Correlation Between Land Use Land Cover And Water Quality...

Correlation Between Land Use Land Cover And Water Quality...
Why use correlation and regression in your work
It is very important in selecting the right kind of statistical, presentation, and analytical methods to determine the relationships between land–use
land–cover and water quality parameters. This is very important because of the issues associated with spatial autocorrelation and non–independence of
sampling site that often accompany research into water quality and land use.
Literally, autocorrelation is where a variable is said to be correlated with itself and it states that pairs of subjects that are close to each other are more
likely to have values that are more similar, and pairs of subjects far apart from each other are more likely to have values that are less similar. The
spatial structure of the data in autocorrelation refers to any patterns that may exist in this nearness or distant relationships. When data are spatially
autocorrelated, it is possible to predict the value at one location based on the value sampled from a nearby location using interpolation methods. When
autocorrelation does not exist, then the data is said to be independent.
Because of the qualitative nature of water quality analysis, there are a lot variety of statistical analyses that can be performed on water quality data for
reporting and interpretation purposes. Though most of this statistics can be said to be intense, many inferences can be made from simple descriptive
statistics such as mean, minimum, maximum, median, range, and standard
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What Is The Methodology Used In Costimating The Impact Of...
This section gives and explains the methodology that is going to be used in estimating the impact of capital flight on economic growth in Zimbabwe
for the period 1980 to 2015. This encompasses the specification of the model but no specific theory can be attributed to the selection of variables to
be used. Model diagnostic tests are to be conducted before interpretation of estimated results of the correctly specified model. 3.0Methodology There
are quite number of methods of estimating regression functions, the generally used ones being the ordinary least squares (OLS) and the maximum
likelihood (ML). This paper will use (OLS) over (ML) because of the properties of (OLS) that is its ability to produce best linear unbiased estimate thus
... Show more content on Helpwriting.net ...
3.2 Stationarity Test Testing the stationary properties of time series is a very important exercise as the use of stationary time series data in the
Classical Linear Regression Model will result in inflated results. The results are likely to be inconsistent and with a low Durbin Watson (DW)
statistic. Several methods can be employed to test whether the time series variables are stationary , these includes residual plot but this paper will
employ the Augmented Dickey Fuller (ADF) to test the existence of a unit root. Conclusion of stationarity is going to be considered at 1% and 5%
level of significance only. Any probability of each variable below the two values will be considered stationary. If the model fails to meet the stationary
requirement, we will use the differencing method to make our model stationary. 3.3 Model Diagnostic Tests Multicollinearity is a test to assess the
randomness of explanatory variables. They are other tests which include the Auxiliary Regressions and correlation matrix. This study will consider pair
wise correlation coefficient from the correlation matrix. If the pair–wise or zero–order correlation coefficient between two explanatory variables is
high, say in excess of 0.8, then multicollinearity is a serious problem (Gujarati, 2004: 359). In the case that two variables are highly correlated then
one of it must be dropped. For

A Brief Note On Diabetes Prevalence Rate And Socioeconomic...
Diabetes is a major health problem in the United States. There is an increasing interest in the relationship between diabetes and sociodemographic and
lifestyle factors but the extent of the geographical variability of diabetes with respect to these variables still remains unclear. The regression models
commonly used for disease modeling either use Ordinary Least Square (OLS) regression by assuming all the explanatory variables have the same
effect over geographical locations or Geographically Weighted Regression (GWR) that assumes the effect of all the explanatory variables vary over the
geographical space. In reality, the effect of some of the variables may be fixed (global) and other variables vary spatially (local). For this type of ...
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Diabetes is associated with obesity, physical inactivity, race and other socioeconomic covariates (Hipp & Chalise, 2015). There is a steady increase in
type 2 diabetes prevalence especially in adolescents and African Americans (Arslanian, 2000; Arslanian, Bacha, Saad, & Gungor, 2005; Harris, 2001).
Studies of the correlates of diabetes ignore the spatial non–stationarity by either fitting OLS method or using all the variables as nonstationary by fitting
GWR model. A number of studies (Chen, Wu, Yang, & Su, 2010; Dijkstra et al., 2013; Hipp & Chalise, 2015; Siordia, Saenz, & Tom, 2012) used
GWR model to study the association between diabetes and other covariates.
GWR is one of the localized regression techniques which accounts for spatial heterogeneity or spatial non– stationarity (Benson, Chamberlin, &
Rhinehart, 2005; C. Brunsdon, Fotheringham, & Charlton, 1996; Fotheringham, Brunsdon, & Charlton, 2003; Lu, Harris, Charlton, & Brunsdon,
2015). As an exploratory tool, GWR is useful in wide varieties of research fields including but not limited to health and disease (Chalkias et al., 2013;
Chen et al., 2010; Chi, Grigsby–Toussaint, Bradford, & Choi, 2013; Dijkstra et al., 2013; Fraser, Clarke, Cade, & Edwards, 2012; Hipp & Chalise,
2015; Lin & Wen, 2011; Nakaya, Fotheringham,

The Importance Of Sea Temperature Anomalies
The oceans play an important role in the climate system owing to the interannual and longer timescale variability in sea surface temperature (SST).
Hasselmann (1976) proposed that this climate variability could be represented by a stochastic first order auto–regressive process
(AR1–process) and should be considered as the null hypothesis for extra–tropical sea surface temperature anomalies (SSTA). According to this concept,
SSTAs quickly responds to the atmospheric heat fluxes at short time period and the heat capacity of the ocean integrates the
SSTA variability on a longer time period. Frankignoul and Hasselmann (1977) have further suggested that the atmospheric forcing for SST anomalies
follows a spectrum of white noise with constant ... Show more content on Helpwriting.net ...
Thus the broad structure of the SSTA spectrum is determined by the depth of the ocean ML and atmospheric process.
Attempts to include additional processes, such as Ekman transport, entrainment of sub–mixed layer thermal anomalies (Dommenget and Latif 2002;
Deser et al. 2003; Lee et al. 2008), state–dependent noise (Sura et al. 2005) and the re–emergence associated with seasonal change in MLD (Schneider
and Cornuelle 2005) has shown to increase the SSTAvariance at annual and longer timescales. However, studies have demonstrated that the SST
variability at some part of the oceans cannot be represented by a simple AR1–process (Hall and Manabe
1997; Dommenget and Latif 2002, 2008). The inconsistency arises from the exchange of heat energy in the mixed layer and sub–mixed layer in the
ocean.
The strong seasonal cycle of the MLD can strengthen the persistence of SSTA from one winter to the following. The timescale in which the
subsurface temperature anomalies entrain to the surface (nearly 1 year) is expected to influence the spectral variance of SSTA. MГ¶ller et al. (2008)
have shown a peak in the annual time period in the power spectrum of midlatitude
SSTA that is associated with the re–emergence. Figure 5.3 illustrates the power spectrum of
SSTA and 90% significance level (shaded), presented in different ways (taken from Moller et al. (2008)). Figure 5.3a express the spectral variance
density, while figure 5.3b

