This document summarizes four projections of total U.S. gasoline consumption from 2015-2017 completed by Team Alpha. A causal time-series analysis using monthly income and price data from 1976-2015 predicted consumption of 3.2 billion barrels in 2015, 3.31 billion in 2016, and 3.34 billion in 2017. A trend time-series analysis using yearly data from 1945-2014 predicted 3.72 billion barrels in 2015, 3.76 billion in 2016, and 3.81 billion in 2017. A traditional stochastic time-series analysis using Holt's exponential smoothing predicted 3.25 billion barrels in 2015, 3.29 billion in 2016, and 3.33 billion in 2017. Finally, a multivariate cross-sectional analysis
A report issued by the Natural Gas Supply Association that projects a marked increase in demand for natural gas during summer 2016, but "downward pressure" on natgas prices nonetheless--because of incredible production from shale.
This document discusses the role of energy in mitigating and adapting to climate change. It provides context on drivers of greenhouse gas emissions from energy and discusses options for decarbonizing energy supply and reducing final energy demand. Key points made include that reaching long-term climate goals will require a 3-4x increase in low-carbon energy by 2050 and that demand reductions alone will not be sufficient. The document also notes the challenges of decarbonizing different sectors like transportation. Adaptation strategies for the energy system are also briefly touched upon.
This document analyzes the relationship between US GDP performance and government current expenditure from 1999 to 2009. Regression analysis shows a strong positive linear relationship between the two variables, with current expenditure explaining 85.4% of the variation in GDP over the period. Both GDP and current expenditure showed an increasing trend over time. The regression coefficients, F-test, and correlation coefficient provide strong statistical evidence that increases in government current expenditure are positively associated with increases in US GDP during this period.
Annual Energy Production Estimates Validation of Wind Farms (March 2012)Carlos Pinto
MEGAJOULE validated wind farm annual energy production estimates by comparing pre-construction estimates to actual production data from 12 wind farms in Portugal totaling 45 farm-years. Results showed estimates were on average 98.9% of actual production after adjusting for windiness and availability. The percentage of farm-years below the P90 estimate was 7.3%, suggesting estimates were somewhat conservative. Further validation with additional data may help understand causes of over-prediction and uncertainty assessment.
Two lists published by the U.S. Energy Information Administration showing the top 100 oil fields in the U.S. and the top 100 gas fields in the U.S., both based on the size of their estimated reserves in the year 2013 (as estimated by EIA researchers). The #1 gas field in 2013 was the Marcellus, and the #1 oil field the Eagle Ford.
The Natural Gas Supply Association’s (NGSA) 2015 Winter Outlook for Natural Gas. This 89-page report, researched by Energy Ventures Analysis, Inc., concludes that the price of natural gas for the winter ahead will be pretty much the same as last winter's prices.
A series of slides from the NGSA that shows, in essence, they believe the short-term summer price for natural gas in the U.S. to rise over last summer because of depleted stores of natural gas. It will take extra capacity to bump up the "gas in storage" and because of the increased demand, the NGSA says the price will tick up.
ECN 410 Final Project Paper, James Wiltbank, Nathan WatersNathan Waters
This study examines the relationship between economic conditions and time spent doing religious activities using data from the American Time Use Survey between 2003-2013. The researchers hypothesize that higher unemployment rates and lower GDP growth will correlate with more time spent on religious activities. Their regression model finds unemployment has a statistically significant positive relationship with religious time, but GDP growth is not significant. Removing outliers improves the model, finding GDP growth also has a positive correlation, contradicting the initial hypothesis.
A report issued by the Natural Gas Supply Association that projects a marked increase in demand for natural gas during summer 2016, but "downward pressure" on natgas prices nonetheless--because of incredible production from shale.
This document discusses the role of energy in mitigating and adapting to climate change. It provides context on drivers of greenhouse gas emissions from energy and discusses options for decarbonizing energy supply and reducing final energy demand. Key points made include that reaching long-term climate goals will require a 3-4x increase in low-carbon energy by 2050 and that demand reductions alone will not be sufficient. The document also notes the challenges of decarbonizing different sectors like transportation. Adaptation strategies for the energy system are also briefly touched upon.
This document analyzes the relationship between US GDP performance and government current expenditure from 1999 to 2009. Regression analysis shows a strong positive linear relationship between the two variables, with current expenditure explaining 85.4% of the variation in GDP over the period. Both GDP and current expenditure showed an increasing trend over time. The regression coefficients, F-test, and correlation coefficient provide strong statistical evidence that increases in government current expenditure are positively associated with increases in US GDP during this period.
Annual Energy Production Estimates Validation of Wind Farms (March 2012)Carlos Pinto
MEGAJOULE validated wind farm annual energy production estimates by comparing pre-construction estimates to actual production data from 12 wind farms in Portugal totaling 45 farm-years. Results showed estimates were on average 98.9% of actual production after adjusting for windiness and availability. The percentage of farm-years below the P90 estimate was 7.3%, suggesting estimates were somewhat conservative. Further validation with additional data may help understand causes of over-prediction and uncertainty assessment.
Two lists published by the U.S. Energy Information Administration showing the top 100 oil fields in the U.S. and the top 100 gas fields in the U.S., both based on the size of their estimated reserves in the year 2013 (as estimated by EIA researchers). The #1 gas field in 2013 was the Marcellus, and the #1 oil field the Eagle Ford.
The Natural Gas Supply Association’s (NGSA) 2015 Winter Outlook for Natural Gas. This 89-page report, researched by Energy Ventures Analysis, Inc., concludes that the price of natural gas for the winter ahead will be pretty much the same as last winter's prices.
