result shows that there is a significant
relationship between foreign direct investment and stock market, as well as there is also a significant
relationship between adjusted saving and stock market but there is insignificant relationship between
market capitalization and stock market. Foreign direct investment, Market capitalization and
Adjusted saving explains 90% of variation in the stock market. It is recommended that the
government can encourage FDI in Pakistan to increase its savings by taking various steps provide
incentives and save foreign investors interest in a ... Show more content on Helpwriting.net ...
The results have shown positive statistically strong relationship between FDI and market
capitalization thus reflecting the complementary role of FDI in the stock market development of
Pakistan. Raza et al. (2012) investigated the role of foreign direct investment in developing host
country's stock markets and to examine whether they are related or not. The results disclosed a
positive impact of foreign direct investment along with other explanatory variables in developing
Stock markets of Pakistan.
Adam and Tweneboah (2008) analyzed the impact of Foreign Direct Investment (FDI) on stock
market development in Ghana. Market capitalization, FDI, stock market development and exchange
rate variable are considered and found long–run relationship between FDI and stock market
development in Ghana. Raza and Jawaid (2012) investigated the effects of foreign capital inflows
and economic growth on stock market capitalization in 18 Asian countries by using the panel data
from the period of 2000–2010 and found that foreign direct investment has significant negative and
economic growth has significant positive relationship with the stock market capitalization, whereas,
the results of workers' remittances is found insignificant in long run. However, no causal
relationship is found in between workers' remittances and stock market capitalization. They
suggested that investor should not idealize the inflow of workers' remittances to

Chapter Four : Research Methodology Essay
CHAPTER FOUR: RESEARCH METHODOLOGY In Chapter 4 will be described the
methodology which was used. In this chapter, we will explain the reasons for choosing this
methodology and give more details about this study. We will explain and present the methods that
help us in this project. An overview of the method that was used to collect the data will be given.
Afterwards, the statistical concepts will be explained thoroughly. 4.1 Data Collection This was a
multicentre, prospective longitudinal cohort study. All eligible people with Dukes A–C colorectal
cancer were approached before primary surgery from 30 NHS cancer treatment centres across the
UK between November 2010 and March 2012. Questionnaires were given whenever possible before
the primary surgery which was the baseline and then after 3, 9, 15 and 24 months. Baseline
questionnaires were handed to the participants by the recruiting clinician or the research nurse and
all the other questionnaires were mailed out to participants. [3] 4.2 Statistical Methods Firstly, we
would like to describe the anxiety and depression data at baseline and at 3, 9, 15, 24 months after
surgery for colorectal cancer. Anxiety was measured with STAI–state scale, depression with CES–D
scale and the relevant question on the EQ–5D™ assessed anxiety and depression together. More
analytically, we will present in a table (Table 2) mean STAI–state scores and mean CES–D scores,
the numbers and the

Estimating Exchange Rate Volatility With Garch Models Essay
Student Number: 159006900
Estimating Exchange Rate Volatility with GARCH Models
Master of Business Analysis and Finance
2016
Department of Economics, University of Leicester
159006900
1 Introduction
?The increased importance being attached to exchange rates is a result of the globalisation of
modern business, the continuing growth in world trade relative to national economies, the trend
towards economic integration and the rapid pace of change in the technology of money transfer.?
(Copeland, Laurence S. 2014). In 21th July 2005, Chinese authority announced that the exchange
rate system changed, from the dollar peg to the floating basket peg system. Recently, since the
volatility in the forex market is growing, which makes there is increase concern about forecasting of
exchange rate movements. (Schnabl, 2008).
It is well–known that currencies exchange rates is difficult to forecast accurately. There are a lot of
models can be used to get relatively accurate estimating result of volatility. Using those models to
research currency exchange rate volatility is necessary. A timely and accurate exchange rate
estimating can provide valuable information in various fields, such as, economy, finance and polity,
which is helpful to get more effective portfolio allocation, better foreign exchange rate investment
result, more accurate assets pricing and more efficient politics administration.
In international finance filed, there are many studies focus on modelling the exchanged

The Foreign Exchange Rate On International Trade And Cross...
1 Introduction
The foreign exchange rate is the rate when domestic currency (for example, Chinese yuan) is used to
exchange foreign currency (for example, us dollar). Volatility of exchange rate has been Kamble and
Honrao (2014) defined as ?the risk associated with unexpected movements in the exchange rate.?
The volatility of exchange rate has great impacts on international trade and cross–country
investment. The increased importance being attached to exchange rate is a result of the globalisation
of modern business, the continuing growth in world trade relative to national economies, the trend
towards economic integration and the rapid pace of change in the technology of money transfer.
(Copeland, Laurence S. 2014). According to the announcement of People?s Bank of China at 21th
July 2005, Chinese exchange rate policies changed from the dollar pegged float to the floating
basket peg system. Recently, since the volatility in the forex market is growing, which makes there
are increase concern about the forecasting of exchange rate movements. (Schnabl, 2008).
It is well–known that currency exchange rates are difficult to forecast accurately. There are a lot of
models can be used for get relatively accurate estimating result of volatility. Using those models to
research currency exchange rate volatility is necessary. A timely and accurate exchange rate
estimating can provide valuable information in various fields, such as, economy, finance and polity,
which is interest

A Criticism On A Migratory Animal : Breeding Season...
A criticism on "Unravelling the annual cycle in a migratory animal: breeding–season habitat loss
drives population declines of monarch butterfly"
Introduction
The hemispheric migration of wildlife is in widespread decline specifically in the Monarch
butterflies (Danaus plexippus). This species is known for its long–distance migration from its non–
breeding location in Mexico to the breeding sites encompassing the south, central, and north regions
of eastern North America. The main context provided in the introduction is that for predicting future
population viability, it is crucial we understand the impact of environmental and anthropogenic
threats on the vital rates of the species at different times and locations in its annual cycle.
Specifically, there could be different environmental threats at the non–breeding and breeding sites
and how they could influence the population could vary depending on how sensitive certain insect
stages are to these disturbances. Thus, understanding the spatiotemporal effects are crucial for
modeling the dynamics of migratory species and for guiding conservation efforts.
In addition to presenting the context for their research, the authors clearly outlined the issues that
they will address. In the ecological context, the main issue involves how environmental factors at
different times could affect the species' abundance. Previous research agrees that anthropogenic
effects such as urbanization and changes in land–use (i.e. agricultural

Reflection Analysis
Evaluating the integral in Equation (37) using the gamma function
∫_0^∞▒〖1/x^(k–w) e〗^(–(υ+i)θ/x) dx=Γ(k–w–1)/[(υ+i)θ]^(k–w–1)
Finally, collecting all of the above evaluations and doing the necessary simplifications, the Renyi
entropy of the Logarithmic–inverse Lindley distribution can be expressed as
Υ_υ [f(x)]= 〖υ ln⁡
(((β–1) θ^2)/(ln⁡
β (1+θ) ))ln〗⁡
〖[∑_(j=0)^∞▒∑_(i=0)^∞▒∑_(k=0)^∞▒∑_(w=0)^∞▒(■(υ+j–1@j)) (■(j@i))(■(i@k))
〖(■(υ@w)) (1–β)〗^j 〖(–1)^i (θ/((1+θ) ))〗^k ] Γ(k–w–1)/[(υ+i)θ]^(k–w–1) 〗/(1–υ) (38)
6. Estimation of the Parameters
In this section we introduce the method of likelihood to estimate the parameters involved and use
them to create confidence intervals for the unknown parameters.
Let x_1,...,x_n be a ... Show more content on Helpwriting.net ...
By using (Eq.42), approximately 100(1–α)% confidence intervals for θ and β can be determined as θ
̂±Z_(α/2) √(V ̂_11 ) β ̂±Z_(α/2) √(V ̂_22 )
where Z_(α/2) is the upper α–th percentile of the standand normal distribution.
7. Data Analysis
The Logarithmic inverse Lindley distribution was applied to two sets of data taken from the
literature with the objective of evaluating its fit relative to other distributions already present in the
literature.
The maximum likelihood method was used to estimate the parameters. For the comparison of the
models, the values of –log⁡
L, the Akaike information criterion (AIC), and the Bayesian information
criterion (BIC), defined respectively by –2 log⁡
L+2q and –2 log⁡
L+q log⁡
〖(n)〗, where q is the
number of estimated parameters and n is the sample size, were taken into account. The most
appropriate model corresponds to that which obtains the lowest value for –log⁡
L, AIC and BIC
The statistics of the Kolmogorov–Smirnov test (KS), the Anderson–Darling test (AD), and the
Cramer–von Mises test (CVM) are also presented, as well as their respective p–values. These tests
observe the differences between the assumed cumulative distribution function and the empirical