Reportfinal Essay
Course ADVANCED ECONOMETRICS ProgrammeMSc in Finance Site HEC Lausanne Semester Fall 2014 Module LeaderDiane Pierret Teaching
AssistantDaria Kalyaeva Assessment Type: Empirical Assignment Assessment Title:A Dynamic Model for Switzerland GDP Written by:Group Y
(Ariane Kesrewani & Alan Lucero) Additional attachments: Zip Folder containing Matlab code, data and figures. Submission Date: December 15 at
00.05 1. Descriptive Statistics a. Time series plots of GDP level and GDP growth i. Definition of weak stationarity. GDP level and growth stationarity.
A stochastic... Show more content on Helpwriting.net ...
ii. Observations from plots. As mentioned before, we can observe from the plots that the GDP level is upward trending, which is a characteristic
feature of economic time series. To offset this, we calculate the first differences as a change in logs. Once plotting the vector of the results, another
characteristic of economic time series arises in the plot of GDP growth: seasonality. This can be seen in quarterly variations year on year, for
example quarter four of each year cannot be purely compared to quarter two since it accounts for a big holiday variation such as Christmas spending,
end of year boosting of financial results, etc. Thus growth should be assessed with the corresponding quarter year on year. This effect compensates the
business cycles variations which are more significant for

Empirical Results From The Modeling Of Claim Inflation
4.2 ARIMA MODEL
This chapter displays the empirical results from the modeling of claim inflation using ARIMA model.
Data Description
Series=claim inflation
Sample 1984–2014
Observations=30
Mean=2.748
Median=2.415
Minimum=1.25
Maximum=7.15
Standard deviation=1.43012
Kurtosis=1.679
Skewness=1.354
4.2.1 Descriptive Statistics for the claim inflation series
The data is not stationary since it does not exhibit a certain state of statistical equilibrium showing that the variance changes with time. Performing a log
transformation still produces a non–stationary process in which case we should difference the series before continuing.
ACF and PACF
4.2.2 Unit Root Test for CPI Series
Test for unity we use the ADF test for unit test hypothesis;
Ho: the CPI has unit root (non–stationary) Vs H1: CPI data has no unit root (stationary). Augmented Dickey fuller test
Data: log.claiminf
Dickey–fuller = –9.6336 lag order=12 p–value=0.01
Alternative hypothesis stationary warning message:

4.2.3 Model Identification, Estimation and Interpretation
ARIMA models are univariate models that consist of an autoregressive polynomial, an order of integration (d), and a moving average polynomial.
Since Claim inflation became stationary after first order difference (ADF test) the model that we are looking at is ARIMA (p, 1, q). We have to
identify the model, estimate suitable parameters, perform diagnostics for residuals and finally forecast the inflation series.

Is Walmart Safe?
Is Walmart Safe?
The Effects of Established Supercenter Walmarts to Property Crime Rates within Dekalb and Gwinnett County from 1999–2010
Class: Economics & Finance Modeling
Professor: Doctor Derek Tittle
Dream Team Group Members:
Alexandra E Steingaszner
Brian–Paul Gude
Kristopher Bryant
Norman Gyamfi
Samantha Gowdy
|
Disclaimer
This report has been created in the framework of a student group project and the Georgia Institute of Technology does not officially sanction its content.
Executive Summary
Every year, Walmart is accused of increasing crime in areas within which it builds Walmart Supercenters. Yet, research and data analyses largely
disprove these claims, as they reveal that other factors such as ... Show more content on Helpwriting.net ...
Iterations of analysis eliminated data points that were listed as "unusual observations," or any data point with a large standardized residual. After 5
iterations, the analysis showed improved residual plots. Randomness in the versus fits and versus order plots means that the linear regression model is
appropriate for the data; a straight line in the normal probability plot illustrates the linearity of the data, and a bell shaped curve in the histogram
illustrates the normality of the data.
Because of the method of monthly data collection, absolute randomness could not be obtained; however, it was decided that 5 iterations was sufficient
because the sixth iteration showed a decrease in the quality of the residual plots. The first test performed was the p–value test of the individual
variables. A p–value is the probability, ranging from 0 to 1, of obtaining a test statistic similar to the one that was actually observed. The only input that

did not have a p–value less than 0.05, which was the chosen significance level, was the "Number of Walmarts" variable; the number of Walmarts has
no specific effect on the output, property crime rate. The R2 of the analysis, or the coefficient of determination, provides a measure of how well
future outcomes are likely to be predicted by the model. R2 values range from 0 to 100% (or 0 and 1) and the

Relationship Between Vietnamese Stock Price Relative On...
METHODLOGY
The purpose of this paper is concentrated on relationship between Vietnamese stock price relative to exchange rate and United State stock market. In
order to have a better view about this relationships, the suitable econometrics model will be used in the research are OLS and ARMA. To determine
the correlation, coefficients among the variables from the test we will be able to find out the ОІ, R2, P–value, Standard Error, Durbin–Watson stat
statistic etc... With the time series dataset, in other to get a good forecast, the regressions will be run and tested on EVIEW program. The main model
will be use is:
VNSP= ОІ_0 + ОІ_1S&P500 + ОІ_2VNER + Оµ (e1)
By using OLS model we can determine how much the dependent variable is influenced by the independent variables. The null and alternative
hypothesizes will be as following:
VNSPViet Nam's monthly stock price index ОІ Beta
S&P500American monthly stock market index
VNERViet Nam's monthly exchange rate Оµ Error term
H_0: The Viet Nam's monthly stock price index is not influenced by American monthly stock market index and Viet Nam's monthly exchange rate.
H_1: The Viet Nam's monthly stock price index is influenced by American monthly stock market index and Viet Nam's monthly exchange rate.
MODELS
The program will be used to run regressions and analyze the outputs is EVIEW8. The Least Squares method of estimation is used for the analysis of
the data. The least squares method of estimation is preferred

Compare And Contrasting Fama's Articles
Comparing and contrasting Fama's articles (1971, 1990), this work will critically assess the development of EMH during the 1970 to 1991 period.
Firstly, it provides the reader to a short introduction to the EMH and to comparison the major changes between two articles. Thereafter, the main focus
will be concentrated on second article and its critical evaluation of results obtained. The main purpose of the capital market is to provide the investors
with accurate signals for resource allocation. It is possible when market security prices do "fully reflect" available information providing the opportunity
to make production–investment decisions. Such markets are also called the "efficient". A precondition for this is that information and trading costs
equal to 0. Moreover, the joint hypothesis problem is the main obstacle about market efficiency because it must be tested jointly with some asset
pricing models. In article of 1971, Fama categorised market efficiency into three main forms. Weak form is based on the historical data of the stock
prices. The semi–strong form tests how efficiently prices adapt to the publically available information. The third form is concerned whether any given
market participants having monopolistic access to the creation of the stock prices. Final Draft – Return Predictability: In short, the new work rejects
the old constant expected returns model that seems to perform well in the early work. It is rejected due to such findings as

The Correlation Between The Value Of Time Series Of...
Autocorrelation
Autocorrelation is defined as the correlation between the value of time series at a specific time and previous values of the same series (Reference). In
other words, with time series what happens in time t contains information about what will happen at time t+1. Autocorrelation plots are a
commonly–used tool for checking randomness in a data set. This randomness is ascertained by computing autocorrelations for data values at varying
time lags. If random, such autocorrelations should be near zero for any and all time–lag separations. If non–random, then one or more of the
autocorrelations will be significantly non–zero. The autocorrelation plots can provide answers to questions such as are the data random? Is an
observation related to an adjacent observation? Is the observed time series white noise, sinusoidal or autoregressive? They help in understanding the
underlying relationship between the data points. The autocorrelation plots of 4 time series of heating operating system are as follows :
a. Supply temperature setpoint :– The plot starts with a high correlation at lag 1 which is slightly less than 1 and slowly declines. It continues to
decrease until it becomes negative and starts showing an increasing negative correlation. The decreasing autocorrelation is generally linear with little
noise. Such a pattern in the autocorrelation plot is a signature of "strong autocorrelation", which in turn provides high predictability if modeled
properly. b. System