A series of slides from the NGSA that shows, in essence, they believe the short-term summer price for natural gas in the U.S. to rise over last summer because of depleted stores of natural gas. It will take extra capacity to bump up the "gas in storage" and because of the increased demand, the NGSA says the price will tick up.
ECN 410 Final Project Paper, James Wiltbank, Nathan WatersNathan Waters
This study examines the relationship between economic conditions and time spent doing religious activities using data from the American Time Use Survey between 2003-2013. The researchers hypothesize that higher unemployment rates and lower GDP growth will correlate with more time spent on religious activities. Their regression model finds unemployment has a statistically significant positive relationship with religious time, but GDP growth is not significant. Removing outliers improves the model, finding GDP growth also has a positive correlation, contradicting the initial hypothesis.
Consumer Price Index for All Urban Consumers – Gasoline vs. Ai.docxbobbywlane695641
Consumer Price Index for All Urban Consumers – Gasoline vs. Air Fare
(August 2017)
For Immediate Release
Summary
✓ The Consumer Price Index for All Urban Consumers – Gasoline was 200.96 during August, using 1982
– 1984 = 100 as the seasonally adjusted index.
• The August 2017 reading of 200.96 was ~10.4% higher than a year ago when the Consumer
Price Index for All Urban Consumers – Gasoline was 182.09.
✓ The Consumer Price Index for All Urban Consumers – Air Fare was 270.65 during August, using 1982 –
1984 = 100 as the seasonally adjusted index.
• The August 2017 reading of 270.65 was -3.2% lower than a year ago when the Consumer Price
Index for All Urban Consumers – Air Fare was 279.71
CPI Measure August 2017 August 2016 YoY % ∆ July 2017
Consumer Price Index for All Urban
Consumers – Gasoline
200.96 182.09 +10.4% 189.06
Consumer Price Index for All Urban
Consumers – Air Fare
270.65 279.71 -3.2% 273.32
1st Paragraph of Press Release:
The Buraeu of Labor Statistics (BLS) recently released its Consumer Price Index data for August of 2017. The
Consumer Price Index for All Urban Consumers: Gasoline is a measure that tracks the average cost of gasoline
(all types). The index measures price changes (as a percentage change) from a predetermined reference date. During
August of 2017, the Consumer Price Index for All Urban Consumers: Gasoline was 200.96 when using 1982 –
1984 = 100 as the seasonally adjusted index. Also included in the monthly report was the Consumer Price Index for
All Urban Consumers: Air Fare which registered at 270.65 during the month, again using 1982 – 1984 = 100 as the
seasonally adjusted index. Eligible for pricing are all regularly scheduled domestic and international commercial airline
trips on certified carriers departing from each of the 87 cities in the CPI sample. For the selected cities that do not have
a qualifying airport, the nearest city with a qualifying airport is designated as the city of departure.
2nd Paragraph of Press Release:
The Consumer Price Index for All Urban Consumers: Gasoline increased on a year-over-year basis by 10.4%
during August, the ninth monthly year-over-year increase in the last 10 months. The Consumer Price Index for All
Urban Consumers: Air Fare decreased on a year-over-year basis by 3.2% during August, which was the fifth monthly
year-over-year decline seen in the last six months.
3rd Paragraph of Press Release:
The Consumer Price Index for All Urban Consumers: Gasoline is an important economic measure in that is a
critical gauge of overall inflation in the economy. Given the percentage of consumer spending that is related to gasoline,
understanding how prices move is of critical importance to the overall tracking of inflation. When prices are increasing
a steady, but low-single digit pace, it is a signal of economic health as wages are likely rising at a similar rate. The
Consumer Price Index for.
Applied Economics Letters, 2010, 17, 325–328
Are demand elasticities affected by
politically determined tax levels?
Simultaneous estimates of gasoline
demand and price
Lennart Flood*, Nizamul Islam and Thomas Sterner
Department of Economics, School of Economics and Commercial Law,
Göteborg University, Göteborg, Sweden
Raising the price of fossil fuels is a key component of any effective policy
to deal with climate change. Just how effective such policies are is decided
by the price elasticities of demand. Many papers have studied this without
recognising that not only is there a demand side response: quantities are
decided by the price but also there is a reverse causality: the level of
consumption affects the political acceptability of the taxes which are the
main component of the final price. Thus prices affect consumption and
consumption levels, in turn, have an affect on taxes and thus consumer
prices. This article estimates these functions simultaneously to show that
there is indeed an effect on the demand elasticity.
I. Introduction
Global carbon emissions from fossil fuels are around
7 Gtons Carbon per year whereof transport fuels in
the OECD account for over 1 Gton. Effective policy
instruments to deal with climate change will have
their main effect through higher fuel prices. To reach
any of the scenarios discussed in for instance
the Stern or IPPC reports, very large reductions
(50–90%) – and thus large price increases will be
needed. The exact extent of the necessary rise in prices
to reach any particular target hinges on the long-run
demand elasticities for fuel. Such elasticities are also
of interest for transport economists and market
forecasts.
As a result there are few areas that are so well
studied particularly after the oil price hikes of the
1970s. The total number of individual studies
is several hundred and even the number of surveys
is quite large, (Drollas, 1984; Oum, 1989; Dahl and
Sterner, 1991a, b; Goodwin, 1992; Hanly et al.,
2002; Graham and Gleister, 2002, 2004). While a
range of estimates is found, the consensus is that
the long-run price elasticity of demand is around
�0.8, while the long-run income elasticity is about
one. Typically the short-run (one year) elasticities
are about a third of the long-run values.