cumulative distribution function from the data to verify the fit of the distributions (p–value> 0.05).
The first data set to be studied was used by Nadarajah et al.

Genetic Cluster Number Of Genetic Clusters
2.5 Number of genetic clusters
To infer genetic cluster number (K) in our sample set, we used two Bayesian approaches based on
the clustering method which differed in that they: a) incorporate or not a null allele model, and b)
use a non–spatial or spatial algorithm. We selected this approach because Bayesian models capture
genetic population structure by describing the genetic variation in each population using a separate
joint posterior probability distribution over loci. First, we used STRUCTURE v.2.3.3 (Falush et al.,
2003; Pritchard et al., 2000), which does not incorporate a null allele model, but uses a non–spatial
model based on a clustering method and it is able to quantify the individual genome proportion from
each inferred population. A previous run had been carried out to define what ancestry models (i.e. no
admixture model and admixture model) and allele frequency models (i.e. correlated and
uncorrelated allele frequency models) fit our dataset. All these previous runs were conducted with
locality information prior to improving the detection of structure when this could be weak (Hubisz
et al., 2009). Run parameters of previous simulations included five runs with 50,000 iterations
following a burn–in period of 5,000 iterations for K = 1–10 as number of tested clusters. Before
choosing models to run our dataset we evaluated Evanno's index ΔK (Evanno et al., 2005), to
identify whether different models yielded different K values, implemented in STRUCTURE
HARVESTER

Capital Structure and Firm Performance: Case Study: Listed...
MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH
CITY ––– oOo –––
HUỲNH ANH KIỆT
CAPITAL STRUCTURE AND FIRM PERFORMANCE: CASE STUDY: LISTED COMPANIES
IN HOCHIMINH STOCK EXCHANGE
MASTER THESIS
Ho Chi Minh City – 2010
MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS
HOCHIMINH CITY ––– oOo –––
HUỲNH ANH KIỆT
CAPITAL STRUCTURE AND FIRM PERFORMANCE: CASE STUDY: LISTED COMPANIES
IN HOCHIMINH STOCK EXCHANGE
MAJOR: BUSINESS ADMINISTRATION MAJOR CODE: 60.34.05
MASTER THESIS
INSTRUCTOR : PROFESSOR NGUYỄN ĐÔNG PHONG
Ho Chi Minh City – 2010
ACKNOWLEDGEMENT
I would like to express my deepest gratitude to my research Instructor, Professor Nguyen Dong
Phong for his intensive support, ... Show more content on Helpwriting.net ...
1 1.1 BACKGROUND ..........................................................................................................................
1 1.2 RESEARCH PROBLEMS ...........................................................................................................
3 1.3 RESEARCH OBJECTIVES .........................................................................................................
4 1.4 RESEARCH METHODOLOGY AND SCOPE ..........................................................................
5 1.5 STRUCTURE OF THE STUDY ..................................................................................................
5 CHAPTER 2: LITERATURE REVIEW .................................................................................... 7 2.1

INTRODUCTION ........................................................................................................................ 7 2.2
CAPITAL STRUCTURE ............................................................................................................. 7 2.3
FIRM PERFORMANCE ............................................................................................................ 11 2.4
HYPOTHESIS AND EMPIRICAL MODEL ............................................................................ 12
2.4.1. Model 1: The Leverage Model

Essay On Wifa
In the second part of the thesis, the geometry of the antennas of each WiFi access point is changed to
URA in order to extend the searching area into 2–D search, the well–known subspace MUSIC
algorithm is used for the examination of the received spatial information, and then it estimates each
spatial spectrum in which the Azimuth Angle of Arrival (AOA) and Elevation Angle of Arrival
(EOA) of all the paths at each URA WiFi access point is located. After that, because our system is
considered under very low SNR, a set of spectra at some APs might be influenced, so, a fine–
grained fusion algorithm has been added, it computes the minimum errors between each location in
a known grid dimension and the estimated AOA and EOA at every URA array, ... Show more
content on Helpwriting.net ...
A hence about the novel GBSA–MDL source number estimation algorithm and our contribution in
this thesis was set in the first part of the introduction.
In the second part, the wireless based indoor localization which are RSS, TOA, DOA, and TDOA
techniques have been introduced and the grammatical failure of these methods for indoor
localization under the effect of the high existence of multipath signals. Also, we gave a brief
explanation about data fusion multiple sensor techniques and several related works have been
introduced. We motivated our novel indoor positioning algorithm by showing the significant
addition of the multiple sensor data fusion with the recently mentioned wireless based indoor
techniques and the serious need of the 2–D array geometry in the next 5G. At the end, our
contribution in this dissertation has been included.
Chapter 2: Literature Review: In this part, several basic concepts are introduced. We start our
chapter by explaining the meaning of optimization and its two main categories which are local
optimization and global optimization, also the advantages of using the last mentioned category
compared to the first one is mentioned. Accordingly, the most know newly invented global
optimization algorithms based on nature behaviors like the GA, PSO and GBSA are introduced. In
addition of that, the galaxy based search algorithm is studied well and its

Injury Mortality Theory
Annals of Surgery An Injury Mortality Prediction Based on the Anatomic Injury Scale ––Manuscript
Draft–– Manuscript Number: ANNSURG–D–15–02018 Full Title: An Injury Mortality Prediction
Based on the Anatomic Injury Scale Article Type: Original Study Keywords: Abbreviated Injury
Scale, injury mortality prediction, injury severity score, logarithm injury severity score, new injury
severity score, predictor of mortality, trauma mortality prediction model, trauma scoring.
Manuscript Region of Origin: CHINA Powered by Editorial Manager? and ProduXion Manager?
from Aries Systems Corporation MiniAbstract 2 Mini–Abstract Derive the mortality rate of different
AIS predot codes into the modified coefficient (MC), the IMP, as a new feasible scoring method ...
Show more content on Helpwriting.net ...
White H. A heteroskedasticity–consistent covariance matrix estimator and a direct test for
heteroskedasticity. Econometrica. 1980;48:817–830. 13. MacKenzie EJ, Rivara FP, Jurkovich GJ, et
al. A national evaluation of the effect of trauma–center care on mortality. N Engl J Med.
2006;354:366–378. 14. Shafi S, Friese R, Gentilello L. Moving beyond personnel and process: a
case for incorporating outcome measures in the trauma center designation process. Arch Surg.
2008;143:115–119. 15. Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS
method. J Trauma. 1987;27:370–378. 16. Salottolo K, Settell A, Uribe P, et al. The impact of the
AIS 2005 revision on injury severity scores and clinical outcome measures. Injury. 2009;40:999–
1003. 17. Stewart KE, Cowan LD, Thompson DM. Changing to AIS 2005 and agreement of injury
severity scores in a trauma registry with scores based on manual chart review. Injury. 2011;42: 934–
939. Table 15 TABLE 1. Patient Demographics Patient Characteristics No. of Patients (%) Age 42
(23–62)* Female 407,200 (35.5) Race White, not Hispanic 760,141 (66.2) Black 163,860 (14.3)
Hispanic 128,135 (11.1) Asian 19,129 (1.7) Native American or Alaskan Native 12,663 (1.1) Other
64,431 (5.6) Mechanism of

