Agricultural and Applied Economics 637
Applied Econometric Analysis II
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Class Email List:
Instructor:Brian W. Gould, 418Taylor Hall, 3-3212
Email: bwgould@.wisc.edu
Class: TR, 9:30-10:45 B30 Taylor Hall/HECC
Lab: W, 1:00 p.m.-2:00 p.m., B30 Taylor Hall
Office Hours: TR: 11:00 p.m.-12:30 p.m.
Course Objective: The course focuses on the development and use of more advanced econometrics techniques that follow naturally from the classical regression model. The course emphasizes empirical applications, illustrating the practical methods and challenges associated with analyzing finite samples of economic data. The course should be of interest to students of economics, business, public policy, and other disciplines in need of a more in-depth understanding of applied regression methods. Some knowledge of linear algebra is required! After taking this course you should be able to understand the empirical work of other applied economists.
To enable you to better understand econometrics, I want to get you inside the econometrics “black box”. I find the easiest way to do this is to have you actually program your own estimators and calculate parameter and regression-related statistics instead of using a canned package. We use the GAUSS software system to achieve this objective. All assignments for this class are to be done using this software. By doing your own programming, you will be able to understand how the various statistics displayed in the output of canned econometric/statistical packages you may use in the futureare actually calculated.
Required Text
Greene, W., Econometric Analysis, 6th Ed., Prentice-Hall, 2008, Textbook website, Errata website
Recommended Text
Train, K.E., Discrete Choice Methods with Simulation, CambridgeUniversity Press, 2003. This text will be very useful for those undertaking analyses where the dependent variable is discrete. e.g., 0,1. Prof. Train makes available an online course on discrete choice analysis [with simulation] based on this text. This course is available at: . The computer code used in the empirical applications which forms the basis of the text is also available and he even uses GAUSS!
Supplementary Texts
Judge, G.G., R.C. Hill, W.E. Griffths, H. Lutkepohl,and T.C. Lee (JHGLL), Introduction to the Theory and Practice of Econometrics, 2nd ed., John Wiley and Sons, New York, 1988. This book is out of print and I have made copies of relevant chapters. I would strongly recommend that you consider obtaining a used version for your library. Although very dry, it has a very good summary and presentation of basic econometric methods. This was previously the required text for AAE636. There should be plenty of used versions around.
Gweke, J.F., J.L. Horowitz and M. Pesaran, 2006. Econometrics: A Birds Eye View, IZA Discussion Paper No. 2458, November, Bonn. This is an unpublished paper that basically gives a history of econometrics. It covers much more material than we will cover. It shows where the various dimensions of econometrics intersect.
Train, K.E., Qualitative Choice Analysis: Theory, Econometrics and an Application to Automobile Demand, MIT Press, Cambridge, MA, 1993.
This is a good text that describes the use of discrete (binary) choice analysis applied to consumer demand.
Gauss Users Manualand Gauss Language Referencefrom APTECH. Be aware the Language Reference PDF is 1048 pages long and Users Manual has 470. The Language Reference PDF describes each of the commands, procedures and functionsavailable in the GAUSSprogramming language. The Users Manual provides some of the fundamentals that will be useful for both the casual as well as heavy user of the GAUSS software system. Both of these are available from within the GAUSS software system. There is also available a Quick Start Guide that you may want to look at first.
Gauss9.0 Light, APTECH Systems, Inc., 2008. GAUSSis a complete analysis environment suitable for performing quick calculations, complexanalysis of millions of data points, or anything in between. Whether you are new to computerizedanalysis or a seasoned programmer, the GAUSS family of products provides an easyto learn environment that is powerful and versatile enough for virtually anynumerical task. (This is the student version of the GAUSS software system. It is a fully functional program except that any one matrix can not have more than 10,000 elements.) This software is available for download from the class website at no cost by clicking on the above. If you have to develop a model that exceeds this limit, you can develop your code using this version and a smaller data set and when you want to run the full version simply bring your code down to the HECC or use the remote server and run your code using thefull version of GAUSS)
Course Evaluation
40% Assignments
30% Mid-Term Exam
20% Term Paper/Final Exam
10% My Fudge Factor
Course Outline
Review of the Classical Regression Model(On your own)
a. Properties of Parameter Estimates Under the Classical Regression Model b. Hypothesis Testing Under the Classical Regression Model c. Regression Model with General Error Variance Structure: Heteroscedasticity and Autocorrelation
Readings: Greene, Ch. 2: 8-19, Ch. 3: 20-39, Ch. 4: 43-63,
Ch. 5: 81-102, Ch. 8: 148-175, Ch. 19: 626-629,632-634,
644-652
JHGLL, Ch. 3: 58-111, Ch. 5: 157-211, Ch. 6: 221-273, Ch. 8: 327-347
Ch. 9: 351-409
I. Before we start with the new econometrics related material we are going to have to undertake a series of “workshops” illustrating the use the GAUSS software system for estimating a variety of econometric models.