Differences between countries are typically moder-
ate but there are differences depending on the type
of model and data used. Estimates that only build
on time series data for one country tend to give
somewhat lower elasticities than studies that include
cross-section evidence.
In a recent article which surveys new developments
in the field, Basso and Oum (2006), identify a
number of important methodological issues not
*Corresponding author. E-mail: [email protected]
Applied Economics Letters ISSN 1350–4851 print/ISSN 1466–4291 online � 2010 Taylor & Francis 325
http://www.informaworld.com
DOI: 10.1080/13504850701735864
mailto:[email protected]
http://www.informaworld.com
su.
Time Series Least Square Forecasting Analysis and Evaluation for Natural Gas ...rahulmonikasharma
Power and mechanical energies are now a down main stream of energy to produce any other type of product in any nation in the world. Currently natural resources are the main stream of resources to produce the power energy or mechanical energy. We aware that the natural resource energy is limited.It’s the prime focus now a day to optimize the natural resource consumption. Natural Gas is an integral part of the natural resource and hence needs a planning and forecasting to optimize the natural gas usage and wastage. Without the proper planning and forecasting, the natural gas consumption will be highly usage and wasted and hence the limited resource will be drained off. In this paper, we analyze the time series least square method and compare its results with the actual data with absolute error percentage. This paper provides a clear analytic results and comparisons which serves as a base forecasting model for natural gas consumption.
The slide deck used by Cabot CEO Dan Dinges during his quarterly phone call with analysts. Of particular interest is slide #9 which shows Cabot believes 2018 is an "inflection year" for the company, with six important infrastructure projects due to go online.
US oil and gas reserves and production study 2018Nihad Azizli
The US oil and gas reserves and production study analyzed 50 major companies and found:
1) Capital expenditures in 2017 totaled $114.5 billion, a 32% increase from 2016, with development and exploration spending increasing the most.
2) Revenues in 2017 were $135.9 billion, up 32% from 2016, due to improved commodity prices. Net income was $17.2 billion after losses in prior years.
3) US oil production increased 5% in 2017 while gas production declined 7% due to asset sales. Reserves saw net upward revisions for the first time in the study period.
This study analyzes how the exchange rate elasticity of exports has changed over time and across countries to determine if currency wars are worth fighting. The analysis uses panel data from 7 countries from 1990-2014 and finds that the elasticity of total exports has declined over this period. Specifically, the elasticity fell from an average of 0.63 in 1990-2003 to 0.4 in 2004-2014. Additional analysis shows this decline preceded the global financial crisis, suggesting cyclical factors are not the sole driver. In conclusion, the effectiveness of currency depreciation in boosting exports appears to have decreased over time.
Outlook for Energy and Minerals Markets - for the 116th CongressRoger Atkins
TESTIMONY OF KEVIN BOOK MANAGING DIRECTOR, CLEARVIEW ENERGY PARTNERS, LLC
BEFORE THE
U.S. SENATE COMMITTEE
ON ENERGY AND NATURAL RESOURCES
FEBRUARY 5, 2019
This document provides a summary of a natural gas conservation potential study conducted for Ontario. Key findings include:
- Significant cost-effective natural gas savings potential exists, with technical potential reducing consumption 35.1-46.1% by 2020-2030, and economic potential reducing consumption 24.5-26.5%
- Under an unconstrained budget, achievable potential could reduce consumption 7.1% by 2020 and 17.8% by 2030
- With a gradually increasing budget, semi-constrained achievable potential could reduce consumption 5.1% by 2020 and 12.4% by 2030
- Constrained to current budgets, achievable potential could reduce consumption 4.5% by 2020 and 9
Business Decision Making Part TwoQNT275.docxRAHUL126667
Business Decision Making Part Two
QNT275
Descriptive statistics are used in the presentation of data sets in form of meaningful summaries. This helps for the important patterns of the data to be observable. Descriptive statistics may not be useful in drawing final conclusions about the data. These statistics are majorly used in describing quantitative data as they involve numerical calculations. The main descriptive statistics are the measures of central tendency and the measures of variability. Measures of central tendency express the central position of a data. Measures of variability, on the other hand, represent spread of the values in a data set.
For the case of American Airline Group, the research involves both quantitative and qualitative data. The operational costs, which represent the dependent variable, can be understood by studying the operational changes that result from the merger. This includes quantitative data on the number of passengers that have access to the airline’s services. The descriptive statistics which could be used in summarized this data include the mean, mode, and median. The mean represents the average number of passengers using the airline’s services for a given period of time, for example in one day. The mode represents the most recurrent number in the data set. For example, if the data were to be collected for a period of one month, the particular number of passengers that would be recorded in many days would represent the mode of the data. Median, on the other hand, represents the centrally placed value after the data has been arranged in either an ascending order or descending order. Descriptive statistics could also be used for summarizing data on the financial capability of the merger. This data is obtained from a survey audit of the airline’s financial data. The measures of variability that could be used in this research include the range, variance, standard deviation, quartiles and absolute. These measures describe the consistency of data by presenting the variability (Holcomb, 2017).
Inferential statistics involve making generalizations about the population using facts from the sample. These statistics are useful where the population under study is large. In this case, it is most feasible to select a small group to be a representative of the population. The inferential statistics that can be used in the analysis of the data from the American Airline Group merger include the estimation of parameters and tests of hypothesis. Estimation of parameters involves approximating population parameters using the calculated sample statistics. For example, the population mean may be estimated by the sample mean. It would be economical to study only a small group. Estimation of parameters is thus highly useful for making inferences about the whole population (Bernstein, & Bernstein, 2011).
