Financial Econometrics Modeling and Forecasting Natural Gas Prices Abstract In this project we will model and forecast the natural gas prices over the short-term through the development of the Error Correction Model (ECM). This is presented as the best predictive model among various alternatives. To build this model‚ we gathered the oil prices to analyze the impact of the changes in these prices on the changes in natural gas prices. The results of the forecasting exercise‚ carried out
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Publisher: VJBooks Inc. Editor: Vijay Gupta Author: Vijay Gupta www.vgupta.com About the Author Vijay Gupta has taught statistics‚ econometrics‚ SPSS‚ LIMDEP‚ STATA‚ Excel‚ Word‚ Access‚ and SAS to graduate students at Georgetown University. A Georgetown University graduate with a Masters degree in economics‚ he has a vision of making the tools of econometrics and statistics easily accessible to professionals and graduate students. At the Georgetown Public Policy Institute he received rave reviews
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Analysis of Financial Time Series Third Edition RUEY S. TSAY The University of Chicago Booth School of Business Chicago‚ IL A JOHN WILEY & SONS‚ INC.‚ PUBLICATION Analysis of Financial Time Series WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A. SHEWHART and SAMUEL S. WILKS Editors: David J. Balding‚ Noel A. C. Cressie‚ Garrett M. Fitzmaurice‚ Iain M. Johnstone‚ Geert Molenberghs‚ David W. Scott‚ Adrian F. M. Smith‚ Ruey S. Tsay‚ Sanford Weisberg Editors Emeriti:
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Introduction to Stata Christopher F Baum Faculty Micro Resource Center Boston College August 2011 Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 1 / 157 Strengths of Stata What is Stata? Overview of the Stata environment Stata is a full-featured statistical programming language for Windows‚ Mac OS X‚ Unix and Linux. It can be considered a “stat package‚” like SAS‚ SPSS‚ RATS‚ or eViews. Stata is available in several versions: Stata/IC
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ECONOMETRICS EXERCISE 3 TRAN THI ANH NGUYET 28 March‚ 2013 1. The data set CEOSAL2.DTA contains information on 177 CEOs. In this sample‚ the average annual salary is $865‚864‚400 with the smallest and largest being $100‚000 and $5‚299‚000‚000‚ respectively. Another most interesting variable is sales with the average being $3‚5329‚463‚000‚ and its the smallest and largest being $29‚000 and $51‚300‚000. Using the data set‚ the following OLS regression is obtained: (1) . ˆ lnsalary = 4.58 + 0
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Models Introduction The Simple Regression Model The Multiple Linear Regression Models Violations of the Assumptions of CLRMs Definition • Econometrics is the application of statistical‚ and mathematical techniques to the analysis of economic data with a purpose of verifying or refuting economic theories. Theory Mathematical Model Econometric Model As income increases‚ consumption also increases‚ but not as much as income. yi = f ( xi ) = β0 + β1xi y i = f ( x i ) = β0 + β1x i +
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account data from set countries in year 2007‚ the year that indicates the entrance period of 2008 world economic crisis. The objective is to evaluate the impact of these independent variables on the total private domestic consumption through the econometrics tools for these set countries‚ which have been randomly chosen in function of the world geography repartition. In addition‚ we want to describe the economic relationship between those variables. For example according to Keynesian model‚ aggregate
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THE IMPACT OF MACROECONOMIC FACTORS ON NONPERFORMING LOANS IN THE KENYAN BANKING INDUSTRY. MAKUSA GEORGE MAWILI HD 335-40-0284/2012 JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLGY Email;mawiligeorge@yahoo.com Phone No. +254 0728 165 416 Abstract This study aimed to investigate the effect of macroeconomic factors on the performance of nonperforming loans in the Kenyan commercial Banking industry. The research methodology adopted was a simple time series analysis design that assisted
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Data The variables of interest are oil imports to Germany‚ and temperature in Germany. The latter is used as a leading indicator for the former‚ to improve on the forecast obtained by the univariate model. Both variables are collected over a time range from January 1985 until and including December 1997‚ whereas the last year is not used for constructing the optimal forecast‚ obtained by fitting a model through the data until the end of 1996. This will enable us to forecast the year 1997 using
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Coursework This handout provides information about the module’s second coursework. Below‚ you will find the coursework as well as information about the marking scheme. * The coursework requires you to engage with regression analysis by performing various regressions in Eviews and by commenting on the main results. * The aim of the coursework is to test your ability to handle datasets with the use of a specialist software and to provide critical and informative comments on the outcome of
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