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Recent Developments in Dynamic Econometric Modelling: A Personal Viewpoint

Published online by Cambridge University Press:  04 January 2017

G. S. Maddala
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Extract

The Gods love the obscure and hate the obvious.

Brihadaranyaka Upanishad (Pre-1000 B.C.)

The quotation above (from more than three thousand years ago) essentially summarizes my perception of what is going on in econometrics.

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Research Article
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Political Analysis , Volume 7 , 1998 , pp. 59 - 87
Copyright
Copyright © Society for Political Methodology 

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References

Achen, C. H. 1986. The Statistical Analysis of Quasi Experiments. Berkeley: University of California Press.Google Scholar
Alogoskoufis, G., and Smith, R. 1991. “On Error Correlation Models: Specification, Interpretation, Estimation.” Journal of Economic Surveys 5: 97–128.Google Scholar
Arellano, M., and Bover, O. 1995. “Another Look at the Instrumental Variable Estimation of Error-Components Models.” Journal of Econometrics 68: 29–51.Google Scholar
Balestra, P., and Nerlove, M. 1966. “Pooling Cross-Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas.” Econometrica 34: 585–612.Google Scholar
Banerjee, A., Dolado, J. J., Galbraith, J. W., and Hendry, D. F. 1993. Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data. Oxford: Oxford University Press.CrossRefGoogle Scholar
Bartels, L. M. 1996. “Pooling Disparate Observations.” American Journal of Political Science 40: 905–42.Google Scholar
Bartels, L. M. 1997. “Specification Uncertainty and Model Averaging.” American Journal of Political Science 41: 641–74.Google Scholar
Bartels, L. M., and Brady, H. E. 1993. “The State of Quantitative Political Methodology.” In Finifter, A. W. (ed.), Political Science: The State of the Discipline II, 121–59.Google Scholar
Beck, N. 1983. “Time-Varying Parameter Regression Models,” American Journal of Political Science 27: 557–600.CrossRefGoogle Scholar
Beck, N. 1989. “Estimating Dynamic Models Using Kalman Filtering.” Political Analysis 1: 121–56.Google Scholar
Beck, N. 1992. “The Methodology of Cointegration.” Political Analysis 4: 237–47.CrossRefGoogle Scholar
Beck, N., and Katz, J. N. 1995. “What to Do (and Not to Do) with Time-Series Cross-Section Data.” American Political Science Review 89: 634–47.CrossRefGoogle Scholar
Beck, N., and Katz, J. N. 1996. “Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models.” Political Analysis 6: 1–36.Google Scholar
Beck, N., and Katz, J. N. 1997. “The Analysis of Time-Series-Cross-Sectional Data with a Binary Dependent Variable.” Paper presented at the 1997 meeting of the Political Methodology Group, Ohio State University, Columbus, OH.Google Scholar
Berger, J. O., and Delampady, M. 1987. “Testing Precise Hypotheses,” Statistical Science 2: 317–52.Google Scholar
Bound, J., Jaeger, D. A., and Baker, R. M. 1995. “Problems with Instrumental Variable Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable Is Weak.” Journal of American Statistical Association 90: 443–50.Google Scholar
Box-Steffensmeier, J. M., and Smith, R. M. 1996. “The Dynamics of Aggregate Partisanship.” American Political Science Review 90: 567–80.Google Scholar
Box-Steffensmeier, J. M., and Smith, R. M. 1997. “Investigating Political Dynamics Using Fractional Integration Methods.” Forthcoming in American Journal of Political Science.CrossRefGoogle Scholar
Burke, S. P. 1994. “Confirmatory Analysis: The Joint Application of Stationarity and Unit Root Tests.” Discussion Paper # 20, University of Reading, U.K.Google Scholar
Cermeno, R. 1997. “Evaluating Convergence with Medium-Unbiased Estimators in Panel Data.” Department of Economics, Ohio State University, Paper presented at the 7th International Conference on Panel Data, Paris, June.Google Scholar
Cochrane, J. H. 1991. “A Critique of the Application of Unit Root Tests.” Journal of Economic Dynamics and Control 15: 275–84.CrossRefGoogle Scholar
Dufour, J. M., and Torres, O. 1996. “Union-Intersection and Sample-Split Methods in Econometrics with Application to MA and SURE Models.” Paper presented at the Summer Meetings of the Econometric Society, Iowa City.Google Scholar