a. Workshop #1–Introduction to GAUSS: Workshop Materials
b. Workshop #2 – Using and Writing Procedures in GAUSS: Workshop Materials
c. Workshop #3 – Using and Writing Procedures in GAUSS, the Classical
Regression Model: Workshop Materials
d. Workshop #4 – Using and Writing Procedures in GAUSS, Extending the CRM
Procedure for Hypothesis Testing: Workshop Materials
II. Nonlinear Regression Models
a. Nonlinear Least Squares (NLS)
b. NLS Estimation of the General Variance Model
c. Hypothesis Testing and Parametric Restrictions
Readings: Greene, Ch 11: 285-300, 302-307
JHGLL, Ch. 12: 497-530,532-535
Amemiya, T., 1983. Non-linear Regression Models, Ch. 6, Handbook of Econometrics, Vol. 1, Elsevier Pub. Co.
Cochrane, D., G.H. Orcutt, 1949. Application of Least Squares Regressions to Relationships Containing Autocorrelated Error Terms, Journal of the American Statistical Association, 44:32-61.
Gallant, A.R., 1975. Nonlinear Regression, The American Statistician, 29(2):73-81
MacKinnon, J., H. White, and R. Davidson, 1983, Tests for Model Specification in the Presence of Alternative Hypotheses: Some Future Results, Journal of Econometrics, 21:53-70.
Mizon, G.E., 1977. Inferential Procedures in Nonlinear Models: An Application to UK Industrial Cross Section Study of Factor Substitution and Returns to Scale, Econometrica, 45(5):1221-1242.
Motulsky, H. and L. Ransnas, 1987. Fitting Curves to Data Using Nonlinear Regression: A Practicaland Non-Mathematical Review, FASEB Journal, 1:365-374.
III. An Introduction to Maximum Likelihood Methods
a. Overview of Maximum Likelihood Techniques
b. Maximum Likelihood Estimation and Hypothesis Testing c. Application of Maximum Likelihood Techniques Applied to the Classical
Regression
d. Application of Maximum Likelihood Techniques to the General Variance and Nonlinear Regression Models
Readings: Greene, Ch. 16: 482-496,498-511, 517-529
JHGLL, Ch. 3: 62-66
Ch 6: 221-230
Ch. 12: 522-527, 529-530, 538-551
Buse, A., 1982. The Likelihood Ratio, Wald and Lagrange Multiplier Tests: An Expository Note, The American Statistician, 36(3):153-157.
Elison, S., 1993. Maximum Likelihood Estimation: Logic and Practice, Sage Series in Quantitative Applications in the Social Sciences, #96, London, p.1-45.
Harvey, A.C., 1976. Estimation of Regression Models with Multiplicative Heteroscedasticity, Econometrica, 44:461-465.
IV. Estimation of a System of Regression Equations
a. An Example of a Consumer Demand System
b. The Linear Seemingly Unrelated Regression (SUR) Model
c. The Linear SUR with a General Variance Structure
d. GLS and Maximum Likelihood Estimation
e. Nonlinear SUR models
Readings: Greene, Ch. 10: 252-267, 272-280
JHGLL, Ch. 11: 443-462
Banks, J. R. Blundell and A. Lewbel, 1997. Quadratic Engel Curves and Consumer Demand, The Review of Economics and Statistics, 79(4):527-539
Berndt, E.R., and D. Wood, 1975. Technology, Prices and The Derived Demand for Energy, The Review of Economics and Statistics, LVII (Aug):259-268
Berndt, E.R., and N. Savin, 1975. Estimation and Hypothesis Testing in Singular Equation Systems with Autoregressive Disturbances, Econometrica, 43(5/6):937-958.
Bollino, C.A., 1987. GAIDS: A Generalized Version of the Almost Ideal Demand System. Economics Letters, 23:199–203.
Bollino, C.A., Violi, R., 1990. GAITL: A Generalized Version of the Almost Ideal and Translog Demand Systems. EconomicsLetters, 34:127–129.