Test of hypothesis involves testing the accuracy of a claim about the population based on the sample under study. Othe ...
This article compares five measures of core inflation: 1) the CPI excluding food and energy, 2) the trimmed mean CPI, 3) the median CPI, 4) the CPI excluding energy, and 5) the CPI excluding the eight most volatile components. Core inflation measures aim to exclude volatile price changes to identify the underlying inflation trend. The article evaluates the measures based on how well they track trend inflation, predict future overall inflation, and their complexity. Of the measures considered, the CPI excluding energy and the trimmed mean CPI appear to be the most accurate indicators of trend inflation and best at predicting future inflation while also being relatively simple measures.
This article compares five measures of core inflation: 1) the CPI excluding food and energy, 2) the trimmed mean CPI, 3) the median CPI, 4) the CPI excluding energy, and 5) the CPI excluding the eight most volatile components. Core inflation measures aim to exclude volatile price changes to identify the underlying inflation trend. The article evaluates the measures based on how well they track trend inflation, predict future overall inflation, and their complexity. Of the measures considered, the CPI excluding energy and the trimmed mean CPI appear to be the most accurate indicators of trend inflation and best at predicting future inflation while also being relatively simple measures.
Honours Thesis 2015 - An Analysis of Fuel Prices and Fuel Taxation in South A...Justin de la Hunt
This document analyzes fuel prices and taxation in South Africa. It finds that the general fuel levy is a more appropriate taxation method than VAT due to its progressive nature and the control it gives policymakers. The document also develops models to predict fuel prices based on lagged oil prices and exchange rates, in order to help policymakers estimate future tax revenue from the fuel levy. This analysis concludes that the general fuel levy is the preferred policy tool for South Africa.
Blackman Et Al Presentation Incidence Fuel Taxesa95osksj
This document summarizes a study on the incidence and regressivity of fuel taxes in Central America. The study used household expenditure survey data from Costa Rica to analyze average household expenditures on different fuels by income decile. It then conducted simulations of the impact of a 10% fuel tax on expenditures, assuming short-run and long-run price elasticities of demand. The analysis found the tax increased expenditures the most for higher-income deciles. Attempts to replicate the analysis in Nicaragua and Panama were unsuccessful due to inconsistencies in the survey data. The document discusses plans to expand the analysis to other Central American countries.
Blackman Et Al Presentation Incidence Fuel Taxesa95osksj
This document summarizes a study on the incidence and regressivity of fuel taxes in Central America. It outlines the policy context of addressing air pollution and greenhouse gases through fuel taxes. It describes the study's goals of using household survey data from Costa Rica to calculate average fuel expenditures and the price elasticity of demand. The results found that a 10% fuel tax would increase expenditures the most for higher-income households in the short run. Issues with other country data are noted.
Measuring the Dependence of Fuel Efficiency of Automobiles on Different FactorsIRJESJOURNAL
Abstract: The purpose of this paper is to develop a multiple regression model for measuring the miles per gallon (Efficiency) by using different independent variables. How much the efficiency in terms of miles per gallon is dependent on engine displacement, horsepower, vehicle weight, model year, number of cylinders, and filters? Which is the most important variable for increasing the efficiency of automobiles? The answers of these questions are the objectives of this paper. The results indicate that model year has positive effect on efficiency of vehicle while horse power, vehicle weight, and filters have negative impact on vehicle’s efficiency. Results also specify that vehicle weight has the maximum impact on miles per gallon while horsepower has the minimum impact.
This document outlines a program plan for firefighting in a community. The goals of the program are to establish concise and affordable fire rescue services and ensure all stakeholders understand safety measures and emergency policies. Objectives include educating the community on fire safety, preparing firefighters to handle emergencies, and equipping firefighters with necessary gear. A logic model is proposed to guide the firefighting group's procedures during incidents to improve speed and effectiveness of the emergency response.
Consumer Price Index for All Urban Consumers – Gasoline vs. Ai.docxbobbywlane695641
Consumer Price Index for All Urban Consumers – Gasoline vs. Air Fare
(August 2017)
For Immediate Release
Summary
✓ The Consumer Price Index for All Urban Consumers – Gasoline was 200.96 during August, using 1982
– 1984 = 100 as the seasonally adjusted index.
• The August 2017 reading of 200.96 was ~10.4% higher than a year ago when the Consumer
Price Index for All Urban Consumers – Gasoline was 182.09.
✓ The Consumer Price Index for All Urban Consumers – Air Fare was 270.65 during August, using 1982 –
1984 = 100 as the seasonally adjusted index.
• The August 2017 reading of 270.65 was -3.2% lower than a year ago when the Consumer Price
Index for All Urban Consumers – Air Fare was 279.71
CPI Measure August 2017 August 2016 YoY % ∆ July 2017
Consumer Price Index for All Urban
Consumers – Gasoline
200.96 182.09 +10.4% 189.06
Consumer Price Index for All Urban
Consumers – Air Fare
270.65 279.71 -3.2% 273.32
1st Paragraph of Press Release:
The Buraeu of Labor Statistics (BLS) recently released its Consumer Price Index data for August of 2017. The
Consumer Price Index for All Urban Consumers: Gasoline is a measure that tracks the average cost of gasoline
(all types). The index measures price changes (as a percentage change) from a predetermined reference date. During
August of 2017, the Consumer Price Index for All Urban Consumers: Gasoline was 200.96 when using 1982 –
1984 = 100 as the seasonally adjusted index. Also included in the monthly report was the Consumer Price Index for
All Urban Consumers: Air Fare which registered at 270.65 during the month, again using 1982 – 1984 = 100 as the
seasonally adjusted index. Eligible for pricing are all regularly scheduled domestic and international commercial airline
trips on certified carriers departing from each of the 87 cities in the CPI sample. For the selected cities that do not have
a qualifying airport, the nearest city with a qualifying airport is designated as the city of departure.