Event Study of Saic Stock Price
Newcastle University Business School
MA International Financial Analysis 2010/11
NBS8002
Techniques For Data Analysis–––––––––––––––––––––––––––––––––––––––––––––––––
SAIC Stock Prices and Its Participation in GM's IPO (Keywords: Event Study, Daily Stock Return, the OLS Market Model, SAIC, IPO)
Tutors Name: A.D Miller Student Name: Chen Kai (Jimmy) Student Number: b109000774 Date of submission: 10th /May/2011 Words Count: 5000
Table of Contents * Introduction * Overview of Market Efficiency and Event studies 1. Market Efficiency... Show more content on Helpwriting.net ...
The five events are correlated and occurring over approximate five months from 18th August 2010 to 13th December 2010.
Choice and Collection of Data
In order to study how stock prices react to these events, approximate three years of continuous daily stock price are chose, beginning at 17th March
2008 and ending more than three months after the final event at 22nd April 2011. In addition, SHANGHAI Stock Exchange Index (SSE) is adopted as
a proxy of the market portfolio.
The three–year SAIC stock price data and its corresponding SSE index are obtained from finance.yahoo.com, as it provides dividend–adjusted closing
prices. The two data are ordered in time in Excel (Sort Ascending). It is found that 46 SAIC daily stock prices are missing due to suspension of
trading, therefore; 46 corresponding SSE daily index are removed in order to match up dates on the two data series.
Estimation Period and Test Period
Given the event date and stock price data, the EP and TP can be constructed in order to estimate the normal returns and abnormal returns respectively.
The model parameters are estimated from the EP and therefore the AR can be calculated within the TP (Strong, 1992). Explicitly, the AR which

Summary: Forecasting Profitability and Earnings
Summary of Forecasting Profitability and Earnings
In the competitive environment, there is a strong prediction in economic theory that profitability is mean reversion both within and across industries.
For instance, under competition, firms will leave relatively profitless industries and turn into relatively high profitable industries. Some companies
introduce new products and technologies that bring more profitability for an entrepreneur. Otherwise, the expectation of failure which makes
companies with low profitability motivations to distribute capital to more productive uses.
Mean reversion represents that changes in earnings and profitability is predictable to a certain extent. However, predictable variation in profitability and
Lagged changes in the profitability are equal to Yt/At minus Yt21/At21. DFEt is equal to Yt/At minus E(Yt/At). Table one indicates that when there
are some lagged changes in profitability, CPt, is exclusive used to explain CPt11, and slope of CPt is forcefully negative; commonly, the changes in
profitability of the companies from t to t+1 back up 30 percent of lagged change. But for average reversion which leads slope of CPt close to 0.
Therefore, there is little as well as statistically dependable negative autocorrelation in the change in profitability. Our evaluation of average reversion
rate of the profitability is 38 percent per year.
In conclusion, differences in risk bring differences in the expected profitability of a firm. Furthermore, Yt/At is the noisy agent for the true economic
profitability. Finally, differences in the expected profitability of a firm can be the results of the monopoly rents. If we suppose all companies revert
toward an overall balance level of profitability, then:
[pic]
Section II is the model to use for nonlinear average reversion. We expand the model as:
[pic]
Table one shows that there is nonlinearity in autocorrelation of changes in expected profitability. It is similar to that studied by Brooks and
Buck–master (1976) as well as Elgers and Lo (1994) about the changes in

Analysis Of Predictability And Efficiency Of Pt Astra Agro...
Part A: Analysis of Predictability and Efficiency of PT Astra Agro Lestari Tbk and PT Kalbe Farma Tbk Market efficiency, predictability and its
importance for stock traders and/or other market participant There is a saying that no one can beat the market systematically when market is efficient
because no one can predict the return. Market is said to be efficient when all available information fully and quickly reflected in the security price.
Efficiency can be achieved when market is perfectly competitive where there is no transaction cost (or lower than expected profit), no transactional
delay and all traders behave rationally. Perfect competitive market made arbitrage trading (buy in one market and sell in another market) possible...
Investors cannot predict future value using past value (or past error) because price changes from one period to the next period, hence technical
analysis will be useless. Security's prices in semi–strong form fully reflected all publicly available information, including its all past value. Investors
cannot obtain abnormal return by using fundamental analysis. While strong form efficiency is achieved when security prices fully reflect public and
privately held information, including past value. As a consequence, information can be obtained by every participant and no one can achieve
systematic abnormal return. Market can be weak–form but not semi–strong or strong but strong form efficient market must be weak–form and
semi–strong. Investment strategy in efficient and not efficient market If market is efficient, investors should adopt passive investment strategy (buy
and hold) rather than active strategy because active strategy will underperform due to transaction cost. Investor will buy asset that they think the
intrinsic value is lower than market value, and vice versa. If the market is not efficient, investors will buy securities that replicate market index
portfolio, which is in the efficient frontier line and have low transaction cost. Technical way of expressing market efficiency E [(Rt+1– Rf) ЗЃ О©t ] =
0, whereRt = rate of return; Rf = return on risk–free assets; О©t = relevant information available at t. Market is efficient if

Marginal Cost and Correct Answer Essay
Question 1 The primary objective of a for–profit firm is to ___________. Selected Answer: Correct Answer: 5 out of 5 points maximize shareholder
value maximize shareholder value Question 2 5 out of 5 points The flat–screen plasma TVs are selling extremely well. The originators of this
technology are earning higher profits. What theory of profit best reflects the performance of the plasma screen makers? Selected Answer: Correct
Answer: innovation theory of profit innovation theory of profit Question 3 5 out of 5 points The Saturn Corporation (once a division of GM) was
permanently closed in 2009. What went wrong with Saturn? Selected Answer: Correct Answer: Saturn sold cars below the prices of Honda or... Show
more content on Helpwriting.net ...
Selected Answer: Correct Answer: autocorrelation autocorrelation Question 17 5 out of 5 points Consumer expenditure plans is an example of a
forecasting method. Which of the general categories best described this example? Selected Answer: Correct Answer: survey techniques and opinion
polling survey techniques and opinion polling Question 18 5 out of 5 points For studying demand relationships for a proposed new product that no
one has ever used before, what would be the best method to use? Selected Answer: Correct Answer: consumer surveys, where potential customers hear
about the product and are asked their opinions consumer surveys, where potential customers hear about the product and are asked their opinions
Question 19 If two alternative economic models are offered, other things equal, we would Selected Answer: 5 out of 5 points select the model that
gave the most accurate forecasts select the model that gave the most accurate forecasts Correct Answer: Question 20 5 out of 5 points The use of
quarterly data to develop the forecasting model Yt = a +bYtв€’1 is an example of which forecasting technique? Selected Answer: Correct Answer:
Time–series forecasting Time–series forecasting Question 21 If the