How Does Foreign Direct Investment Effects on Host...
1.ABSTRACT
The focus of this research is examining the affects of foreign direct investment on economic growth.
Then the research reached this question: How Does Foreign Direct Investment Effects On Host
Country's GDP (Economic Growth)? Firstly, research starting with discussing the potential of FDI to
affect host country's economic growth and argues that two important objectes for FDI affects on
economic growth, inflows of physical capital and technology spillovers, and according to research
the technology spillovers have the stronger effect to enhance economic growth in the host country.
Using cross section analysis with the range of ten years the empirical part of the paper reached a
conclusion that FDI inflows improve economic growth ... Show more content on Helpwriting.net ...
For many years FDI has allowed lots of externalities such as displacement of general knowledge,
level elevation in industrilization, important technology change in production and distribution,
workforce development. Host country's competitiveness increase by FDI's services. Cause when a
country get FDI inflow that means the country attract new capital so its productivity increase. If FDI
would be productive then the capital that coming the country will be stable and long lasting. With
the competition brought by FDI, in the host country's companies effected very efficially from this.
They tend to become more productive and they would be more powerful againts abroad competitor.
In a country when the companies has strong productivity, then the growth ratio in that country is
higher than others. ( Baracaldo (2005)). With the FDI inflow of country, the employment generation
will increase in that country. When the country's productivity increase, employment ratio will
increase and its competitiveness increase, too. FDI contribute to the spillover of technology and
improve knowledge. FDI make them efficient and with this way productivity increase(Ramírez
(2006)). Also, FDI allow bring new goods and services in host country.
Table–1 Inward FDI stocks in high and low growth developing economies High growth | Inward
FDI stock 2000, dollars | Low growth economies | Inward FDI stock per capita in 2002,dollars |
China | ... | Suudi Arabia | 1.159 | Korea | 918 |

Evaluate The Impact Of Human Capital Disclosure On...
Methodology
The paper ascertained the impact of human capital disclosure on shareholders' value using panel
pool, fixed and random models in Nigeria oil and gas companies from 2004 to 2016. The work uses
secondary source of data in an attempt to achieve the set objective of the study and to solve the
problem under study. The secondary data were obtained from the annual financial reports of selected
listed oil and gas companies as released by the Nigerian Stock Exchange over the period 2005 –
2014. Measurement of Variables
The dependent variable; Shareholders' value as used in this study was measured similarly to the one
used by Olayiwola, (2016) which have been widely embraced in the literature as shareholders' value
and is measured ... Show more content on Helpwriting.net ...
(2)
Where:
DPS = Dividend Per Share (proxy for shareholders' value)
HCD = Human Capital Disclosure/Costs (In aggregate)
U = error term
HCR .................... +/–
Human capital costs comprises of Salaries and Wages, Training Cost, Retirement Benefits,
Medical/Health and Labour Turnover
The aggregate of the indices for measuring human capital disclosure shall be regressed against the
dividend per share of companies to determine the impact of labour cost accounting information
disclosure on the profitability potential of oil and gas companies.
Relationship between dividend per share and shareholders' value of oil and gas companies
Dependent Variable: DPS
Method: Panel Least Squares
Date: 06/22/17 Time: 05:19
Sample: 2004 2016
Periods included: 13
Cross–sections included: 9
Total panel (balanced) observations: 117
Variable Coefficient Std. Error t–Statistic Prob.
Salaries & Wages (N '000) 0.333528 0.105231 3.169481 0.0020

Training Cost (N '000) 0.242760 0.098147 2.473436 0.0149
Retirement benefits (N' 000) 0.171664 0.197811 0.867815 0.3874
Pension Provident Fund (N '000) –0.015787 0.119144 –0.132508 0.8948
Medical/Health (N '000) 0.179497 0.095314 1.883211 0.0623
Labour Turnover Ratio –0.014291 0.075641 –0.188930 0.8505
C –2.272480 0.682633 –3.328994 0.0012
R–squared 0.416917 Mean dependent var 2.334626
Adjusted R–squared 0.385112 S.D. dependent var 0.611377
S.E. of regression 0.479410 Akaike info criterion

Econometrics. a Regression Analysis
Question 1: Run the regression Report your answer in the format of equation 5.8 (Chapter 5, p. 152)
in the textbook including and the standard error of the regression (SER). Interpret the estimated
slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of
dollars and LOT is measured in acres. Model 1: OLS estimates using the 832 observations 1–832
Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P–VALUE const
119.575 1.54566 77.362 <0.00001 *** lot 1.38850 0.209083 6.641 <0.00001 *** Mean of
dependent variable = 122.076 Standard deviation of dep. var. = 44.3478 ... Show more content on
Helpwriting.net ...
If this is present it means there is a violation of the constant variance assumption. * The effect of
heteroskedasticity on the OLS estimator is that it is still unbiased. * The effect of heteroskedasticity
on the OLS estimator standard errors are that the results in adjusted robust standard errors cause the
homoskedasticity results to be incorrect standard errors. Question 5: As mentioned in class, one
commonly employed solution to heteroscedasticity is to adjust the standard errors for the possible
presence of heteroskedasticity, i.e. we compute the heteroskedasticity–robust standard errors, which
are also referred to as heteroskedasticity–consistent standard errors. Rerun the regression in part (2)
with the OLS standard errors replaced by heteroskedasticity–robust standard errors. Comment on
the differences between the OLS standard errors in part (2) and the heteroskedasticity–robust
standard errors in this part. * With Homoskadasticity, Part 2 model, with constant variance of error
term: Model 2: OLS estimates using the 832 observations 1–832 Dependent variable: price
VARIABLE COEFFICIENT STDERROR T STAT P–VALUE const 34.6160 4.74177 7.300
<0.00001 *** lot 1.71129 0.148643 11.513 <0.00001 *** bdrm 3.39579 1.36729 2.484
0.01320

Revenue vs. Education in the U.S. and the United Kingdom
4.0. Analysis and Results
In this chapter, statistical results of the revenue vs. education in The USA and in The UK will be
comparatively illustrated. The time period chosen lies between 2008 and 2013 (immediately after
the effects of the financial crisis started to appear, and up until today); firstly, data will be presented
via bar charts and statistical information, and will continue with a regression for each country which
will illustrate the qualitative parameters of the chosen model, and will establish the amount of
influence between the "education level" and "annual income" series. The fixed model (or the
Ordinary Least Square approach) is the most suitable model for our datasets, according to the result
of the Hausman test.
4.1. ... Show more content on Helpwriting.net ...
If we are to look again at the bar charts from the beginning of this chapter, we can also observe the
fact that the increase of the wage, in time, is very slow, and in the case of 2008–2009, a visible
decrease had even been registered.
4.1.3. OLS Regression results
Dependent Variable: MEDIAN_ANNUAL_WAGE
Method: Least Squares
Date: 02/25/14 Time: 12:32
Sample: 1 30
Included observations: 29
Variable Coefficient Std. Error t–Statistic Prob.
C (1) 25545.74*** 4151.713 6.153061 0.0000
C (2) 16162.59*** 1666.441 9.698867 0.0000
R–squared 0.776985 Mean dependent var 58985.59
Adjusted R–squared 0.768725 S.D. dependent var 25899.07
S.E. of regression 12455.14 Akaike info criterion 21.76413
Sum squared resid 4.19E+09 Schwarz criterion 21.85842
Log likelihood –313.5798 F–statistic 94.06801
Durbin–Watson stat 1.147248 Prob(F–statistic) 0.000000