2nd Paragraph of Press Release:
The Consumer Price Index for All Urban Consumers: Gasoline increased on a year-over-year basis by 10.4%
during August, the ninth monthly year-over-year increase in the last 10 months. The Consumer Price Index for All
Urban Consumers: Air Fare decreased on a year-over-year basis by 3.2% during August, which was the fifth monthly
year-over-year decline seen in the last six months.
3rd Paragraph of Press Release:
The Consumer Price Index for All Urban Consumers: Gasoline is an important economic measure in that is a
critical gauge of overall inflation in the economy. Given the percentage of consumer spending that is related to gasoline,
understanding how prices move is of critical importance to the overall tracking of inflation. When prices are increasing
a steady, but low-single digit pace, it is a signal of economic health as wages are likely rising at a similar rate. The
Consumer Price Index for.
Applied Economics Letters, 2010, 17, 325–328
Are demand elasticities affected by
politically determined tax levels?
Simultaneous estimates of gasoline
demand and price
Lennart Flood*, Nizamul Islam and Thomas Sterner
Department of Economics, School of Economics and Commercial Law,
Göteborg University, Göteborg, Sweden
Raising the price of fossil fuels is a key component of any effective policy
to deal with climate change. Just how effective such policies are is decided
by the price elasticities of demand. Many papers have studied this without
recognising that not only is there a demand side response: quantities are
decided by the price but also there is a reverse causality: the level of
consumption affects the political acceptability of the taxes which are the
main component of the final price. Thus prices affect consumption and
consumption levels, in turn, have an affect on taxes and thus consumer
prices. This article estimates these functions simultaneously to show that
there is indeed an effect on the demand elasticity.
I. Introduction
Global carbon emissions from fossil fuels are around
7 Gtons Carbon per year whereof transport fuels in
the OECD account for over 1 Gton. Effective policy
instruments to deal with climate change will have
their main effect through higher fuel prices. To reach
any of the scenarios discussed in for instance
the Stern or IPPC reports, very large reductions
(50–90%) – and thus large price increases will be
needed. The exact extent of the necessary rise in prices
to reach any particular target hinges on the long-run
demand elasticities for fuel. Such elasticities are also
of interest for transport economists and market
forecasts.
As a result there are few areas that are so well
studied particularly after the oil price hikes of the
1970s. The total number of individual studies
is several hundred and even the number of surveys
is quite large, (Drollas, 1984; Oum, 1989; Dahl and
Sterner, 1991a, b; Goodwin, 1992; Hanly et al.,
2002; Graham and Gleister, 2002, 2004). While a
range of estimates is found, the consensus is that
the long-run price elasticity of demand is around
�0.8, while the long-run income elasticity is about
one. Typically the short-run (one year) elasticities
are about a third of the long-run values.
Differences between countries are typically moder-
ate but there are differences depending on the type
of model and data used. Estimates that only build
on time series data for one country tend to give
somewhat lower elasticities than studies that include
cross-section evidence.
In a recent article which surveys new developments
in the field, Basso and Oum (2006), identify a
number of important methodological issues not
*Corresponding author. E-mail: [email protected]
Applied Economics Letters ISSN 1350–4851 print/ISSN 1466–4291 online � 2010 Taylor & Francis 325
http://www.informaworld.com
DOI: 10.1080/13504850701735864
mailto:[email protected]
http://www.informaworld.com
su.
Time Series Least Square Forecasting Analysis and Evaluation for Natural Gas ...rahulmonikasharma
Power and mechanical energies are now a down main stream of energy to produce any other type of product in any nation in the world. Currently natural resources are the main stream of resources to produce the power energy or mechanical energy. We aware that the natural resource energy is limited.It’s the prime focus now a day to optimize the natural resource consumption. Natural Gas is an integral part of the natural resource and hence needs a planning and forecasting to optimize the natural gas usage and wastage. Without the proper planning and forecasting, the natural gas consumption will be highly usage and wasted and hence the limited resource will be drained off. In this paper, we analyze the time series least square method and compare its results with the actual data with absolute error percentage. This paper provides a clear analytic results and comparisons which serves as a base forecasting model for natural gas consumption.
The slide deck used by Cabot CEO Dan Dinges during his quarterly phone call with analysts. Of particular interest is slide #9 which shows Cabot believes 2018 is an "inflection year" for the company, with six important infrastructure projects due to go online.
US oil and gas reserves and production study 2018Nihad Azizli
The US oil and gas reserves and production study analyzed 50 major companies and found:
1) Capital expenditures in 2017 totaled $114.5 billion, a 32% increase from 2016, with development and exploration spending increasing the most.
2) Revenues in 2017 were $135.9 billion, up 32% from 2016, due to improved commodity prices. Net income was $17.2 billion after losses in prior years.
3) US oil production increased 5% in 2017 while gas production declined 7% due to asset sales. Reserves saw net upward revisions for the first time in the study period.
This study analyzes how the exchange rate elasticity of exports has changed over time and across countries to determine if currency wars are worth fighting. The analysis uses panel data from 7 countries from 1990-2014 and finds that the elasticity of total exports has declined over this period. Specifically, the elasticity fell from an average of 0.63 in 1990-2003 to 0.4 in 2004-2014. Additional analysis shows this decline preceded the global financial crisis, suggesting cyclical factors are not the sole driver. In conclusion, the effectiveness of currency depreciation in boosting exports appears to have decreased over time.