Regression Analysis of Dependent Variables
Table: 1, represents the results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy,
cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI,
Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted RВ
І and pronounced as R squared, indicates how well
data points fit a statistical model and the adjusted RВІ values in the analysis are fairly good which is more than 60%, indicates the considered model is
fit for analysis. Also, the F–Statistics which provides the statistical significance of the model and its probabilities which are below 5% level and hence
proves the model's significance.
Table: 1: Regression Results.
Method: Least Squares
Sample: 2005Q1 2013Q4
Included observations: 36
R–squaredAdjusted R–squaredF–statisticProb(F–statistic)
0.9553780.946146103.48450.00000
0.9631820.955564126.44260.00000
0.7467360.9088915.583180.01877
0.9521150.94220896.103770.00000
0.9608830.95279118.72720.00000
0.8684180.84119431.899090.00000
0.876410.8508434.274540.00000
0.9333360.91954367.669150.00000
0.8892150.86629438.794620.00000
0.9241630.90847358.899870.00000
0.7399030.6860913.749490.00000
Serial Correlation and Heteroskedasticity:
Normally the possibilities for the time series data to have the Serial correlation or auto correlation are more. It can be tested with the

A Study Of The Economic Forecasting Of New One Family...
A STUDY OF THE ECONOMIC FORECASTING OF NEW ONE FAMILY HOUSEHOLDS SOLD IN THE US– AN ANALYSIS Context and
Objective of the Analysis The US housing industry has witnessed a downward trend post 2005 due to deteriorating macroeconomic conditions in the
United States. The steep decline in the last 5 years has led to investigations on the future of the industry and understands the way forward for the
industry. The report answers the following questions: How long is the fall in the industry going to continue? When is the recovery expected in the
Housing Market? What is the future of the industry? The report is an attempt to understand the trends in the US New One Family Household market
(herein referred to as NHS) and forecast the NHS... Show more content on Helpwriting.net ...
Detailed study of the forecasts reveals that the housing industry is in a consolidation phase and the recovery of the industry is not expected in the next
one year (2011). Historical Trend of NHS and impact of external factors – A qualitative analysis The US National Housing market, specifically the One
Member Housing Market has seen a steep decline since the latter half of the last decade. The NHS data for the last 35 years (1975–2010) has been
shows in the figure below. From the data, three specific trend profiles of the NHS can be witnessed; the period from 1975 to 1991, where the NHS
showed a stable trend, the period from 1991 to 2005 where the NHS showed a steady acceleration and the period from 2005 onward showing a steep
decline in the NHS numbers. Figure 1. NHS data (1000s) 1975–2010 A high level visual analysis of the data reveals a significant seasonality and trend
factor. The next section we will attempt to understand the quantitative impact of the trend and seasonality factors. Relationship between Housing Data
and Mortgage Rate & Disposable Income The decline can be attributed to the decline in the macroeconomic conditions in the US. However, an
in–depth analysis of impact of specific economic indicators would be essential understand the way forward for the NHS. The data provided

Linear Accounting Valuation When Abnormal Earnings Are Ar...
Referee Report on: Jeffrey L. Callen and Mindy Morel (2001), Linear Accounting Valuation When Abnormal Earnings Are AR (2), "Review of
Quantitative Finance and Accounting", vol. 16 pp 191–203 Introduction In this study, Callen and Morel (Callen & Morel, 2001) compare the linear
information dynamics of Ohlson model (Ohlson, 1995) with AR (1) process, which is used in Ohlson's research, and AR (2) process for earnings,
book values and dividends. The purpose of this research is to evaluate the forecasting ability of the Ohlson model with AR (2) process. The authors
reference the methods in Myers' research (Myers, 1999). And, they find that there is no significant difference between the results of original model and
the new model, though the... Show more content on Helpwriting.net ...
The valuation equation with AR (1) process is following: V_t^1=y_t+(R_f П‰_0)/((R_f–П‰_1 )(R_f–1))+П‰_1/((R_f–П‰_1)) x_tâ The AR (2)
dynamic (Callen & Morel, 2001) can be expressed as: x_(t+1)â=П‰_0+П‰_1 x_tâ+П‰_2 x_(t–1)â+Оµ_(t+1) So, the valuation equation (Callen
& Morel, 2001) is: V_t^2=y_t+(R_f^2 П‰_0)/((R_f^2–П‰_1 R_f–П‰_2 )(R_f–1))+(П‰_2 R_f)/((R_f^2–П‰_1 R_f–П‰_2)) x_(t–1)â+(гЂ–R_f
П‰гЂ—_1+П‰_2)/((R_f^2–П‰_1 R_f–П‰_2)) x_tâ Besides, the sample is selected from 676 firms with at least 27 years, a total of 19,789
firm–years statistics. These data is selected by three standards, including long–term data (at least 27 years), positive book values and non–financial
firms. By panel data techniques, the writers find that the AR (2) dynamic is poorer in explaining V_t when comparing with AR (1) dynamic.
Meanwhile, the results indicate that both A (1) and AR (2) dynamics underestimate equity values, though the latter has a slight advantage. Major
Concerns The researchers select long–term statistics (up to 34 years) to test the dynamic model. It is more accurate by using long–term data since
some shocks in short run may impact the results. The writers not only provide the result that AR (2) dynamic does not have obvious improvement
when comparing with AR (1) dynamic, but also state their explanations, which offers various directions of following researches. Minor Concerns This
study might be stricter if the researcher added stationary test. Since most variables are

What Is MTM-SVD?
Where each row represents the measurements from different taper K at the same sensing node, and each column represents the measurements from
different sensing node at the same taper. Based on these measurements he applied SVD, and he got the power estimation from singular value, as it is
represented the power at this pin. In this paper cite{alghamdi2009performance} the author evaluated the performance of MTM–SVD for setting
specified number of sensing nodes with the chosen MTM parameters. The author cite{alghamdi2010local} continued the previous work, by exploring
the probability of detection, miss detection and false alarm, in order to evaluate the MTM–SVD performance. On the other hand,some papers worked
on reducing the time consuming... Show more content on Helpwriting.net ...
Therefore, the measurements taking from Multitaper will be arranged in 3dimension matrix, where the third dimension is the consecutive OFDM
blocks and the others are CR antennas and DPSS measurement. The measurements will be taken and will be applied to higher order tensor
decomposition , in order to take new singular value computation as the tensor core G(l,m,k) .Consequently the decision will be taken as the sum of
squared singular value ,then compare it by threshold . Although MTM–SVD provides reliable detection performance, in the worst environmental
conditions and specific SNR the system suffers from some degradation performance. 2.3.3subsubsection{ Weighting MTM:} The lower–order Eigen
spectrum of the MTM method has an excellent bias property. However,as the index k increases toward the time bandwidth product NW, the method
experiences some degradation in performance. Therefore Thomsoncite{thomson1982spectrum} introduces a set of weights ${dk(I)}$ which it effects
on down–weighting the higher order spectrum . Haykinfollows him in this paper cite{haykin2007multitaper}, where he proposed a simpler solution
for computing the adaptive weight. Accordingly, he derived an adaptive weight by minimizing the mean square error between an exact coefficient of
incremental random process and coefficient of $ k^{th} $ samples.} 2.3.4subsubsection{Compressive SVD–MTM Sensing :} As we

