GG 712 Regional Energy Modelling
Spring Semester, 2004
Lectures T-Th 2:00 Ð 3:30 SAR 104 Offie hours M, F 1:00-2:00 or T,TR, 3:30 Ð 4:30
Instructor: R.K. Kaufmann 675 Commonwealth Avenue, Room 141 H 353-3940
Course Description
This class seeks to teach students how to use econometric techniques to analyze real world data. To do so, I will cover a variety of econometric techniques that are designed to analyze time series data, cross sectional data, and categorical data. Discussion of each topic will proceed in two steps. First, I will cover the basic principles of the technique, with emphasis on the situations for which the technique is appropriate and how to interpret statistical results. Next, we will discuss the application of the technique to data set drawn from the climate change literature, the energy literature, and/or the remote sensing literature. Many of the examples are papers I have publishedÑI chose these because I have the data sets and we will use the data set in the homework assignments.
Course Requirements
Throughout the semester, I will give assign homeworks that require students to apply the techniques taught in class to data sets. Often these home works will use the data sets described in the papers. To do so, I will teach students how to use RATS. At the end of the semester, there will be one take-home exam. This exam will require students to answer questions by analyzing a data set. Students also will be expected to estimate a model on some a topic that is relevant to their field of interest. This model will be described in a paper that is due at the end of the semester. A brief description of the model's subject is due before spring break.
Academic Honesty
You are allowed to cooperate on homework assignmentsÑI ask that you include the name of all those who helped. You may be allowed to work together on your class project. You must obtain permission from me before proceeding. There can be no cooperation or discussion for the take-home exam. Plagiarism, cheating on exams, submitting the same work for more than one course, deliberately impeding the academic performance of others and other forms of academic misconduct are serious offenses. I take them very seriously and I expect my students to do likewise. You should read the CAS Academic Conduct Code for further information about specific definitions, procedures, sanctions, etc. Copies of the Code are available in CAS 105. I am required to refer cases of suspected academic misconduct to the CAS Dean's Office. I assign a "0" or "F" to any assignment that was judged by the Dean (after a hearing in front of a faculty/student Academic Conduct panel) to be a violation of the Academic Conduct Code.
I) Basic Regression Model
Theory
Stock & Watson Chpt 4-5
Applications
Introduction to RATS
Wigley et al., (1998)
2) Stochastic Trends, Spurious Regressions & Cointegration
Theory
Stock & Watson 12.6,14.3-14.4
Hylleberg et al., (1990)
Application
Kaufmann et al. (in preparation)
3) Long Run vs. Short Run
Theory
Partial adjustment models
Polynomial Lags
Error correction models Stock & Watson (552-554)
Example
Kaufmann (1994)
Kaufmann et al., (in review)
4) Asymmetric Relations
Theory
Gately, (1992)
Granger & Lee (1989)
Example
Gately, (1992)
Kaufmann and Laskowski
5) VARS & Granger Causality
Theory
Stock and Watson 14.1, 12.4
Enders page 294-305.
Example
Kaufmann & Stern (1997)
6) Cross Sectional Data
Theory
Stock & Watson Chapter 8
Pedroni (1999)
Granger and Huang (1997)
Example
Seto and Kaufmann (2003)
7) Stability of regression coefficients/break points
Theory
Cuthbertson et al., (1992)
Stock & Watson 12.7
Hansen, (1997)
Canova and Hansen (1995)
Example
Cleveland and Kaufmann (1997)
8) Evaluating models
Theory
Diebold and Mariano (1995)
Newbold & Harvey (2002)
Example
Richmond & Kaufmann (review)
9) Categorical Data
Theory
Stock & Watson Chapter 9
Example
Stanley (2001)
Forbes and Zampelli (2000)
10) Estimating cointegrating relations
Theory
Stock & Watson Chapter 14.4
Example
Kaufmann and Cleveland (2000)
11) Instrumental variables: Simultaneity & measurement errors
Theory
Stock & Watson Chapter 10
Hausman (2001)
Example
Kaufmann et al., (in review)
Kaufmann and Stern (in review)Stock, J.H. and M.W. Watson, 2003. Introduction to EconometricsAAddison Wesley, Boston,
Cleveland, C.J. and R.K. Kaufmann. 1997. Natural gas in the US: How far can technology tretch the resource base? The Energy Journal 18:89-108.