Christianson, L. and W. Greene, 1976. Economies of Scale in U.S. Electric Power Generation, Journal of Political Economy, 84:655-676
Christensen, L., D. Jorgenson and L. Lau, 1975. Transcendental Logrithmic Utility Functions, The American Economic Review, 65(3):367-383.
Deaton, A., and J. Muellbauer, 1980. An Almost Ideal Demand System, The American Economic Review, 70(3):312-326.
Gould, B.W., T.L. Cox and F. Perali, 1991. Demand for Food Fats and Oils: The Role of Demographic Variables and Government Donations, American Journal of Agricultural Economics, 73:212-221.
Gould, B.W., T.L. Cox and F. Perali, 1990. The Demand for Fluid Milk in the U.S.: A Demand Systems Approach, Western Journal of Agricultural Economics, 15:1-12.
McElroy, C., 1977. Goodness of Fit For Seemingly Unrelated Regressions, Journal of Econometrics, 6:381-387.
Zellner, A., An Efficient Methodof Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias, Journal of the American Statistical Association, 57(298):348-368.
V. Models of Discrete Choice
a. Models for Binary Choice
b. Estimation/Inference in Binary Choice Models
c. Multiple Discrete Choice Models
d. Ordered Probability Models
Readings: Greene, Ch 23: 770-794, 813-822, 831-836, 841-859
JHGLL, Ch 19: 785-795
Train(2003), Ch. 1: 1-11, Ch. 2: 15-37, Ch. 3: 38-79, Ch. 4: 80-97,
Ch. 5: 101-118, Ch. 8: 189-205
Anderson, S., and R. Newell, 2003. Simplified Marginal Effects in Discrete Choice Models, Economics Letters, 80:321-326.
Ali, C. and E. Norton, 2003. Interaction Terms in Logit and Probit Models, Economics Letters, 80:123-129.
Cragg, J.G. and R. Uhler, 1970. The Demand for Automobiles, Canadian Jourl of Economics, 3:386-406.
Greene, W., 1998. Gender Economics Courses in Liberal Arts Colleges: Further Results, Journal of Economic Education, 24(4):291-300.
Greene, W., 1996. Marginal Effects in the Bivariate Probit Model, Working Paper No. 97-02, Department of Economics, Stern School of Business, New York University
Jimenez, J and M. Salas-Velasco, 2000. Modeling Educational Choices: A Binomial Logit Model Applied to the Demand for Higher Education, Higher Education, 40:293-311.
McFadden, D., 1973. Conditional Logit Analysis of Qualitative Choice Behavior, in P.Zarembka (ed.), Ch. 4, Frontiers of Econometrics: 105-142, Academic Press, New York.
McKelvey, R. and W. Zavoina, 1975. A Statistical Model for the Analysis of Ordinal Level Dependent Variables, Journal of Mathematical Sociology, 40(4):103-120.
Nagubadi, V., K. McNamara, W. Hoover and W. Mills, 1996. Program Participation Behavior of Nonindustrial Forest Landowners: A Probit Analysis, Journal of Agricultural and Applied Economics, 28(2):323-336.
Nayga, R., 1996. Consumer Demand for Poultry At-Home and Away-From-Home: A Discrete Choice Analysis, Applied Economics Letters, 3:669-672.
Uhler, R.S. and J.G. Cragg, 1971. The Structure of Asset Portfolios of Households, Review of Economic Studies, 38:341-357.
Verbeke, W., R.W. Ward and J. Viane, 2000. Probit Analysis of Fresh Meat Consumption in Belgium: Exploring BSE and Television Communication Impact, Agribusiness, 16(2):215-234.
VI. Limited Dependent Variable, Count and Duration Models
a. Truncated Regression
b. Censored Regression
c. Sample Selection Model
d. Models of Event Counts
e. Overview of Duration Models/Event History Analysis
Readings: Greene: Ch. 24: 863-898
Ch. 25: 906-915, 922-943
Adesina, A.A. and M.M. Zinnah, 1993. Technology Characteristics, Farmers’ Perceptions and Adoption Decisions: A Tobit Model Application in Sierra Leone, Agricultural Economics, 9:297-311.
Berk, R.A., 1983. An Introduction to Sample Selection Bias in Sociological Data, American Sociological Review, 48(3):386-398.
Blundell, R. and C. Meghir, 1987. Bivariate Alternatives to the Tobit Model, Journal of Econometrics,34:179-200.
Brown, C. and R. Moffitt, 1983. The Effect of Ignoring Heteroscedasticity on Estimates of the Tobit Model, NBER Technical Paper No. 27, January
Cameron, A.C. and P.K. Tirvedi, 1986. Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators, Journal of Applied Econometrics, 1:29-53.