Absolute Best Model For Forecasting
The objective of this experiment was to find the best possible model for forecasting. I will use a series of tests both visual and statistical to find the
absolute best model for forecasting the data set. The forecast will be made for the conglomerate Wal–Mart. I start my test by taking the time series plot
graph of the data. This indicates whether the data has a seasonal or quarterly trend, and if there is a time trend. I also run a trend analysis on the data
set. I compare the graphs through trend analysis, and choose the graph with the smallest amount of error. I have elected to use the quadratic trend
model because the mean square deviation (MSD) is much lower in terms of error compared to the linear graph. My other objective, is to... Show more
content on Helpwriting.net ...
My first step is to remove the quarterly trend by taking a fourth difference. Taking a fourth difference will remove the quarterly trend that could be
affecting my ability to determine if there is a time trend. I used 16 numbers of lags because we are using quarterly information instead of seasonal.
As you can tell by the above graph there is no longer any seasonal data. However, there are 4 or more blue measure points above the red line which
indicates there is a time trend. The red line symbolizes Bartlett's test, which states that a consecutive string of 4 or more spikes above the red line
indicates a time trend. I will now take a first difference of the fourth difference of revenue. This is basically taking out both the quarterly and time
trend that could be potentially distorting the data. Now on the graph on the right, is the result of taking the first difference of the fourth difference of
revenue. It is now very clear that the time trend is no longer existing within the data set, and the quarterly trend has also been removed. It is somewhat
concerning that there are spikes at various points. More specifically at the first and fourth lag which I will consider when adding the partial
autocorrelation tool. The partial autocorrelation and autocorrelation functions, are used to determine if there is an autoregressive, moving average, or
mixed model as I mentioned earlier. The AR represents trend and quarterly values, while MA tends to represent the

Econometric Essay
Table of Contents Chapter 1: INTRODUCTION2 Chapter 2: THEORETICAL BASIS3 Chapter 3: DATA COLLECTION5 Chapter 4: EMPIRICAL
MODEL AND HYPOTHESIS TESTS7 Chapter 5: CONCLUSION14 Chapter 1: INTRODUCTION Since the introduction of doi moi (renovation)
economic reforms in 1986, Vietnam's economy has been among the fastest growing economies in the region. Its economic structure reflected an
increasing share of industry and services while the share of agriculture declined. Vietnam has been successful in poverty reduction strategies and has
been able to ensure rapid growth with... Show more content on Helpwriting.net ...
This Dummy variable includes: 0: mountain area and midland 1: coast 2: Delta Its expectation sign is positive (+) Therefore, the model proposed
is: FDI = [pic]INDUSTRIAL ZONE +[pic]SCHOOL + [pic]POLICY +[pic]DENSITY +[pic]REGION Chapter 3: DATA COLLECTION 3.1 Source of
survey: The data is collected from some websites of General Statistic Office as well as Industrial Zones in Vietnam 3.2 Scope of survey: My group
collected the data from 45 provinces in Vietnam randomly, after that we classified them into 5 categories: population density, the number of industrial
zones, school, policy, and region 3.3 Data table: [pic]The estimated model 1 is: FDI = –3023.01 + 757.328 INDUSTRIAL ZONE + 4.47475 SCHOOL
+ 2778.14 POLICY + 2.64933

Essay On Cd Metal
Interpolating Cd Metal in Soil Using Spatial Techniques in Metropolis Areas of Faisalabad Abstract Rapid industrialization and urbanization in recent
decades has resulted in large emissions of heavy metals especially in urban soils around the world. Soil contamination with heavy metals may pose
serious threat to environmental quality and human health due to their toxicity even at low concentration. Cadmium (Cd) is one of the toxic heavy
metals that has high mobility in soil–plant system and can accumulate in plant and human bodies. In this study, we determined the content of Cd in
urban and peri–urban soils of four towns (Lyallpur, Iqbal, Jinnah and Madina) of Faisalabad. The samples of surface soil (0–15 cm) were collected from
Due to massive increase in population and so residential colonies in Pakistan many industrial units once located outside of big cities has now
surrounded by living places. This is particularly true for Faisalabad Metropolitan, where many industrial units once outside city have been surrounded
by many residential colonies. Most of these industrial units release untreated wastewater and gaseous pollutants in soil–water and air compartments of
atmosphere. The waste water released from industrial units is being used by farmers for growing several vegetables and fodder crops. The continuous
use of such waste water for irrigation is introducing many heavy metals in soils. These heavy metals especially cadmium (Cd) from soils can easily
enter food through the consumption of food crops grown on metal contaminated soils. Owing to high mobility in soil–water–plant nexus, Cd is easily
entered in food chain and thus can pose serious threat to biological molecules and affects several body functions in human body. (Momodu and
Anyakora, 2010). Soil is a heterogeneous body that shows large variations in most of the properties (physical, chemical and biological). Although many
factors and processes of soil formation contribute to the variation in soil properties, time and space are the two most important

Temporal Variation Of Municipal Water Quality
Spatio–Temporal Variation in Municipal Water Quality in Abuja, Nigeria 1Abiola Kassim AbayomiВ№*, Olanrewaju LawalВ
І and Medugu Nasiru
Idris3 В№and 3 Department of Geography, Faculty of Social Sciences, Nasarawa State University, Keffi, Nigeria 2 Department of Geography and
Environmental Management, Faculty of Social Sciences, University of Port Harcourt, P.M.B 5323, Choba Campus, Port Harcourt.
*kassima2013@gmail.com Abstract A total number of Eighty eight water samples were collected at different designated point areas in the area
councils of FCT, Abuja. The qualities of the samples were analyzed for the physico–chemical properties of water supplied from difference sources in
the council areas. However fourteen parameters were determined in the water samples supplied to these areas, using appropriate physical and chemical
laboratory technics. The results of the physico–chemical analyses indicated variation in the amount elements (eg. pH, TDS, Colour, BOD. Anion and
cations) that are present in the water consumed and supplied. Significant positive correlation was observed between and among the parameter at
0.05significant level (Kruskal–Wallis Statistical Technique).Furthermore Moran's I was computed to examined global spatial autocorrelation. In
addition to this spatial autocorrelation analysis, local clustering of the values was also examined using Hot Spot Analysis (Getis–OrdGi*) revealed
point that are statistically significant hot or cold spot across the area sample. One

The Role Of Indian Fdi On Nepalese Economic Growth
3. Data and Methodology Present paper utilizes the annual data of GDP, Indian FDI, level of Investment and Export in real terms from the period
1989/90 to 2013/14. The concerned variables are transformed into logarithm and hereafter these are denoted by гЂ–LnGDPгЂ—_t,гЂ–LnFDIгЂ—_t
гЂ–LnIгЂ—_t and гЂ–LnXгЂ—_t . Fully Modified Ordinary Least Squares (FMOLS) is the main econometric methodology used in this paper to
examine the role and impact of Indian FDI on Nepalese economic growth. The FMOLS of economic growth of Nepal on Indian FDI augmented with
level of investment and export has been used to find the magnitude of long run relationship between the variables under study. GDP is taken as the
proxy for Nepalese economic growth. Some attention is necessary while employing FMOLS test. The variables under study must be cointegrated. So
before applying the FMOLS we examine the cointegration by method of Johansen's (1990) cointegration test. Prior to employing the Johansen's
Cointegration test we perform unit root test using ADF method. FMOLS method was designed by Phillips and Hansen (1990) to estimate the
cointegrating regressions. This method employs a semi–parametric correction to eliminate the problems created by long run correlation between
cointegrating equation and stochastic regressors innovations. This method is used to modify the least squares to account for serial correlation effects
and for the endogeneity in the regressions that result from the existence of cointegrating