Outlook for Energy and Minerals Markets - for the 116th CongressRoger Atkins
TESTIMONY OF KEVIN BOOK MANAGING DIRECTOR, CLEARVIEW ENERGY PARTNERS, LLC
BEFORE THE
U.S. SENATE COMMITTEE
ON ENERGY AND NATURAL RESOURCES
FEBRUARY 5, 2019
This document provides a summary of a natural gas conservation potential study conducted for Ontario. Key findings include:
- Significant cost-effective natural gas savings potential exists, with technical potential reducing consumption 35.1-46.1% by 2020-2030, and economic potential reducing consumption 24.5-26.5%
- Under an unconstrained budget, achievable potential could reduce consumption 7.1% by 2020 and 17.8% by 2030
- With a gradually increasing budget, semi-constrained achievable potential could reduce consumption 5.1% by 2020 and 12.4% by 2030
- Constrained to current budgets, achievable potential could reduce consumption 4.5% by 2020 and 9
Business Decision Making Part TwoQNT275.docxRAHUL126667
Business Decision Making Part Two
QNT275
Descriptive statistics are used in the presentation of data sets in form of meaningful summaries. This helps for the important patterns of the data to be observable. Descriptive statistics may not be useful in drawing final conclusions about the data. These statistics are majorly used in describing quantitative data as they involve numerical calculations. The main descriptive statistics are the measures of central tendency and the measures of variability. Measures of central tendency express the central position of a data. Measures of variability, on the other hand, represent spread of the values in a data set.
For the case of American Airline Group, the research involves both quantitative and qualitative data. The operational costs, which represent the dependent variable, can be understood by studying the operational changes that result from the merger. This includes quantitative data on the number of passengers that have access to the airline’s services. The descriptive statistics which could be used in summarized this data include the mean, mode, and median. The mean represents the average number of passengers using the airline’s services for a given period of time, for example in one day. The mode represents the most recurrent number in the data set. For example, if the data were to be collected for a period of one month, the particular number of passengers that would be recorded in many days would represent the mode of the data. Median, on the other hand, represents the centrally placed value after the data has been arranged in either an ascending order or descending order. Descriptive statistics could also be used for summarizing data on the financial capability of the merger. This data is obtained from a survey audit of the airline’s financial data. The measures of variability that could be used in this research include the range, variance, standard deviation, quartiles and absolute. These measures describe the consistency of data by presenting the variability (Holcomb, 2017).
Inferential statistics involve making generalizations about the population using facts from the sample. These statistics are useful where the population under study is large. In this case, it is most feasible to select a small group to be a representative of the population. The inferential statistics that can be used in the analysis of the data from the American Airline Group merger include the estimation of parameters and tests of hypothesis. Estimation of parameters involves approximating population parameters using the calculated sample statistics. For example, the population mean may be estimated by the sample mean. It would be economical to study only a small group. Estimation of parameters is thus highly useful for making inferences about the whole population (Bernstein, & Bernstein, 2011).
Test of hypothesis involves testing the accuracy of a claim about the population based on the sample under study. Othe ...
This article compares five measures of core inflation: 1) the CPI excluding food and energy, 2) the trimmed mean CPI, 3) the median CPI, 4) the CPI excluding energy, and 5) the CPI excluding the eight most volatile components. Core inflation measures aim to exclude volatile price changes to identify the underlying inflation trend. The article evaluates the measures based on how well they track trend inflation, predict future overall inflation, and their complexity. Of the measures considered, the CPI excluding energy and the trimmed mean CPI appear to be the most accurate indicators of trend inflation and best at predicting future inflation while also being relatively simple measures.
This article compares five measures of core inflation: 1) the CPI excluding food and energy, 2) the trimmed mean CPI, 3) the median CPI, 4) the CPI excluding energy, and 5) the CPI excluding the eight most volatile components. Core inflation measures aim to exclude volatile price changes to identify the underlying inflation trend. The article evaluates the measures based on how well they track trend inflation, predict future overall inflation, and their complexity. Of the measures considered, the CPI excluding energy and the trimmed mean CPI appear to be the most accurate indicators of trend inflation and best at predicting future inflation while also being relatively simple measures.
Honours Thesis 2015 - An Analysis of Fuel Prices and Fuel Taxation in South A...Justin de la Hunt
This document analyzes fuel prices and taxation in South Africa. It finds that the general fuel levy is a more appropriate taxation method than VAT due to its progressive nature and the control it gives policymakers. The document also develops models to predict fuel prices based on lagged oil prices and exchange rates, in order to help policymakers estimate future tax revenue from the fuel levy. This analysis concludes that the general fuel levy is the preferred policy tool for South Africa.
Blackman Et Al Presentation Incidence Fuel Taxesa95osksj
This document summarizes a study on the incidence and regressivity of fuel taxes in Central America. The study used household expenditure survey data from Costa Rica to analyze average household expenditures on different fuels by income decile. It then conducted simulations of the impact of a 10% fuel tax on expenditures, assuming short-run and long-run price elasticities of demand. The analysis found the tax increased expenditures the most for higher-income deciles. Attempts to replicate the analysis in Nicaragua and Panama were unsuccessful due to inconsistencies in the survey data. The document discusses plans to expand the analysis to other Central American countries.