Figure 5: OLS regression results for The USA dataset
The regression results show that the R–square value is 0.776, meaning that the model could be
explained by the independent value in a proportion of 77%. This is a significant value, which reveals
that 77 percent of the overall factors that influence the income are related to the education level of
an individual. The p–value of the model (or probability value) is less than 0.05, which means that
the model is statistically significant; the t–statistics also reveals a significant value of 9.69,

Analysis on Inflation Regression Model
Analysis on Inflation Regression Model
Done by: Hassan Kanaan & Fahim Melki
Presented to: Dr. Gretta Saab
Due on: Tuesday, January 25, 2011
Outline: I. Introduction A. Definition of Variables B. Type of Variables II. Background and
Literature Review A. Inflation and Unemployment B. Inflation and Oil Prices C. Inflation and GDP
D. Inflation and Money Supply III. Analysis A. SPSS 17 analysis B. E–Views 5 analysis IV.
Conclusion and Recommendation V. Indexes A. SPSS17 results Enter and Stepwise (Index 1) B. E–
Views 5 results Stationarity and Granger Causality (Index 2) C. Data Collection (Index 3)
The project that the group will be handling is about Inflation and how can these four ... Show more
As cited in their article; Hamilton reached a conclusion that the increase in oil prices Granger–cause
the downturn in economic activity. However in later years: "Hamilton has proposed a more
complicated measure of oil price changes: the "net oil price increase." The measure distinguishes
between oil price increases that establish new highs relative to recent experience and increases that
simply reverse recent decreases" (2004).
Mahmood Arai, Mats Kinnwall, and Peter Skogman Thoursie wrote a paper on GDP and inflation
mainly concerned with the effect of inflation on GDP. The article titled "Cyclical and causal patterns
of inflation and GDP growth" addressed the following problem that there were "No evidence is
found supporting the view that inflation is in general harmful to GDP growth" (2004). In part IV of
their article, the authors introduced the results of their experiment and it shows that "...potential
inflation effects might be period specific rather than year specific. Another point of this exercise is
that some individual effects might, in principle, being time invariant during the entire sample period
but others

The Effect Of Sale Price Of A House By Lots Size Of...
The purpose of this report is defining the effect on the sale price of a house by lot size of property
and the number of bedrooms. Firstly, basing on a data which contains 450 observations, then we will
show the chart's relationship of the number 3 factors which are bedrooms with the sale price of
houses, the number of bedrooms with lot size, and lot size with the houses' sale price. Secondly, the
model of the sale price of houses will be given and explain how we get that model. Finally, it will
give answers of some question which are does the number of bedrooms have a positive impact on
the average market price of houses with a fixed set of other characteristics? And how much? How
much does the average price of a house increase if the lot size is increased by 1 square foot, with all
other characteristics held constant? As the information of question we know the sale price of a house
is depended on two factors that is the number of bedroom and lot size. There are the chart of
relationship chart between the number of bedrooms and the house's price: We can see that is positive
relationship of the house price with number of bedrooms and the house price with lot size are
positive, then when the number of bedrooms or lot size are increasing then the house's price also
increase. We can predict the coefficient of the number of bedrooms and lot size in house price model
that is positive. The model that shows the relationship of house's price and the number of bedrooms
and lot

Safe Sex Behavior Paper
(is this necessary?): The results from the McEachan et al. study indicate that the standard TPB
model has only a minor chance, between 13.8 and 15.3 percent, of predicating safe sex behavior
(McEachan 2011). Based on these types of findings, researches have encouraged the inclusion of
additional variables to the TPB framework that may help to increase predictability (Conner &
Armitage, 1998). In research undertaken by Turchik & Gidych, six variables were added to extend
the model, the first three; past behaviour, anticipated affect and moral norms, have consistently
shown to increase predictability when using the TPB, furthermore researchers have argued for their
permanent inclusion in the model (Turchik & Gidycz, 2012). The last three, sexual ... Show more
They used the TPB to examine the predictors of having condoms available. Based on past studies of
safe sex behavior, Jellema et al substituted a measure of PBC for self efficacy, also adding
descriptive norms which is how people perceive others to be behaving, personal norms and goal
enjoyment (Jellema et al. 2013). In this more specific study, approximately 35% of the variance in
having condoms available was explained, making it substantially more useful in predicting condoms

A Report On Engle Granger Cointegration Test
4. Empirical Results In this section, we discuss our findings of Engle–Granger cointegration test
which we applied in order to identify whether there is cointegration relationship between dependent
variable – the real non–oil GDP and independent variables – real credit to the private sector and
non–oil sector real effective exchange rate. The steps of the EG approach have been undertaken in
order to obtain the long–run model that explains the relationship between these variables. 4.1. Unit
Root Test First of all, variables should be given in log levels in order to alleviate the problem of
serial correlation and the elasticity of the coefficients. The results of ADF unit root test in levels
concludes that all three variables – seasonally ... Show more content on Helpwriting.net ...
Table Variable name ADF test (1% critical value =–3.557472, N=56), H0: [has a unit root] Inference
t–Statistic Prob.* ln_rgdp_noil_sa –0.202877 0.9314 I(1) ln_rcred_to_ps –0.874036 0.7892 I(1)
ln_reer_noil –0.507243 0.8815 I(1) 5% critical value =–3.557472, N=55, t=0 d(ln_rgdp_noil_sa) –
11.60110 0 I(0) d(ln_cred_to_ps) –9.090784 0 I(0) dln_reer_noil) –5.649022 0 I(0) Sample:
2000Q1:2013Q4 In the Table , d stands for 1st difference, such that d(ln_rgdp_noil_sa) is the result
of the 1st difference ADF unit root test on seasonally adjusted real non–oil GDP and etc. The graphs
below show the trend of the three series through the period from 2000 to 2013 based on level and
1st difference Augmented Dickey Fuller unit root tests, respectively. Figure Figure ADL and
Optimal Lag Selection: From General to Specific After checking for stationarity, autoregressive
distributed lag (ADL) models are estimated and the proper lag length is chosen so as to make the
residuals of our model white noise. As can be seen in the tables on ADLs in Appendix 1, all the
model specifications' residuals according to the Jarque–Bera Histogram–Normality tests, Breusch–
Godfrey serial correlation LM tests, and Breusch–Pagan–Godfrey Heteroskedasticity tests are
normally distributed, serially uncorrelated and homoscedastic, respectively. It shows that all residual
diagnostic parameters are satisfactory for estimating our model. Therefore, the

Definition Of Exponential Distributions
A resulting straight line through the graph of H(t) would suggest that an exponential distribution
would be the favoured choice for these data, while a straight line through the graph of log H(t)would
suggest a weibull distribution for the data. Figure~ref{HazCumHaz}, for patients who filled in a
Home Care or both a Home Care and a Contact Assessment, does not support either distributions.
Part results for outputs from the exponential and weibull distributions for these data are shown in
Appendix~ref{AppendixB}. These results confirm the evidence from Figure~ref{HazCumHaz}
and show that neither distributions fit the data well. Therefore, formal modelling for the 3525
patients who filled in either a Home Care assessment alone, or ... Show more content on
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Further, stepwise regression is also known to pick smaller models than
desired~footnote{http://www.biostat.jhsph.edu/~iruczins/teaching/jf/ch10.pdf}. The criteria used in
the step–wise variable selection process for this study was the Akaike information criterion (AIC).
AIC estimates the quality of each model, relative to each of the other models by estimating the
information lost when a model is used to represent the data. In doing so, it deals with the trade–off
between the goodness of fit of the model and the number of parameters used. When using the AIC
criterion, the model with the least is the preferred model. After using the AIC selection process, any
insignificant variables which were selected by step–wise regression were manually removed from
the model. There was also a need to consider the practicality of the model. Some highly significant
variables resulted in coefficients that were very small for any practical use. For instance, if a
variable resulted in a coefficient of $0.04$ then this coefficient would be $0.04 times$ the baseline
hazard. This would consequently increase mortality by a factor of $e^{0.04}$ days; which is $~1.04
times$ baseline number of days. The resulting increase is very insignificant and could be ignored.
Table~ref{LowCoeffs} consists of 63 variables that were removed from