Duncan, D. B., and Horn, S. D. 1972. “Linear Dynamic Recursive Estimation from the Viewpoint of Regression Analysis.” Journal of the American Statistical Association 67: 815–21.Google Scholar
Durbin, J. 1993. “Time Series Analysis Based on State-Space Modelling for Gaussian and Non-Gaussian Observations.” Lectures delivered at Ohio State University, Spring Quarter, 1993.Google Scholar
Durr, R. H. 1992a. “An Essay on Cointegration and Error Correction Models.” Political Analysis 4: 185–228.Google Scholar
Durr, R. H. 1992b. “Of Forests and Trees.” Political Analysis 4: 255–58.Google Scholar
Elliott, G. 1995. “On the Robustness of Cointegration Methods When Regressors Almost Have Unit Roots.” Discussion Paper 95-18, University of California, San Diego.Google Scholar
Elliott, G., Rothenberg, T. J., and Stock, J.H. 1996. “Efficient Tests for an Autoregressive Unit Root.” Econometrica 64: 813–36.Google Scholar
Elliott, G., and Stock, J. H. 1994. “Inference in Time-Series Regression When the Order of Integration of the Regressor Is Known,’ Econometric Theory 10: 672–700.Google Scholar
Fisher, R. A. 1932. “Statistical Methods for Research Workers.” 4th ed. Edinburgh: Oliver and Boyd.Google Scholar
Franklin, C. H. 1989. “Estimation across Data Sets: Two-Stage Auxiliary Instrumental Variables Estimation (2SAIV).” Political Analysis 1: 1–23.Google Scholar
Freeman, J. R. 1988. “Granger Causality and the Time Series Analysis of Political Relationships.” American Journal of Political Science 27: 327–58.Google Scholar
Freeman, J. R. 1989. “Systematic Sampling, Temporal Aggregation, and the Study of Political Relationships.” Political Analysis 1: 61–98.CrossRefGoogle Scholar
Freeman, J. R., Houser, D., Kellstedt, P., and Williams, J. T. 1996. “Unit Roots and Casual Inference in Political Science.” Paper presented at the Political Methodology Summer Meetings, Ann Arbor, MI.Google Scholar
Friedman, M., and Schwartz, A. J. 1991. “Alternative Approaches to Analyzing Economic Data.” American Economic Review 81: 39–49.Google Scholar
Granato, J. 1991. “An Agenda for Econometric Model Building,” Political Analysis 3: 123–54.Google Scholar
Granger, C. W. J. 1969. “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods.” Econometrica 37: 424–38.Google Scholar
Granger, C. W. J. 1980. “Testing for Causality: A Personal Viewpoint.” Journal of Economic Dynamics and Control 2: 329–52.Google Scholar
Granger, C. W. J., and Swanson, N. 1996. “Future Developments in the Study of Cointegrated Variables.” Oxford Bulletin of Economics and Statistics 58: 537–53.Google Scholar
Harvey, A. C. 1989. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.Google Scholar
Harvey, A. C., and Jaeger, A. 1993. “Detrending, Stylized Facts and the Business Cycle.” Journal of Applied Economics 8: 31–47.Google Scholar
Hsiao, C. 1997. “Cointegration and Dynamic Simultaneous Equations Model.” Econometrica 65: 647–670.Google Scholar
Im, K. S., Pesaran, M. H., and Shin, Y. 1996. “Testing for Unit Roots in Heterogeneous Panels.” Discussion paper, University of Cambridge.Google Scholar
Jackson, J. E. 1991. “Estimation of Models with Variable Coefficients.” Political Analysis 3: 27–49.Google Scholar
Johansen, S. 1995. Likelihood Based Inference on Cointegration in the Vector Autoregressive Model. Oxford: Oxford University Press.Google Scholar
Johansen, S., and Juselius, K. 1994. “Identification of the Long-Run and Short-Run Structure: An Application to the IS-LM Model,” Journal of Econometrics 63: 7–37.CrossRefGoogle Scholar
Keane, M. P. 1993. “Simulation Estimation for Panel Data Models with Limited Dependent Variables.” In Maddala, G. S., Rao, C. R., and Vinod, H. D. (eds.), Handbook of Statistics. Vol. 11: Econometrics. Amsterdam: Elsevier.Google Scholar
Kellstedt, P., McAvoy, G. E., and Stimson, J. A. 1993. “Dynamic Analysis with Latent Constructs.” Political Analysis 5: 113–50.Google Scholar
King, G. 1989. Unifying Political Methodology; The Likelihood Theory of Statistical Inference. Cambridge: Cambridge University Press.Google Scholar
Kiviet, J. F. 1995. “On Bias, Inconsistency and Efficiency of Various Estimators in Dynamic Panel Data Models.” Journal of Econometrics 68: 53–78.Google Scholar