Obesity And The United States
Compared to other countries, the United States was reported to have the second highest rate of obesity in the world after Mexico. Over the past
decade, cases of obesity have triplicated in the U.S., affecting more than one–third (34.9% or 78.6 million) of the adults (Ogden et al. 2014). Given the
current trends, it is projected that 42% of the U.S. population will be obese by 2030 (Finkelstein et al. 2012). Aside from its nefarious impact on the
overall quality of life of the affected individual on a micro level, obesity has an enormous economist cost on the US healthcare system. In their
extensive annual medical spending report, Finkelstein et al. (2012) indicated that the annual medical cost for obesity in the US amount to $147 billion...
According to the most recent data, two states have adult obesity rates above 35 percent, 20 states have rates at or above 30 percent, 43 states have
rates at or above 25 percent and every state is above 20 percent. (State of Obesity 2013). Studies (Arcaya et al. 2013; Burdette and Whitaker 2004) have
identified various factors that play a role in the state of this current conjuncture. Findings on the subject are not uniformed however. Papas et al. (2007)
have identified twenty studies in their systematic literature review that investigate the effect of environment's structure on the rate of obesity. While 17
of those studies show a significant relationship between those two variables, three of them found no relationship. At a county–level, only two studies
(Holzer, Canavan and Bradley 2014; Slack, Myers, Martin et al. 2014) have investigated the geographical variability in the rate of obesity. They
discovered that higher obesity rates were linked with counties with lower number of dentists per capita, higher percentages of African Americans,
higher rates of unemployment, lower rates of educational attainment and fewer adults who engaged in regular physical activity. The results of these
two studies provided up to date evidence on a national scale. In the end, the situation remains, the same: the dynamic between local level factors
associated with this public health

Computational Model of Neural Networks on Layer IV or...
Topic: Computational Modeling of Neural Networks on Layer IV of Primary Visual Cortex Confirms Retinal Origin of Orientation Map
Results section Orientation selectivity is one of the properties of neuron in primary visual cortex that a neuron response maximally when particular
orientation of stimulus is given. The orientation map is a map showing the orientation preferences of cortical neurons in primary visual cortex. This
research provides evidences for support of the theory posit that the orientation selectivity map is a product of a MoirГ© interference pattern that
originates in retinal ganglion cells. This paper shows that interactions between excitatory neurons and inhibitory neurons in neuron network modeled by
NEURON simulator having a MoirГ© interference pattern which results in an orientation selectivity map on the primary visual cortex.
The LGN neural network
The Feed Forward Input Network
The On and Off mosaics of magnocellular LGN cells were created. Examples of the mosaics are shown in the figure 5. The networks act as feed
forward input to the cortical neural network. Figure 5. The On and Off KGN mosaics. A) The ideal mosaic when there is no spatial noise.
B) The mosaics that created following the real physiological data constraints.
A shows more interference pattern than B.
Layer 4C of Primary Visual Cortex Cortical Network Model
There are two types of cortical neurons being considered in the model, excitatory neurons and inhibitory neurons.

Measuring A Computational Prediction Method For Fast And...
In general, the gap is broadening rapidly between the number of known protein sequences and the number of known protein structural classes. To
overcome this crisis, it is essential to develop a computational prediction method for fast and precisely determining the protein structural class. Based on
the predicted secondary structure information, the protein structural classes are predicted. To evaluate the performance of the proposed algorithm with
the existing algorithms, four datasets, namely 25PDB, 1189, D640 and FC699 are used. In this work, an Improved Support Vector Machine (ISVM) is
proposed to predict the protein structural classes. The comparison of results indicates that Improved Support Vector Machine (ISVM) predicts more
accurate protein structural class than the existing algorithms.
Keywords–Protein structural class, Support Vector Machine (SVM), NaГЇve Bayes, Improved Support Vector Machine (ISVM), 25PDB, 1189, D640
and FC699.
I.INTRODUCTION (HEADING 1)
Usually, the proteins are classified into one of the four structural classes such as, all–О±, all–ОІ, О±+ОІ, О±/ОІ. So far, several algorithms and efforts
have been made to deal with this problem. There are two steps involved in predicting protein structural classes. They are, i) Protein feature
representation and ii) Design of algorithm for classification. In earlier studies, the protein sequence features can be represented in different ways such
as, Functional Domain Composition (Chou And Cai, 2004), Amino Acids

The Importance Of Drinking Water In Bangladesh
Introduction
Safe drinking–water is essential for healthy life, and United Nations (UN) General Assembly declared safe and clean drinking–water as a human right
essential to the full enjoyment of life [1]. Moreover, the importance of water, sanitation and hygiene for health and development has been reflected in
the outcomes of a series of international policy forums [1]. These have also included health and water–oriented conferences, but most importantly in the
Millennium Development Goals (MDG) adopted by the General Assembly of the UN in 2000. The UN General Assembly declared the period from
2005 to 2015 as the International Decade for Action, "Water for Life" [1]. Access to safe drinking–water is important as a health and development
issue at national, regional and local levels. Bangladesh, a developing country from South Asian (SA) region also takes several steps for ensuring
sanitation and safe drinking water facilities among the people. As a result, Bangladesh has made great progress in this sector. The government also
claimed that it has achieved the MDG indicator of ensuring safe drinking water for 85% people of the country. According to different demographic
and health surveys, the percentage of using improved sources of drinking water is about 98% (reported in the latest two surveys Multiple Indicator
Cluster Surveys (MICS) 2012–13 and Bangladesh Demographic and Health Survey (BDHS) 2014) [2,3]. But, this achievement statistics are
overlooking the shortcomings.

Model Of Ols Model
whether the independent variable had a positive or negative relationship to the dependent variable. This was helpful when studying the graduated
colours map of number of votes to determine how the variables could help explain the patterns seen on the map. Once a variable was deemed suitable,
an OLS model was run to test the hypothesis that the number of votes is a function of the chosen variable. This process was repeated with different
groups of variables while assessing the outputs and altering the composition of variables. The checks included ensuring that the coefficients have the
expected sign and are statistically significant, that there is no redundancy in the explanatory variables, high adjusted R2 value, low AIC value and...
This model was chosen because it experienced the most significant increase in adjusted R2 (up from 90.5%) and a decrease in AIC (down from
773.9) from the OLS model. The coefficients that were computed by the GWR tool and mapped (Figure 2) helped to demonstrate that each
explanatory variable and its associated coefficient vary spatially in its predictive strength of the dependent variable. As we know, there is spatial
autocorrelation and relationships in the data. This is not necessarily negative but it is important to capture the structure of the correlation in the model
residues with explanatory variables. Until then, the model cannot necessarily be trusted (ESRI, 2016). However, the high level of significance of the
p–value (0.0000) and the z–value (6.059441) indicate that the model can be trusted. The small p–value indicates that the coefficients are not zero and
therefore the explanatory variables are statistically significant predictors of the behaviour of the dependent variable (ESRI, 2016). The small dataset is
more troubling. Some future solutions for eliminating spatial autocorrelation include continuing to resample variables until there is no more
statistically significant spatial autocorrelation (clustering). Unfortunately, that was not accomplished during the OLS regression without interfering
with the ability of the GWR to run. The output of the OLS

