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Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

Published online by Cambridge University Press:  04 January 2017

Nathaniel Beck  and
Jonathan N. Katz
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Abstract

In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.

Type
Research Article
Information
Political Analysis , Volume 6 , 1996 , pp. 1 - 36
Copyright
Copyright © Society for Political Methodology 

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References

Atunes, George, and Stimson, James. 1988. User's Guide to Microcrunch. Houston: SofTex Micro Systems.Google Scholar
Beck, Nathaniel. 1991. “Comparing Dynamic Specifications: The Case of Presidential Approval.” Political Analysis 3: 51–87.CrossRefGoogle Scholar
Beck, Nathaniel, and Katz, Jonathan N. 1995. “What to Do (and Not to Do) with Times-Series-Cross-Section Data.” American Political Science Review 89: 634–47.CrossRefGoogle Scholar
Blais, André, Blake, Donald, and Dion, Stéphane. 1993. “Do Parties Make a Difference? Parties and the Size of Government in Liberal Democracies.” American Journal of Political Science 37: 40–62.CrossRefGoogle Scholar
Burkhart, Ross, and Lewis-Beck, Michael. 1994. “Comparative Democracy: The Economic Development Thesis.” American Political Science Review 88: 903–10.CrossRefGoogle Scholar
Clingermayer, James C., and Dan Wood, B. 1995. “Disentangling Patterns of State Debt Financing.” American Political Science Review 89: 108–20.CrossRefGoogle Scholar
Engle, Robert. 1984. Wald, Likelihood Ratio and Lagrange Multiplier Tests in Econometrics. In Handbook of Econometrics, edited by Griliches, Z. and Intriligator, M. Vol. 2. Amsterdam: North-Holland, pp. 775–826.CrossRefGoogle Scholar
Engle, Robert, and Granger, C. W. J., eds. 1991. Long Run Relationships: Readings in Cointegration. New York: Oxford University Press.CrossRefGoogle Scholar
Freedman, David, and Peters, Stephen. 1984. “Bootstrapping a Regression Equation: Some Empirical Results.” Journal of the American Statistical Association 79: 97–106.CrossRefGoogle Scholar
Gilbert, Christopher. 1990. Professor Hendry's Econometric Methodology. In Modeling Economic Series, edited by Granger, C. W. J. New York: Oxford University Press, pp. 279–303.Google Scholar
Giles, Michael, and Hertz, Kaenan. 1994. “Racial Threat and Partisan Identification.” American Political Science Review 88: 317–26.CrossRefGoogle Scholar
Greene, William. 1993. Econometric Analysis. 2d ed. New York: Macmillan.Google Scholar
Harvey, Andrew. 1990. The Econometric Analysis of Time Series. 2d ed. Cambridge: MIT Press.Google Scholar
Hendry, David, and Mizon, Graham. 1978. “Serial Correlation as a Convenient Simplification, Not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England.” Economic Journal 88: 549–63.CrossRefGoogle Scholar
Hsaio, Cheng. 1986. Analysis of Panel Data. New York: Cambridge University Press.Google Scholar
Hurwicz, L. 1950. Least-Squares Bias in Time Series. In Statistical Inference in Dynamic Economic Models, edited by Koopmans, T. New York: Wiley, pp. 365–83.Google Scholar
King, Gary. 1990. “On Political Methodogy.” Political Analysis 2: 1–29.CrossRefGoogle Scholar
Kmenta, Jan. 1986. Elements of Econometrics. 2d ed. New York: Macmillan.Google Scholar
Levin, Andrew, and Lin, C.-F. 1993. “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties.” Discussion Paper 92-23 (revised), Department of Economics, University of California, San Diego.Google Scholar
Levobic, James. 1994. “Riding Waves or Making Waves? The Services and the U.S. Defense Budget, 1981-1993.” American Political Science Review 88: 839–52.Google Scholar
MacKinnon, James, and White, Halbert. 1985. “Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties.” Journal of Econometrics 29: 305–25.CrossRefGoogle Scholar
Nickell, S. 1981. “Biases in Dynamic Models with Fixed Effects.” Econometrica 49: 1417–26.CrossRefGoogle Scholar
Parks, Richard. 1967. “Efficient Estimation of a System of Regression Equations When Disturbances Are Both Serially and Contemporaneously Correlated.” Journal of the American Statistical Association 62: 500–509.CrossRefGoogle Scholar
Pollins, Brian. 1989. “Does Trade Still Follow the Flag?” American Political Science Review 83: 465–80.CrossRefGoogle Scholar
Stimson, James. 1985. “Regression in Space and Time: A Statistical Essay.” American Journal of Political Science 29: 914–47.CrossRefGoogle Scholar
Stock, James, and Watson, Mark. 1988. “Variable Trends in Economic Time Series.” Journal of Economic Perspectives 2(3): 147–74.CrossRefGoogle Scholar
White, Halbert. 1980. “A Heteroskedasticity-Consistent Covariance Matrix and a Direct Test for Heteroskedasticity.” Econometrica 48: 817–38.CrossRefGoogle Scholar
White, Halbert. 1984. Asymptotic Theory for Econometricians. Orlando: Academic Press.Google Scholar