Cameron, A.C. P.K. Trivedi, F. Milne and J. Piggott, 1988. A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia, The Review of Economic Studies, 55(1):85-106.
Cuddeback, G., E. Wilson, J. Orme, and T. Combs-Orme, 2004., Detecting and Statistically Correcting Sample Selection Bias, Journal of Social Service Research, 30(3):19-33
Gould, B.W., W.E. Saupe and R.M. Klemme, 1989. Conservation Tillage: The Role of Farm and Operator Characteristics and the Perception of Soil Erosion, Land Economics, 65(2): 167-182.
Gould, B.W., 1997. Consumer Promotion and Purchase Timing: The Case of Cheese, Applied Economics, 29:445-457.
Greene, W., 1999. Marginal Effects in the Censored Regression Model, Economics Letters, 64:43-49.
Greene, W., 1981. Sample Selection Bias as a Specification Error: Comment, Econometrica, 49:795-798
Hausman, J.A. and D. A. Wise, 1977. Social Experimentation, Truncated Distributions and Efficient Estimation, Econometrica, 45(4):919-938.
Heckman, J.,1979. Sample Selection Bias as a Specification Error, Econometrica, 47(1):153-162
Keifer, N.M., 1988. Economic Duration Data and Hazard Functions, Journal of Economic Literature, 26(2):646-679.
McDonald, J.F. and R.A. Moffitt, 1980. The Uses of Tobit Analysis, The Review of Economics and Statistics, 62:318-321.
Norris, P.E. and S.S. Batie, 1987. Virginia Farmers’ Soil Conservation Decisions: An Application of Tobit Analysis, Southern Journal of Agricultural Economics, 19(1):79-90.
Stolzenberg, R.M., and D.A. Relles, 1997. Tools for Intuition about Sample Selection Bias and Its Correction, American Sociological Review, 62(3): 494-507.
Wang, Q., C. Halbrendt, J. Kolodinsky and F. Schmidt, 1997. Willingness to Pay for rBST-Free Milk: A Two-Limit Tobit Model Analysis, Applied Economics Letters, 4(10):619-621
Winkelmann, R. and K. Zimmermann, 1995. Recent Developments in Count Data Modeling: Theory and Application, Journal of Economic Surveys, 9(1):1-24
VII. An Introduction to Spatial Econometric Models
a. General Overview of Spatial Error Structure
b. Some Simple Examples
c. Estimation using GLS and Maximum Likelihood Techniques
Readings: Greene, Ch. 9: 218-222.
Anselin, Luc, 1999. Spatial Econometrics, Unpublished manuscript, University of Texas
Anselin, Luc, and A. Bera, 1998. Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics, in Handbook of Applied Economic Statistics by A. Ullah and D. Giles, eds., Marcel Decker, Inc., New York.
Bockstael, N., 1996. Modeling Economics and Ecology: The Importance of a Spatial Perspective, American Journal of Agricultural Economics, 78:1168-1180.
Gilley, O.W., and R.K. Pace, 1996. On the Harrison and Rubinfeld Data, Journal of Environmental Economics and Management, 31:403-405.
Harrison, D. and D.L. Rubinfeld, 1978. Hedonic Housing Prices and the Demand for Clear Air, Journal of Environmental Economics and Management, 5:81-102.
LeSage, J., 1997. Regression Analysis of Spatial Data, The Journal of Regional Analysis and Policy, 27(2):83-94.
LeSage, J., Spatial Econometrics, Chapters I and II, Web Book of Regional Science, Regional Research Institute, West VirginiaUniversity, 1999
Pace, R.K., and O.W. Gilley, 1997. Using the Spatial Configuration of the Data to Improve Estimation, Journal of Real Estate Finance and Economics, 14:333-340.
Roe, B., E. Irwin, J. Sharp, 2002. Pigs in Space: Modeling the Spatial Structure of Hog Production in Traditional and Nontraditional Production Regions, American Journal of Agricultural Economics, 84(2):259-278.
VIII. Panel/Pooled Data Methods
a. Overview of the Fixed Effects Model
b. Overview of the Random Effects Model
c. Covariance Structures for Pooled Time-Series Cross-Sectional Data
d. Application of SUR to Panel Data and Spatially Correlated Error Terms
Readings: Greene, Ch. 9: 181-213, 218-222, Ch. 10: 267-272.
JHGLL, Ch. 11: 468-490
Yaffee, R.A., 2003. A Primer for Panel Data Analysis, unpublished paper, New YorkUniversity
AAE63707!
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