Foreign Direct Investment Trends Of Kenya
DATAANALYSIS AND INTERPRETATION 4.1 FOREIGN DIRECT INVESTMENT TRENDS
IN KENYA. Kenya has recently experienced a surge in foreign direct investment (FDI) following a
period of substantial declines in FDI inflows near the turn of the century. Net FDI flows to Kenya
have not only been highly volatile but also generally declined in the 1980s and 1990s. Kenya's total
FDI as a percentage of GDP rose from 4.21 percent in 1980 to 7.39 percent in 2000 however this
declined to 5.17 percent in 2006 and currently FDI as a percentage of GDP is a 7.52 percent
(UNCTAD, 2014). The investment wave of the 1980s dwindled in the 1990s as the institutions that
had protected both the economy and the body politic from arbitrary interventions were eroded. The
FDI inflows to Kenya since 2008 have considerably improved from $96Million to $514Million in
2013. In recent years, China has emerged as a key source of FDI in Kenya. Figure 1: FDI Inflows in
Eastern Africa Key: Y axis– Inward FDI Inflows in USD $ millions X axis– Years Kenya's net FDI
inflows compared to its East African neighbours however is poor. In 2012, Tanzania attracted FDIs
worth US$ 1.70 billion. Uganda received US$ 1.72 billion in investment, while Kenya drew in US$
259 million (UNCTAD 2013). Table 1:FDI INFLOWS IN EAST AFRICA. Years USD $ millions
Kenya Uganda Tanzania 2008 96 1 383 729 2009 115 953 842 2010 178 1 813 544 2011 335 1 229
894 2012 259 1 800 1205 2013 514 1 872 1146 (UNCTAD 2014) 4.2 DATA DESCRIPTION In
order

Corporate Tax, Cost of Debt, Cost of Equity and Capital...
Corporate Tax, Cost of Debt, Cost of Equity and Capital Structure: A case study of REITs and
conventional real estate firms in the UK
University of Groningen
Faculty of Economics and Business
BSc International Business
January 2013
Table of contents
1. Introduction 4
2. REITs 7
3. Literature Review 9 3.1 Capital Structure Irrelevance 9 3.2 Present Models 10
4. Data and Methodology 12 4.1 Regression 12
5. Findings and Discussion 16
6. Conclusion 20
7. Appendix 21
8. Bibliography 30
Abstract
In January 2007 the UK adopted the globally successful real estate investment trust (REIT) regime,
allowing real estate firms to adopt the REIT status with the benefit of immediate exemption from ...
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Furthermore, I expect that REITs use relatively less debt for financing, because of the relatively
higher cost of debt.
Already in 1958, Modigliani and Miller have pointed the discussion of capital structure towards the
cost of debt and equity. According to their first proposition, in a world of no corporate taxes and
with perfect markets, financial leverage has no effect on a firm's value. In their second proposition,
they state that the cost of equity equals a linear function defined by the required return on assets and
the cost of debt (Modigliani and Miller, 1958).

As negative aspects of debt, e.g. personal tax loss and bankruptcy costs however do exist in reality,
Miller (1977) elaborates that leverage will either have no or a negative effect on the firm's value,
hence untaxed firms should favor equity.
Nevertheless, firms have used leverage even before corporate taxes have been introduced (Maris and
Elayan, 1990). This implies the existence of some market imperfections, which benefit the use of
debt financing, thus enable a trade–off of the cost and benefits of debt resulting in an optimal capital
structure, where marginal cost equal marginal benefits.
In general, the majority of existing research is set up by taking the security issuance choice as the
dependent variable and then tests empirically for determinants based on data from one type of
companies. It needs to be taken into consideration that security issue decision and capital

Impact of Foreign Aid on Poverty and Economic Development...
CHAPTER ONE INTRODUCTION
This project focuses on the poverty profile in Nigeria, the foreign aids given to the nation to help
alleviate poverty and how it affects the economic development of Nigeria. According to the World
Bank website, "poverty is hunger. It is lack of shelter. Poverty is being sick and not being able to see
a doctor. It is not being able to go to school, not knowing how to read, and not being able to speak
properly. Poverty is not having a job, and is fear for the future, and living one day at a time. It is
losing a child to illness brought about by unclean water. And lastly, it is powerlessness, lack of
representation and freedom."
Poverty is the inability to achieve a certain minimum standard of living. It is ... Show more content
on Helpwriting.net ...
By 1996, it was very obvious that urban poverty had become an increasing problem in Nigeria. For
example, the number of people in poverty increased from 27% in 1980 to 46% in 1985. it declined
slightly to 42% in 1992, and increased very sharply to 67% in 1996. In 1999, estimates showed that
over 70% of Nigerians lived in poverty. The government then declared in November 1999 that the
470 billion naira budget for the year 2000 was "to relieve poverty."
By 1996, Nigeria had become the 13th poorest country in the world and occupied the 142nd rank on
the human development index (HDI) scale. (World Bank, 1996)
With the reforms, the real growth became positive but there was still a question whether the reform
alleviated poverty; how far poverty was reduced.
Foreign aid is the economic help provided to communities of countries due to the occurrence of a
humanitarian crisis or for the achievement of a socioeconomic objective. There are two types of
aids:
Humanitarian aid is the immediate assistance given to individuals, organizations or government for
emergency relief caused by war or natural disasters. Development aid is help given by developed
countries to support economic or social development in developing countries so as to create long
term sustainable economic growth.
The sources of foreign aids include bilateral and multilateral aids. Bilateral aid is given by the
government of one country directly to another. Multilateral aid is aid from an international

The Importance Of Waiting Time To Service
Waiting times to each service for these data sets are shown in Figure~ref{WaitCAOnly} and
Figure~ref{WaitCACombo}, respectively. They seem longer for patients with larger values of
assessment urgency scale; in particular, levels 5 & 6. Level 4, in most cases has the shortest waiting
times to services. Patients with an assigned value of "5" seem to have the longest waiting times.
There seems to be two separate distributions for waiting times to some departments; in which levels
5 & 6 are not given services as quickly as the rest. When a clinician was contacted for expert
advice, he speculated that AUS level 6 could be a group of patients who were excluded from further
clinical intervention. I.e. the group of patients whom clinicians ... Show more content on
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It would be expected that higher levels of AUS; being more urgent, would have the least waiting
times to services.newline Table~ref{AUSDorA} also shows that there were more patients who
died in levels 5 & 6 than in the other levels. In fact, level 5 recorded the most number of deaths
proportionately. This is also counter expectation as one would expect AUS level 6 to have had the
most deaths. begin{table}[H] centering caption{Number of patients who died in each Assessment
urgency level group.} label{AUSDorA} begin{tabular}{|l|l|l|l|l|l|l|l|} hline Assessment Urgency
Scale & 0 & 1 & 2 & 3 & 4 & 5 & 6 hline Alive & 4 & 141 & 28 & 360 & 171 & 100 & 109
hline Dead & 3 & 12 & 6 & 74 & 48 & 158 & 87 hline end{tabular} end{table} From
Figure~ref{CAOnly} and Figure~ref{CACombo}, it is not clear whether the patients with AUS
levels 5 & 6 who have long waiting times for the Community service, say, would probably have
had shorter waiting times for the Emergency service. Therefore, pursuing such an investigation
seemed prudent. Waiting times were calculated by taking the shortest time to any of Emergency,
Community, Inpatient or outpatient. If the