Blackman Et Al Presentation Incidence Fuel Taxesa95osksj
This document summarizes a study on the incidence and regressivity of fuel taxes in Central America. It outlines the policy context of addressing air pollution and greenhouse gases through fuel taxes. It describes the study's goals of using household survey data from Costa Rica to calculate average fuel expenditures and the price elasticity of demand. The results found that a 10% fuel tax would increase expenditures the most for higher-income households in the short run. Issues with other country data are noted.
Measuring the Dependence of Fuel Efficiency of Automobiles on Different FactorsIRJESJOURNAL
Abstract: The purpose of this paper is to develop a multiple regression model for measuring the miles per gallon (Efficiency) by using different independent variables. How much the efficiency in terms of miles per gallon is dependent on engine displacement, horsepower, vehicle weight, model year, number of cylinders, and filters? Which is the most important variable for increasing the efficiency of automobiles? The answers of these questions are the objectives of this paper. The results indicate that model year has positive effect on efficiency of vehicle while horse power, vehicle weight, and filters have negative impact on vehicle’s efficiency. Results also specify that vehicle weight has the maximum impact on miles per gallon while horsepower has the minimum impact.
This document outlines a program plan for firefighting in a community. The goals of the program are to establish concise and affordable fire rescue services and ensure all stakeholders understand safety measures and emergency policies. Objectives include educating the community on fire safety, preparing firefighters to handle emergencies, and equipping firefighters with necessary gear. A logic model is proposed to guide the firefighting group's procedures during incidents to improve speed and effectiveness of the emergency response.
1. Team Project:
Total Gasoline Consumption in the United States forecast for the
years 2015 - 2017
Team Alpha:
Patrick Carter
Wendel Friedl
Robert Niess
Brent Welcher
This forecast culminates the effort of Team Alpha, specifically Patrick Carter, Wendel Friedl,
Robert Niess, and Brent Welcher, to project gasoline through 2017. This written report follows
up the Oral presentation of Monday, June 22nd
by the same aforementioned group members.
1. Introduction
To forecast the total U.S. consumption of gasoline, measured in thousands of barrels (42 gallons
per barrel), for the years 2015 through 2017, our team completed four separate projections each
using a different forecasting technique. The resulting forecast for each technique, as well as the
structure, details, and analysis of individual results, are detailed in the following sections of this
paper. This paper concludes with a summary of overall results. All supplementary tables and
graphs are attached in an appendix. An initial literature review was conducted to generate a list
of possible variables and model specifications. Details as to the specific academic papers
reviewed as well as website addresses for all data sources are listed in SITREP 3.
2. Causal Time-Series Analysis
The pool of independent variables considered for this section included: Population (measured by
civilian non-institutional or Total U.S population including armed forces overseas) from FRED,
Disposable Real Income per capita (in 2009 dollars) from the BEA, Miles Driven (Total miles
and miles per capita were both separately considered) from the DOT, Real price per gallon of
Gasoline (in 2009 dollars) from the BLS, Unemployment Rate (FRED), Employment to
Population Ratio (FRED), and the 1-year Treasury Bill Yield (FRED).
2. Preliminary regressions using yearly data from 1945 – 2014 exhibited severe positive
autocorrelation (Durbin-Watson statistics in the range of .10 to .27) and multicollinearity (VIF
scores in the high teens to high twenties) with all combinations of independent variables tested.
Adopting a double-log model specification as well as the use of the 1st
differences of all yearly
data were both attempted to reduce autocorrelation and multicollinearity, with very limited
success. While positive autocorrelation and multicollinearity were both reduced, they largely
remained well within the “severe” range and the regression yielded an extremely low R2
.
A second round of regressions using monthly data from January 1976- March 2015 largely
eliminated the problems of autocorrelation and multicollinearity, as evidenced by the Durbin-
Watson and VIF statistics, after dropping several of the above-mentioned independent variables.
The two most consistently statistically significant independent variables were disposable real
income and gasoline price. Attempting to add either measure of population considered (total or
non-institutional) as a third independent variable to a regression with income and price caused
multicollinearity between population and price (figure 2-A), verified with a correlation matrix
(figure 2-B). Unlike the yearly data regressions, a linear model specification produced slightly
better overall results in terms of adjusted R2
when using monthly data. Residual plots showed no
likely heteroscedasticity for either independent variable.
After comparing regression results, of the seven independent variables originally considered only
income and price were included in the final model. Some measure of market size (population,
number of drivers, miles driven, etc.) seems to be most important element left out of this model,
and I believe that finding an appropriate, non-multicollinear market measure would improve the
accuracy of the model. The final model used for forecasts in this section was:
Predicted Consumption = 118,894.17 – Gas Price*3,147.09 + Disposable Income*4.54
To test the accuracy of the forecast model, a 13-month holdout period “backcast” regression was
conducted (figure 2-C). Results showed that for the 13-month period examined, this model
overestimated gasoline consumption by an average of 9.04%.
The Holt-Winters Exponential Smoothing Model was used to forecast 33 months of future values
for gasoline prices and 32 months of future values for real disposable income per capita. Future
values (or actual values for 2015, where available) were plugged into the model equation to
3. arrive at the forecasted consumption of gasoline for each year from 2015 through 2017. The
monthly results are shown in figure 2-D, and the abbreviated and rounded yearly consumption
projections were 3.51 billion barrels in 2015, 3.64 billion barrels in 2016, and 3.67 billion barrels
in 2017. Adjusting these figures based upon the 9% average projection over-estimation found
when performing the 13-month “backcast,” the yearly figures are 3.2 billion barrels in 2015,
3.31 billion barrels in 2016, and 3.34 billion barrels in 2017 (error adjustments non-
compounded).