Statistical Analysis of Basketball Shooting in a...
When I watch basketball on television, it is a common occurrence to have an announcer state that some player has the hot–hand. This raises the
question: Are Bernoulli trials an adequate model for the outcomes of successive shots in basketball? This paper addresses this question in a controlled
(practice) setting. A large simulation study examines the power of the tests that have appeared in the literature as well as tests motivated by the work
of Larkey, Smith, and Kadane (LSK). Three test statistics for thenull hypothesis of Bernoulli trials have been considered in the literature; one of these,
the runs test, is effective at detecting one–step autocorrelation, but poor at detecting nonstationariy. A second test is... Show more content on
Helpwriting.net ...
Their third test is a test of fit and the researchers refer to it as a test of stationarity. The test is nonstandard, but simple to describe. Suppose that the data
are
1100100011110101 . . . .
Group the data into sets of four,
1100 1000 1111 0101 . . . , and count the number of successes in each set,
2, 1, 4, 2 . . . .
Use the 25 counts to test the null hypothesis that the data come from a binomial distribution with n = 4 and p estimated as the proportion of successes
obtained in the data. The first difficulty with implementing this test is that typically one or more of the expected counts is quite small. The researchers
overcame this problem by combining the O's and E's to yield three response categories: fewer than 2, 2, and more than
2, and then applied a П‡
2
test with one degree of freedom. The test can be made one–sided by rejecting if and only if the П‡
2
test would reject at 0.10 and E > O for the middle category (corresponding to two successes). The rationale for this decision rule is that E > O in the
central category indicates heavier tails, which implies more streakiness. The theoretical basis for this test is shaky, but the simulation study reported in
Section 3

The Housing Bubble And The Gdp : A Correlation Perspective
LITERATURE REVIEW A study from Ray M. Valadez, "The housing bubble and the GDP: a correlation perspective" in Journal of Case Research in
Business and Economics has been done to focus on the relationship between the Real Gross Domestic Product and the situation of Housing Bubble. In
this research, the author has concentrated on the time from the beginning of losing trust in government from the financial institution. He emphasizes
how much the housing bubble relates to the recession in the economy. The author takes the sample on changes in GDP and changes in the housing
price index from 2005 to 2006 in order to illustrate the statistical connection between them. The dependent variable were used is quarterly changes
of adjusted GDP, the database of the research were base on a report on NCSS software. According these results, the changes in both HPI and GDP
have likely similar common from in the period of 2005 and 2006, the data showed that there were significant changes in the next two years. The result
also showed that housing price and GDP has been long observed and their relationship has more innovations at the end of 2009. Another Research has
done by a group of composers including Zhuo Chen, Seong–Hoon Cho, Neelam Poudyal and Roland K. Roberts. The name of research was
"Forecasting Housing Prices under Different Submarket Assumptions." The paper focus on the submarket and use the data of home sale. The database
was taken from the Knoxville city combined with

The Effect Of Effect On Emerging Stock Markets Of Four...
Part 3 – Data and Methodology
3.1 Data Description The purpose of this study is to investigate the presence of January effect in emerging stock markets of four Southeast Asia
countries: Malaysia, Thailand, Philippine and Indonesia, for the period of January 2012 until December 2015, which is the most recent period after the
financial crisis in 2007–2008. The financial crisis would affect the behaviour of the stock markets and thus the stock price might not reflect its true
value. As the most recent economic crisis is believed to have ended in Fall 2011 (Elliott 2011; Weisenthal 2013), this study will focus on the most
recent 4–year period, from January 2012 until December 2015. The four Southeast Asia countries are selected because there are limited studies about
them. Furthermore, they are the only Southeast Asia countries being included in MSCI Emerging Markets Index as of 2016. Thus it is worth
examining the efficiency of the stock markets of these high growth emerging markets.
Daily equity market indices for four Southeast Asia countries will be collected from Yahoo Finance and DataStream. The daily price index is
collected instead of monthly price index because this study attempts to examine if the January effect is stronger on the first five days of January. The
indices are FTSE Bursa Malaysia KLCI Index (KLCI) for Malaysia, SET Index for Thailand, Philippine Stock Exchange Composite Index (PSEi) for
Philippine and IDX Composite Index for Indonesia. Since these

Creating a Model to Forecast the Adjusted Close Price of...
Aim of the Project
My intention is to create a model in order to forecast the adjusted close price of Paddy Power PLC shares. I will examine some of the different
Statistical Modelling techniques and evaluate the merits of each in turn.
I will use the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model if it is found that the variance of the time series is
non–constant. My final forecasting model will primarily use the Autoregressive Integrated Moving Average (ARIMA) model to predict future closing
prices of the share, with a GARCH model of the variance incorporated.
I will use the R Software to implement these methods. R is a large open source statistical software which is favoured by many professional statisticians
and academics.
Data Set
I have obtained the Adjusted Daily Close Prices of Paddy Power PLC as quoted on the Irish Stock Exchange for the past 3 years, from October
15th 2008 to October 13th 2011. I believe that a sample of this size is large enough to test for statistical trends, such as seasonality. I have plotted my
data set using the R software package. Figure 1 is what was generated. A sample of the data can be found in the References along with a link to an
internet page containing the data.
Figure 1 Statistical Modelling Methods
Multiple Linear Regression
Regression analysis involves finding a relationship between a response variable and a number of explanatory variables. For a sample number t, with p
explanatory

The Relationship Between Economic Growth And Its...
The relationship between economic growth and its determinants has been examined extensively. One important issue is whether population leads to
employment changes or employment leads to population changes (do 'jobs follow people' or 'people follow jobs'?) To explain this interdependence
between household residential choices and firm location choices, a simultaneous equations model was initially developed by Carlino and Mills (1987).
This modeling framework has also been applied in various studies to investigate the interdependence between migration and employment growth or
migration, employment growth, and income jointly determined by regional variables such as natural amenities (Clark and Murphy, 1996; Deller, 2001;
Waltert et al., 2011), public land policy (Duffy–Deno, 1997, 1998; Eichman et al., 2010; Lewis et al., 2002, Lewis et al., 2003; Lundgren, 2009), and
land development (Carruthers and Mulligan, 2007). In the Carlino–Mills (1987) model, the assumption is that households and firms are spatially
mobile. Also, it is assumed that households migrate to maximize their utility from the consumption of private goods and services and use of
non–market goods (amenities) and firms locate to maximize their profit whose production costs and revenues depend on business conditions, local
public services, markets, and supply of inputs. In addition, these assumptions indicate that interdependence between employment and household
income exists because household migrate if they

Hausman, Autocorrelation Test and Heteroscedasticity,...
Hausman test
Hausman test which usually accepted method of selecting between random and fixed effects which is running on regression equation. Hausman (1978)
provided a tectonic change in interpretation related to the specification of econometric models. The seminal insight that one could compare two models
which were both consistent under the null spawned a test which was both simple and powerful. The so–called 'Hausman test' has been applied and
extended theoretically in a variety of econometric domains. We focus on the construction of the Hausman test in a variety ofpanel data settings, and in
particular, the recent adaptation of the Hausman test to semi–parametric and nonparametric panel data models. A formal application of the Hausman
test is given focusing on testing between fixed and random effects within a panel data model. Mostly fixed effects are accepted way to run with panel
data as they always present consistent outcomes but may not be the most effective way to implement. On the other hand, random effects usually
provide to the researcher better P–values as it considered to be a more active estimator, so researcher can study random effects if it is reasonable to do
so. Moreover, Hausman test choose a more effective model compared to a less efficient as consistent model should presents robust estimates and
consistent results owing to the more efficient model.
Autocorrelation test
Another terms sometimes used for describe Autocorrelation these are "lagged