Canova, F and B.E. Hansen, 1995. Are seasonal aptterns constant over time? A test for seasonal stability. Jounral of business and Economic Statistics 13(3) 237-252.
Diebold, F.X. and R.S. Mariano. 1995. Comparing predictive accuracy. Journal of Business & Economic Statistics 13:253-263.
Enders, Walter. 1995. Applied Econometric Time Series John Wiley & Sons. NY.
Forbes, K.E. and E.M. Zampelli, 2000 Technology and the exploratory success rate in the US offshore The Energy Journal 21:109-120.
Gately, D. 1992. Imperfect price-reversibility of U.S. gasoline demand: Asymmetric responses to price increases and declines. The Energy Journal 13(4): 179-207.
Granger, C.W.J., and T.H. Lee, Investigation of production, sales and inventory relationships using multicointegration and nonsymmetric error correction models Journal of Applied Econometrics 4:S145-S159, 1989.
Hansen, B, 1997, Approximate Asymptotic P values for structural Ðchange tests, Journal of Business & Economic Statistics, 15:60-67.
Hausman, J. 2001. Mismeasured variables in econometric analysis: problems from the right and problems from the left, Journal of Economic Perspectives 54(4), 57-67.
Hylleberg, S, R.F. Engle, L.W.J. Granger, and B.S. Yo, Seasonal integration and cointegration. Journal of Econometrics 44:215-238, 1990.
Kaufmann, R.K. 1994. The relation between marginal produce and price in US energy markets: implications for climate change policy 1994. Energy Economics 16(2): 145-158.
Kaufmann, R.K. and C.J. Cleveland. 2001. Oil production in the lower 48 states; Economic, geological, and institutional determinants. The Energy Journal. 22:27-49.
Kaufmann, R.K, S. Dees, P. Karadeloglou, and M. Sanchez, Does Opec matter? an econometric analysis of oil prices The Energy Journal.
Kaufmann, R.K. and D. I Stern. 1997. Evidence for human influence on climate from statistical analysis of hemispheric temperature observation and GCM outputs. Nature 388:39-44.
Kaufmann, R.K, H. Kauppi , J.H. Stock,. In review. Emissions, concentrations, & temperature: a time series analysis Climatic Change.
Kaufmann, R.K. and C. Laskowski, Causes for an asymmetric relation between the price of crude oil and refined petroleum products, Energy Policy.
MacKinnon, J.G. Approximate asymptotic distribution functions for unit root and cointegration tests. Jounal of Business and Economic Statistics 12(2):167-176.
Newbold, P. and D.I. Harvey, 2002, Forecast Combination and Encompassing, In: A Companion to Economic Forecasting M. P. Clements and D.F. Hendry (eds.) Blackwell Publishers, Malden, MA.
Richmond, A. and R.K. Kaufmann, Is there an Environmental Kuznets curve for energy use and carbon emissions? The Energy Journal
Seto, K.C. and R.K. Kaufmann, 2003, Modeling the drivers of urban land-use change in the Pearl River Delta, China: Integrating remote sensing with socioeconomic data. Land Economics79(1):106-121.
Stanley, T.D. 2001. Wheat from chaff: meta-analysis as quantitiative literature review. J. Econ. Perspectives. 15(3) 131-150.
Wigley, T.M.L., R.L. Smith, and B.D. Santer, 1998: Anthropogenic influence on the autocorrelation structure of hemispheric mean temperatures. Science, 282, 1676-1678.