3. Trend Time-Series Analysis
The dependent variable used for the trend time-series analysis was annual U.S. product supplied
of finished motor gasoline (in thousands of barrels) from 1945-2014 (EIA). Time and one
dummy variable to account for the consumption shift, caused presumably by implementation of
CAFÉ legislation, serve as the independent variables.
A line graph of gasoline consumption from 1945-2014 shows a nearly linear upward trend, with
a significant economic shock around the year of 1984. Without the use of a dummy variable, the
regression analysis rendered a .95 adjusted R2
with a highly significant t-statistic of 37.8. Adding
a dummy variable to account for the year that it looked like the intercept changed showed that
the dummy variable was statistically significant. For the year of 1982, a dummy variable was
created that gave a slightly better R2
than did the regression run without a dummy variable.
Continuing to change the dummy variable for different years to see if the adjusted R2
would
improve showed slight improvement until the year of 1985, which gave an adjusted R2
of 0.96, f-
statistic of 903.98, and t-statistic of -4.34. Using the dummy variable for this year helped us
realize that the sweet spot was back in 1984 with an adjusted R2
of 0.96, f-statistic of 906.48, and
t-statistic of -4.36 (figure 3-A). The correlation matrix shows a slightly stronger correlation
between the time and dummy independent variables than between the dummy and the dependent
variable, indicating that multicollinearity may be an issue with the model (figure 3-B). Fixing
possible multicollinearity within the trend time-series model is, however, beyond the scope of
this paper.
The final model used to generate forecasts in this section was:
4. Predicted Consumption = -88,539,501 + (45,951*Date) - (337,542*Dummy)
Using this model, projected consumption for 2015 was 3.72 billion, 3.76 billion barrels for 2016,
and 3.81 billion barrels for 2017. The trend time-series regression model forecasts the U.S.
product supplied of finished motor gasoline (in thousands of barrels) to continue to follow an
upward linear trend.
4. Traditional Stochastic Time-Series Analysis
For the traditional stochastic time-series forecast, several models were tested looking for the
model that would best fit the data while keeping to the lowest MAPE. The time-series models
tested were the Simple Moving Average (SMA) model, the Simple Exponential Smoothing
(SES) model, the Holt’s (double) Exponential Smoothing (HES) model, and the (Holt) Winter’s
Exponential Smoothing (WES) model. All models in this section used monthly U.S. product
supplied of finished motor gasoline (in thousands of barrels) from 1945-2015 (EIA) to forecast
33 months of future consumption (through December 2017).
The SMA forecast yielded a MAPE of 5.57%. The SMA model was deemed inappropriate for
this project, as it is best suited for very short-term projections while we were forecasting 33
periods of data. The SES produced a forecast with a MAPE of 5.33%. This model fit the data
better than did the SMA model but, as SES is unsuitable for projecting data with a trend,
additional models were tested. The HES model produced a MAPE of 5.31%. The WES model
produced by far the lowest MAPE of the four models considered, at 2.06%. However, since the
WES model is an extended test of the HES model that accounts for seasonality, the added
mathematical complexity was judged unnecessary given the similarity in the monthly forecast
numbers between the WES and HES models. Holt’s model was used to generate consumption
forecasts in this section (figure 4-A).
The HES model bore projections of 3.25 billion barrels of gasoline for 2015, 3.29 billion barrels
for 2016, and 3.33 billion barrels for 2017.
5. 5. Multivariate Cross-Sectional Analysis
Independent variables considered for this section were: Motor gasoline price in dollars per
million BTU by state for 2013 (EIA), disposable real income per capita by state for 2013 (BEA),
total population by state for 2013 (Census), unemployment rate by state for 2013 (BLS), and
Average miles driven per capita by state for 2013 (DOT).
An initial regression using all listed independent variables produced an R2
of 97%, indicating
severe multicollinearity was a possibility. The VIF for the initial regression was a huge 74.56,
providing further evidence of multicollinearity. A correlation matrix showed possible
multicollinearity between the total population per state and average miles driven, so those two
independent variables were dropped from subsequent regressions. After dropping the
multicollinear variables, a regression produced an R2
of only 20%. Looking to increase the R2
,
the double-log model specification was adopted. The double-log specification made the model
much stronger, with a 33% R2
. The VIF for this model fell to 1.5, indicating that
multicollinearity was no longer a problem. A correlation matrix confirmed that multicollinearity
among these variables was unlikely (figure 5-A). Residual plots for variables showed no distinct
patterns, so heteroscedasticity did not appear to be an issue. As this section uses a cross
sectional model, the unlikelihood of autocorrelation made generating a Durbin-Watson statistic
unnecessary.
The final model used for forecasts in this section was:
ln Predicted Consumption = 31.8 + (-7.7 * ln Gas Price) + (-.33 * ln Disposable Income) +
(1.4 * ln Unemployment rate)
The Holt Exponential Smoothing Model was used to forecast 3 years of future annual values for
gasoline prices, real disposable income per capita, and unemployment. Plugging these projected
figures into the model equation resulted in forecasts of 3.49 billion barrels of gasoline
consumption in 2015, 3.56 billion barrels in 2016, and 3.6 billion barrels in 2017.
6. 6. Summary
Yearly gasoline consumption totals were relatively consistent among the four models (figure 6-A
and 6-B).The Trend Time-Series model consistently produced the highest forecast figures, while
the Traditional Time-Series using Holt’s model consistently produced the lowest. The overall
spread of the projections for each year was 467 million barrels in 2015 (14.4%), 474 million
barrels in 2016 (14.42%), and 479 million barrels in 2017 (14.39%).