The Effect of Savings Rate in Canada
THE EFFECT OF SAVINGS RATE IN CANADA The impact of savings rate in an economic has
become a very conflicting issue in research and among economist all over the world. This may be
due to the importance of savings generally to the economic growth and development of any nation.
However, the structure of every economy cannot be generalised by a particular economics' variation
because various countries have different social security and pension schemes, and different tax
systems, all of which have an effect on disposable income. In addition, the age of a country's
population, the availability and ease of credit, the overall wealth, and cultural and social factors
within a country all affect savings rates within a particular country. Therefore, ... Show more content
on Helpwriting.net ...
All variables used in the study have been seasonally adjusted. For the period 1983 to 2010, table 1
below shows that SAV, PCI and DR had average values of .20366, 35.4638 and 5.4539 respectively
and also had corresponding standard deviations of .024869, 6.4639 and 3.8434. SAV, which had the
lowest mean and deviation from mean, also had a coefficient of variation of .094204 while PCI and
DR had coefficient of variation of .14290 and .76027 respectively. The high coefficient of variation
of DR implies that there is greater dispersion in the variable than in SAV which has the least
dispersion. Table 1: Statistical Summary Sample period :1983Q1 to 2010Q4 Variable(s) SAV PCI
DR Mean .20366 35.4638 5.4639 Standard Deviation .024869 6.4639 3.8434 Coefficient of
Variation .094204 .14290 .76027 As shown in table 2 below, the correlations between the variables
show that both PCI and DR were positively correlated with SAV. While PCI had a higher correlation
with a value of .34810, DR had a lower correlation with a value of .12820. This correlation indicates
a predictive positive relationship between the variables. It was also observed that RCPY and DR
were negatively correlated with a value of –.86320. Table 2: Estimated Correlation Matrix of

The Impact Of Indian Inflation On The Economy Of Nepal Essay
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Nature and Source of Data
The present study is associated with the utilization of secondary data on Money Supply and Price
Level for the economy of Nepal. The data of concerned variables are taken from various issues of
Economic Bulletin of Nepal Rastra Bank. Quarterly data on money supply and price level ranging
from 1976Q1 to 2012Q2, a total of 143 periods have been used in the present study. The present
study has employed the data sets of money supply and price level transformed in logarithmic form
to minimize the problem of heteroscedasticity.
Besides, the present study utilizes the quarterly data of Indian wholesale price index (WPI)
transformed into logarithmic form to examine the impact of Indian inflation on Nepalese inflation.
The WPIs are taken from Reserve Bank of India (RBI).
Likewise, the present study utilizes the annual data of remittance and population growth to find the
impact of remittance on inflation of Nepal. While analyzing the impact of remittance on inflation,
the annual data of remittance and inflation as well as population growth have been employed. The
political instability is taken as dummy variable while analyzing the relationship between annual
inflation and remittance. The data for remittance are taken from Economic Survey of Nepal and data
associated with population are taken from International Monetary Fund (IMF).
Finally, the impact of anticipated money supply on price level is also analyzed by using

An Outage Occurrence? Based On The Expected Effect Of...
Modeling Overview The models developed for this research focused on addressing the following
question: What is the likelihood of an outage occurrence? Based on the expected effect of weather,
demographics, and socioeconomic features on outages, some hypotheses to be tested are as follows:
Hypothesis #1 – Weather has an influence on power outages. Snow, rain, wind and temperature have
an impactful effect on underground cables, overhead lines, or complete electrical infrastructure that
can cause electrical grids to fail. Weather also effects consumer demand, which drives changes on
the electrical grid. Hypothesis #2 – Income changes will increase/decrease energy consumption and
power outages. Changes in income have an effect on the commercial and residential developments
in the area. The changes affect the max load capabilities of the electrical grid. Income changes have
a hierarchy of purchases. For example, if a group has a change of income that now allows air
conditioner purchases, the electrical load will change quickly. Hypothesis #3 – Ethnicity of a
population has an influence on power outages. Certain ethnicities have generalized electrical usage.
Some groups do not use air conditioners, but others leave the windows open into the winter. That
type of demographic features may be impactful explanatory variables to help predict outages. The
objective of this Capstone is to utilize data mining procedures and the business analytica framework
learned throughout

Variable Selection Via Penalized Likelihood Plays An...
Variable selection via penalized likelihood plays an important role in statistics and machine learning.
In this paper, we first review some classical methods like AIC, BIC, Mallow's Cp, then discuss some
regularization methods including Lasso, adaptive Lasso, elastic–net, adaptive elastic–net and group
Lasso. We also consider the application of regularization methods in generalized linear model, Cox's
model and time series analysis. At the end, we give suggestions for future work. 1 Classical Method
The problem of variable selection is always the center of statistical research. Some classical
methods like best subset selection, forward selection and backward elimination are proposed to
handing massive data set. These methods are basically based on the idea of grid search. For
example, the best subset selection searches the optimal model by all possible combination of the
predictors. The forward selection find the best model by adding one predictor at each time, while the
backward elimination finds the best model by removing one predictor at each time. Although these
methods are powerful and accurate, they also cost a lot. For instance, suppose our model has 10
predictors, if we run the best subset selection to get the optimal model, we need to compare 1024
combinations of the predictors. It is unrealistic to perform such method in our real life because it
time consuming. As a result, more efficient methods to solve the problem of variable selection are
urgently desired. In

Assessing Inflation Risk Of General Insurance Industries
ASSESSING INFLATION RISK IN GENERAL INSURANCE INDUSTRIES BY
MWAKAVI JACKSON K KarU/ACS/413/12
This research project is submitted to the School of Pure and Applied Science of the Karatina
University in partial fulfillment of the requirement for the degree of Science in Actuarial Science.
DECLARATION
This project as presented in this report is my original work and has not been presented for any other
university award.
Candidate: MWAKAVI ... Show more content on Helpwriting.net ...
Last but not least I wish to thank my family for always being there for me throughout the entire
course and specifically for the encouragement they offered me while I was working on my project.
May God reward them impressively.
ABSTRACT
Inflation risk is very virtual to the non–life insurance companies as it has immerse impact on the
claim reserving. This study focuses on modelling of the claim inflation risk based on data provided
by APA automobile insurance. The aim is to show empirically the impact of inflation on claim
reserving and more so to model the claim inflation which help in predicting such future uncertainty.
I will use multiple linear regression model to fit all drivers of claim inflation to obtain a linear
relationship using claim inflation as dependent variable. I'll also use ARIMA model to forecast the
future claim inflation in relative to the past values of claim inflation.
Table of Contents
DECLARATION................................................................................................................i
ACKNOWLEDGEMENT.................................................................................................ii
LIST OF TABLES ............................................................................................................iii