Econ
MULTIPLE CHOICE (CHAPTER 4) 1. Using a sample of 100 consumers, a double–log regression model was used to estimate demand for gasoline.
Standard errors of the coefficients appear in the parentheses below the coefficients. Ln Q = 2.45 –0.67 Ln P + . 45 Ln Y– .34 Ln Pcars (.20) (.10) (.25)
Where Q is gallons demanded, P is price per gallon, Y is disposable income, and Pcars is a price index for cars. Based on this information, which is
NOT correct? a. Gasoline is inelastic. b. Gasoline is a normal good. c. Cars and gasoline appear to be mild complements. d. The coefficient on the
price of cars (Pcars) is insignificant. e. All of the coefficients are insignificant. 2. In a... Show more content on Helpwriting.net ...
a, b, and c 12.The estimated slope coefficient (b) of the regression equation (Ln Y = a + b Ln X) measures the ____ change in Y for a one ____
change in X. a. percentage, unit b. percentage, percent c. unit, unit d. unit, percent e. none of the above 13.The standard deviation of the error terms in
an estimated regression equation is known as: a. coefficient of determination b. correlation coefficient c. Durbin–Watson statistic d. standard error of
the estimate e. none of the above 14.In testing whether each individual independent variables (Xs) in a multiple regression equation is statistically
significant in explaining the dependent variable (Y), one uses the: a. F–test b. Durbin–Watson test c. t–test d. z–test e. none of the above 15.One
commonly used test in checking for the presence of autocorrelation when working with time series data is the ____. a. F–test b. Durbin–Watson test c.
t–test d. z–test e. none of the above 16.The method which can give some information in estimating demand of a product that hasn't yet come to market
is: a. the consumer survey b. market experimentation c. a statistical demand analysis d. plotting the data e. the barometric method 17.Demand functions
in the multiplicative form are most common for all of the following reasons except: a. elasticities are constant over a range of data b. ease of
estimation of elasticities

Unit 3 Autocorrelation Test Paper
a.R2 value generated by empirical estimation regression model individual very high but many independent variables that are not significantly affect the
dependent variable.
b.Analyzing the correlation matrix of the independent variables. If the correlation between independent variable is fairly high (generally above 0.90),
then this is an indication multicollinearity. Multicollinearity can be appear due to the combined effect of two or more independent variables.
c.Multicollinearity can also be seen from (1) the value of tolerance and (2) variance inflation factor (VIF). Both these measurements indicate each
variable which independent explained by other independent variables. In a simple understanding of each independent variable becomes the dependent
variable (tied) and regressed against other independent variables. Tolerance measuring the variability of ... Show more content on Helpwriting.net ...
This shows the size of each independent variable which explained by other independent variables. Tolerance measures the variability of the variable
independently chosen that are not explained by the other independent variable. So a low tolerance value equal to the value of high VIF. Cutoff value
that is commonly used to indicate the presence multicollinearity is the value of tolerance 0.10 or equal to the value of VIF 10 (Ghozali, 2005).
3.5.2.3Autocorrelation Test
Autocorrelation test aims to determine whether there is a correlation between bully errors in period t to period t–1 (previously). If correlation occurs,
then there is a problem called autocorrelation. Autocorrelation appears because successive observations over time are related to each other.
This problem arises because the residual (error bullies) are not independent of one observations to other observations. It is often found in the time
series data (time series) because of "disturbances" on an individual / group tend affect the "disturbance" at the individual / group the same period next.
A good regression model is free of

Analysis Of The Bank Of Canada
With Canada's economy growing in every direction, we see a lot of new changes done by the Bank of Canada; which can have vast affects on the
economy and our standard of living. In this analysis I look at three variables: the Bank Rates, Consumer Price Index (CPI), and Foreign Exchange
Rates. Before I get into the actual data I'd like to give a brief description on how each variable affect each other. As we know interest rate and inflation
have a negative relationship, meaning as one increase the other decreases. The Bank of Canada tend to increase interest rates if they see that inflation is
starting to increase so they increase interest rates to reduce the inflation rate and vice versa. However for exchange rates and interest rates the ... Show
more content on Helpwriting.net ...
Empirical Analysis:
Considering the following regression model: BRi=ОІ0++ОІ1(Y)+ОІ2(Z)+ui which connects the bank rate (BR) of Canada to foreign exchange
rates(Y) and CPI(Z). In this model X1 and X2 are the corresponding independent variables exchange rates and CPI measured in decimals. There were
three estimation methods that were used to estimate the model: The Durbin Watson test is used to test the presence of autocorrelation. The residual
values from the regression analysis helps determine if there is a relationship between values that are lagged. The result of the Durbin Watson test lies
between 0 and 4 and depending on the value it will show the presence or absence of autocorrelation. The value that is closer to 0 indicates that there is
positive autocorrelation, 2 indicates that there is no autocorrelation and values approaching 4 indicate that there is negative autocorrelation. For the
hypothesis testing I've used the F–Statistic testing, in the later section of the paper I will explain my findings and the results.
The hypothesis test helps understand if the null hypothesis should be rejected or not. The purpose of the F test is to estimate if there is a larger
difference among the sum of square residuals. I used the F–test to run my testing according to the data we conclude by rejecting the null hypothesis for
both tests, due to F–Statistic>F–Critical. Therefore in this case as bank rates

Energy Detection Based Spectrum Sensing Method
In energy detector, the received signal is first filtered with a band pass filter in bandwidth to normalize the noise variance and to limit the noise power.
The output signal is then squared and integrated as follows: for each in–phase or quadrature component, a number of samples over a time interval are
squared and summed. The conventional energy detection method assumes that the primary user signal is either absent or present and the performance
degrades when the primary user is absent and then suddenly appears during the sensing time. An adaptive method to improve the performance of
Energy detection based spectrum sensing method is proposed .In this proposal, a side detector is used which continuously monitor the spectrum so as
to improve the probability of detection. The Primary user uses a QPSK signal with a 200 kHz band–pass bandwidth (BW). The sampling frequency is
8 times the signal BW. A 1024–point FFT is used to calculate the received signal energy. Simulation results showed that when primary users appear
during the sensing time, the conventional energy detector has lower probability of detection as compared to the proposed detector. The performance
of energy detector is characterized by Receiver Operating curves usually. AOC (Area under the Receiver Operating curves) is used to analyze the
performance of the energy detector method over Nakagami fading channels. Results showed that a higher value of fading parameter leads to larger
average AUC, and

Time Series Analysis
V.I.1.a Basic Definitions and Theorems about ARIMA models
First we define some important concepts. A stochastic process (c.q. probabilistic process) is defined by a T–dimensional distribution function.
Time Series Analysis – ARIMA models– Basic Definitions and Theorems about ARIMA models
marginal distribution function of a time series
(V.I.1–1)
Before analyzing the structure of a time series model one must make sure that the time series are stationary with respect to the variance and with respect
to the mean. First, we will assume statistical stationarity of all time series (later on, this restriction will be relaxed).
Statistical stationarity of a time series implies that the marginal probability distribution is time–independent ... Show more content on Helpwriting.net ...
A practical numerical estimation algorithm for the PACF is given by Durbin
(V.I.1–29)
with
(V.I.1–30)
The standard error of a partial autocorrelation coefficient for k > p (where p is the order of the autoregressive data generating process; see later) is given
by
(V.I.1–31)

Finally, we define the following polynomial lag–processes
(V.I.1–32)
where B is the backshift operator (c.q. BiYt = Yt–i) and where
(V.I.1–33)
These polynomial expressions are used to define linear filters. By definition a linear filter
(V.I.1–34)
generates a stochastic process
(V.I.1–35)
where at is a white noise variable.
(V.I.1–36)
for which the following is obvious
(V.I.1–37)
We call eq. (V.I.1
–36) the random–walk model: a model that describes time

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