LIST OF

The Concept Of Co-Integration Analysis
Methodology Any co–integration of these variables has been established in relation to the
development of GDP using the Engle–Granger co–integration test. These tests were applied to
selected statistical data from the years 2000 to 2015. The data are quarterly and adjusted for periodic
disturbances. Granger and Engle (1991) made developments in the field of co–integration, which
links long run elements of a pair or of a group of series. It can then be used to discuss some types of
steadiness and to present them into time–series models in impartially unquestionable ways. In light
of this report's objective, the concept of co–integration is used to examine how the loans provided
by banks to private non–financial sector and M3 affected GDP ... Show more content on
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Checking whether it is stationary or non–stationary will be carried out by finding the p values (the
level of significance is set at 0.05), which then shows whether the null hypothesis is rejected or
accepted with 95% probability. For this test, this is expressed as follows: H0: the tested series has a
unit root (non–stationary) H1: the tested series a unit root does not exist (Stationary) Since non–
stationarity can be presumed for the series examined, the option left here is to remove it by
differencing the individual analyzed series. However, researches have proven that this process will
result in the loss of important information on long–term relationships between the elements of time
series. For the examination of unsteady relationships between series, the EG test was hence used,
which can analyze the co–integration of non–stationary time series using the given hypotheses: H0:
Test series are not co–integrated H1: Test series are co–integrated Decisions on the relationship
between time series are based on p values defined by the EG test. If the null hypothesis (p> 0.05) is
not rejected, the time series will be identified as non– co–integrated thus, for series with no long–
term relationship is irrelevant since they have been developed over the long term independently.
Otherwise in cases where p <0.05 the time series will be identified as co–integrated; i.e. for

Descriptive And Bivariate Analyses Were Conducted
Descriptive and bivariate analyses were conducted in SPSS. These correlations were then followed
by a multivariate analyses in SPSS and GeoDa. In GeoDa, a standard OLS model estimation was
conducted in order to determine whether a spatial lag or error model was necessary. A spatial error
model was selected based on the statistical significance of the Lagrange Multiplier and Robust
Lagrange Multiplier, and these are the results presented hereafter. Data was screened in SPSS for
any violation of assumptions prior to analysis, and the assumptions were met. No multicollinearity
was found in the SPSS or GeoDa models. Although a multicollinearity condition number of 60.83
was produced in GeoDa, which is above the acceptable mark of 30 as an indicator of
multicollinearity; however, such values are typical in trend models (Anselin, 2005). Results Table 2
shows the SPSS bivariate results among all the variables in the present study. All variables exhibited
a statistically significance with one another at p < .001. As can be seen, all property characteristics
resulted with positive and statistically significant relationships to home sales values, with the
exception of age of the sold home. Thus, sold homes with AC units and fireplaces, and larger
acreage, basement square footage, and building square footage are related with greater home sale
values; whereas, sold homes that are older are related with lower home sale values. As expected,
greater rates of Blacks, Hispanics,

Nt1310 Unit 2 Case Study Betas
Looking at the betas (Exhibit1) we can clearly define two different segments in the sample.
Segment1 is a "steady" segment, the customers in this group are not much affected by both actions
and external factors, and moreover, they have a positive base. Therefore, they are probably willing
to continue their relationship even without any solicitations from the company. On the other side, we
find Segment2. The customers in this segment are much more unstable as both the action and the
external betas are higher values than in Segment1. Also, this segment shows a negative base, that,
combined with the strong negative external beta coefficient, means the company has to activate
some kind of solicitations in order to maintain the customer, otherwise she is going to be predictably
lost.
Anyway, the members in the second segment are a minority as we can see from the parameter q1,
which ... Show more content on Helpwriting.net ...
Is difficult to read properly the Likelihood Ratio Index and the Akaike Information Criterion without
a comparison, but LRI is a value between 0 and 1 and the higher is better, in our model it is just over
zero; AIC has no an upper limit but it is the lower the better, in our model it is over five hundred.
Though, the most significant indicator is the third, the Hit Ratio. The value itself is not that bad, it is
over sixty percent, but the big concern is that the Hit Ratio of the model is exactly the same as the
proportion of retention in the model. Hence, we would reach the same accuracy in the result just
assuming that all the members of our sample are going to be retained. It could be due to the
combined effects of having a high percentage (q1) of customers in segment1 which is, as we saw in
the betas analysis, the group that naturally tend to be retained. Anyway, all the three indicators
clearly show the ineffectiveness of our

Exchange Rate Volatility Measure And Relative Price
3.2 Exchange Rate Volatility measure and relative price An important issue in this topic is how to
choose the appropriate technique to estimate the exchange rate volatility. However, wide variety of
measures have been discussing in the literature, but there is no right or wrong measure of exchange
rate volatility. Mckenzie (1999) provides a brief over–view of different methods to measure
exchange rate volatility, such average absolute difference between the previous forward and current
spot rate, variance of the spot exchange rate around its trend, absolute percentage change of the
exchange rate and the moving average of the standard deviation of the exchange rate. A moving
standard deviation of nominal or real exchange rate seems to be the most commonly used method in
the empirical literature. Hence, we will construct the moving average standard deviation of the
monthly real exchange rate volatility with the same spirit as Serenis and Tsounis (2014) and a
moving standard deviation of real exchange rate can be expressed as:
Where R_t is logarithm of nominal or real exchange rate and m is the number of periods which can
be range from 4 to 12.In this paper, we will use the moving average of the standard deviation of
exchange rate as the measure of exchange rate volatility by using the real exchange rate and the
order m is set to be 12. Koray and Lastrepes (1989) have shown that the moving average of the
standard deviation of the exchange rate captures the variation in the

Distributed Lag Model For Money Supply And Price...
CHAPTER EIGHT DISTRIBUTED LAG MODEL FOR MONEY SUPPLY AND PRICE
RELATIONSHIP 8.1 Distributed Lag Model The economic variable Y is affected by not only the
value of X at the same time t but also by its lagged values plus some disturbance term i.e.X_t,X_(t–
1),X_(t–2).....,X_(t–k),ε_t.this can be written in the functional form as: 〖Y_t=f(X〗_t,X_(t–
1),X_(t–2).....,X_(t–k),ε_t) In linear form, Y_t=α+β_0 X_t+β_1 X_(t–1)+β_2 X_(t–2)+⋯+β_j X_(t–
k)+ε_t (8.1) Where, β_0 is known as the short run multiplier, or impact multiplier because it gives
the change in the mean value of Y_t following a unit change of X_tin the same time period. If the
change of X_t is maintained at the same level thereafter, then, (β_0+β_1) gives the change in the
mean value of Y_t in the next period, (β_0 + β_1+β_2) in the following period, and so on. These
partial sums are called interim or intermediate multiplier. Finally, after k periods, that is =β,
therefore ∑▒β_i is called the long run multiplier or total multiplier, or distributed–lag multiplier. If
we define the standardized β_i^* = β_i/(∑▒β_i ) then it gives the proportion of the long run, or total,
impact felt by a certain period of time. In order for the distributed lag model to make sense, the lag
coefficients must tend to zero as k. This is not to say that 2 is smaller than 1; it only means
that the impact of X_(t–k)on Y_t must eventually become small as k gets large. The distributed lag
plays

Case Study: ARL Bounds Test For Co-Integration
Table 3: ARDL Bounds Test for Co–integration
Co–integration tests Bound testing for co–integration Diagnostic tests
Models FStatistics Lag R2 DW test 7.3806*** 2,2,2,2,1,1 0.99994 1.9644 5.4298** 2,0,2,0,1,1
0.99503 1.9805 5.4930** 0,0,2,1,2,2 0.98491 2.1880 6.5027*** 2,2,2,2,1,1 0.99994 1.9592
8.0358*** 1,1,2,0,1,1 0.99732 2.0646 4.2303* 1,2,0,0,0,0 0.96578 2.2186 Critical value
Significance level Lower bounds (0) Upper bounds (1)
1% level 4.030 5.598
5% level 2.922 4.268
10% level 2.458 3.647
The critical value according to Narayan (2005) (Case III: Unrestricted intercept and on trend) No
trend, K = 5, (***), (**), (*) denotes Significant at 1%, 5% and 10% respectively.
Table 3, represents the long–run co–integration test ... Show more content on Helpwriting.net ...
Table 5 shows the estimated ARDL error correction approach. The results illustrate most of the
variables in this model as either statistically significant or not significant at any level with an
expected sign. Specifically, food production (dLFD) and annual population growth rate (dLPOP) are
positive and significant at 1% and 5% level of significant respectively. For instance, improvement in
the in food production and annual population growth rate in the short–run are related to
improvement in Cereal Production. As can be seen from the results. Food production has an
immediate impact on cereal production in Nigeria. So, with this analysis, it can be stated that food
production can foster growth of the cereal production and that its effects seem to be quite lasting
over time, although the magnitude is rather small. As a consequence, population growth displays a
prolonged impact on the agricultural productivity in the short–run. However, this finding agrees
with the Malthusiantheory which states that population increase at a faster rate it stimulates urgent
demand for food and increases output. To be exact, improvement of food production by 1% leads to
increase in Cereal production by 10.07%. This findings consistent with the finding by Battisti&

Regression analysis of oil price return
Contents
1.0 Introduction and Motivation 2
2.0 Methodology 5
2.1. Descriptive Statistics 5
2.2 Matrix of pairwise correlation. 6
3.0 Model Specification 6
3.1 Linear Regression Model. 6
3.2 The Regression Specification Error Test 8
3.3 Non–linear models 9
3.4 Autocorrelation. 10
3.5 Heteroskedasticity Test 10
4.0 Hypothesis Testing 11
5.0 Binary (Dummy) Variables 11
6.0 Conclusion 13
Reference List 13
1.0 Introduction and Motivation
Crude oil is one of the world's most important natural resources. Over the past six decades or so,
crude oil – because of the products derived from it, has become highly indispensable in our
everyday lives. Despite being a non–renewable resource, it is still used extensively in power
generation. ... Show more content on Helpwriting.net ...
Through group brainstorming, we came up with a number of variables that theoretically should
affect the price of crude oil, and we used Bloomberg to find data on the same. Our two main criteria
for a "good" variable were statistical significance and R2. We conducted a regression analysis as
well as multiple regression analysis to double check the variables we selected on the Bloomberg
terminal. Moreover, so as to not omit any good variables, we broadened our search to the Oil
commodity section to find relevant industry reports and prospective variables. Through a process of
rough trial and error, and after eliminating several variables due to problems such as
multicollinearity and heteroskedasticity, we finalized the three variables that are mentioned below.
As crude oil is invoiced in USD, it is of interest to note how fluctuations in the value of the USD
affect oil prices. Another of our factors is the price of natural gas, the closest substitute as a source
of energy to oil. Lastly, we seek to establish a relationship between returns in the S&P500 and oil
prices.

We used monthly time–series data over a period of ten years beginning from 2005 for the purpose of
this study. To avoid issues of non–stationary data, we used oil price returns and S&P500 returns.
2.0 Methodology
Our y variable is the percentage monthly return on WTI oil spot prices. West Texas Intermediate
Cushing crude oil price is typically used as the reference spot price in the

What Predictive Modeling Situations Would The Aic...
3 In what predictive modelling situations would the AIC statistic be the most appropriate choice, and
why?
Akaike (1973) adopted the Kullback–Leibler definition of information, I(f;g) , as a natural measure
of discrepancy, or asymmetrical distance, between a "true" model, f(y), and a proposed model, g(y|
β), where β is a vector of parameters. Based on large–sample theory, Akaike derived an estimator for
of the I(f;g) general form 〖AIC〗_m = –2 Ln (L_m ) + 2 〖.k〗_m where Lm is the sample log–
likelihood for the mth of M alternative models and km is the number of independent parameters
estimated for the mth model. The term, , may be viewed as a penalty for over–parameterization. A
min(AIC) strategy is used for selecting among two or more competing models. In a general sense,
the model for which AICm is smallest represents the "best" approximation to the true model. That is,
it is the model with the smallest expected loss of information when MLE's replace true parametric
values in the model. In practice, the model satisfying the min(AIC) criterion may or may not be (and
probably is not) the "true" model since there is no way of knowing whether the "true" model is
included among those being compared. Thus, for example, in comparing four hierarchic linear
regression models, AIC is computed for each model and the min(AIC) criterion is applied to select
the single "best" model.
The choices for the selection criterion have several model fit statistics that are useful for model

Exponential And Weibull Model In Home Care
As shown in previous sections of this chapter, when analysing data, a preliminary exploration may
be made graphically by plotting non–parametric estimates of H(t) and log H(t) to give an informal
check on whether an exponential or weibull model might be adequate. These plots are shown in
Figure~ref{HazCumHaz}. begin{figure}[H] centering includegraphics[width=150mm]
{HazCumHaz.jpeg} caption{Hazard and log hazard functions of time to death for patients with HC
and HCCA assessments.} label{HazCumHaz} end{figure} A resulting straight line through the
graph of H(t) would suggest that an exponential distribution would be the favoured choice for these
data, while a straight line through the graph of log H(t)would suggest a weibull ... Show more
Predictive trees are an excellent choice for data that have features which interact in a complicated
way as the models sub–divide (or partition), the data space into smaller regions, making the
interactions more manageable. This partitioning continues until model can no longer make a better
model than the one previously made for each subset of the data. One extension of the basic tree
methodology is the survival tree, which applies recursive partitioning to censored survival data. The
literature presents several types tree models for censored data. These trees are a more flexible non–
parametric option to survival methods such Cox's proportional hazards methods and AFT models
with more stringent assumptions. The main difference in the various predictive trees is the splitting
criteria. In an article by Segal, M.R. states that, he replaced the traditional splitting criteria for
regression trees for right–censored data with criterion based on variations of the two sample
statistics, in which contrary to common practice at the time; for which within–node separation was
maximised, his algorithm preferred splits that result in large between–node
separation~cite{segal1988regression}. The default criterion in the R package "Rpart", which is
maximized in each split, is the Gini coefficient. The Gini coefficient is a measure of variation in a
set of data.

Examining Genetic and Environmental Effects on Reactive...
Examining Genetic and Environmental Effects on Reactive Versus Proactive Aggression"
Introduction Prior to this study, no other research had studied the genetic and environmental
influences on reactive and proactive aggression. The purpose of this study was to explain how much
genes and (shared and non–shared) environmental factors each contribute to aggression, specifically
proactive and reactive. Once a positive correlation between the two types of aggression was
determined, a "sub–purpose" was to find out if any correlation was due to another common factor,
such as physical aggression. And, which factors are unique to proactive aggression and which are
unique to reactive aggression. The article defines proactive aggression, or ... Show more content on
Helpwriting.net ...
However, there should also be non–overlapping/non–correlated factors contributing as well, as
reactive and proactive aggression have different predictors, associations and 'temperamental and
physiological correlates'. With these new developments, the researchers carried out a separate test,
recalculating a correlation factoring in the possible overlap of physical aggression.
Methods
Sample. The participants of this study were 6–year–olds (N= 72.7 months) selected from another
current study, the Quebec Newborn Twin longitudinal Study. They initially enrolled 648 pairs of
twins but the final sample size was 172 twins (55 monozygotic girls, 48 monozygotic boys, 33
dizygotic girls, and 36 dizygotic boys) after excluding data from different–sex twins and twins who
were in the same class (which might indicate exaggerated similarities between the twins if rated by
the same teacher). The children's reactive and proactive aggression levels were measured using
Dodge and Coie's (1987) assessment, using informant reporting. The informant was the childrens'
kindergarten teacher during the spring (enough time for the teacher to get to know the child) in
his/her preferred language (either English or French; translated twice and approved by binglingual
judges). The children's physical aggression was measured using the Preschool Behavior
Questionnaire developed by Behar & Stringfield (1974),

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