Kiviet, J. F., and Phillips, G. D. A. 1993. “Alternative Bias Approximations with a Lagged Dependent Variable.” Econometric Theory 9: 62–80.Google Scholar
Koopmans, S. J., Harvey, A. C., Doornik, J. D., and Shepherd, N. 1995. STAMP: 5.0 Structural Time Series Modeller and Predictor. London: Chapman and Hall.Google Scholar
Kwiatowski, D., Phillips, P. C. B., Schmidt, P., and Shin, Y. 1992. “Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root.” Journal of Econometrics 54: 159–78.Google Scholar
Lauritzen, S. L. 1981. “Time Series Analysis in 1880: A Discussion of Contributions Made by T. N. Thiele.” International Statistical Review 49: 319–31.Google Scholar
Levin, A., and Lin, C. F. 1992. “Unit Root Test in Panel Data: Asymptotic and Finite Sample Properties.” University of California at San Diego, Discussion paper, #92-93.Google Scholar
Levin, A., and Lin, C. F. 1993. “Unit Root Test in Panel Data: New Results.” University of California at San Diego, Discussion paper, #93-56.Google Scholar
Li, H., and Maddala, G. S. 1997. “Bootstrapping Cointegrating Regressions.” Journal of Econometrics 80: 297–318.Google Scholar
Lubrano, M. 1995. “Testing for Unit Roots in a Bayesian Framework.” Journal of Econometrics 69: 81–109.Google Scholar
Maddala, G. S. 1971a “On the Use of Variance Component Models in Pooling Cross-Section and Time-Series Data” Econometrica 39: 341–58.Google Scholar
Maddala, G. S. 1971b. “The Likelihood Approach to Pooling Cross-Section and Time-Series Data” Econometrica 39: 939–53.Google Scholar
Maddala, G. S. 1977. Econometrics. New York: McGraw-Hill.Google Scholar
Maddala, G. S. 1987a “Limited Dependent Variable Models Using Panel Data” Journal of Human Resources 22: 307–60.Google Scholar
Maddala, G. S. 1987b. “Recent Developments in the Econometrics of Panel Data Analysis.” Transportation Research 21A: 303–26.Google Scholar
Maddala, G. S. 1991a “To Pool or Not to Pool: That Is the Question.” Journal of Quantitative Economics 7: 255–64.Google Scholar
Maddala, G. S. 1991b. “Of What Use Are Cointegration Tests?” Lecture delivered at the Bank of Korea, June, 1991.Google Scholar
Maddala, G. S. 1992. Introduction to Econometrics, 2nd ed. New York: Macmillan. (3rd ed., forthcoming 1999).Google Scholar
Maddala, G. S., and Hu, W. 1995. “The Pooling Problem.” In Matyas, L. and Sevestre, P. (eds.), The Econometrics of Panel Data Boston, MA: Kluwer.Google Scholar
Maddala, G. S., and Kim, I. M. 1996. “Structural Change and Unit Roots.” Journal of Statistical Planning and Inference 49: 73–103.Google Scholar
Maddala, G. S., and Kim, I. M. 1998. Unit Roots, Cointegration and Structural Change. Cambridge: Cambridge University Press.Google Scholar
Maddala, G. S., and Wu, S. 1996. “A Comparative Study of Alternative Panel Data Unit Root Tests and a New Simple Test.” Paper presented at the Econometric Society Meetings, New Orleans, January, 1997. Forthcoming in Oxford Bulletin of Economics and Statistics.Google Scholar
Maddala, G. S., and Yin, Y. 1997. “Outliers, Unit Roots and Robust Estimation of Nonstationary Time Series.” In Handbook of Statistics. Vol. 15: Robust Inference. Amsterdam: Elsevier Science, 237–66.Google Scholar
Maddala, G. S., Li, H., Trost, R. P., and Joutz, F. 1997. “Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators.” Journal of Business Economics and Statistics 15: 90–100.Google Scholar
Mizon, G. E. 1995. “A Simple Message for Autocorrelation Correctors: Don't.” Journal of Econometrics 69: 267–88.Google Scholar
Nicholls, D. F., and Pagan, A. R. 1985. “Varying Coefficient Regression.” Chapter 16 in Hannan, E. J., Krishniah, P. R., and Rao, M. M. (eds.), Handbook of Statistics. Vol 5. North Holland.Google Scholar
Nerlove, M. 1997a. “Properties of Alternative Estimators of Dynamic Panel Data Models.” Manuscript, University of Maryland.Google Scholar
Nerlove, M. 1997b. “Likelihood Inference with Panel Data” Paper presented at the Seventh International Conference on Panel Data, Paris.Google Scholar
Ng, S., and Perron, P. 1997. “Estimation and Inference in Nearly Unbalanced, Nearly Cointegrated Systems.” Journal of Econometrics 79: 53–81.Google Scholar
Ostrom, C. W., and Smith, R. M. 1992. “Error Correction, Attitude Persistence, and Executive Rewards and Punishments: A Behavioral Theory of Presidential Approval.” Political Analysis 4: 127–83.CrossRefGoogle Scholar
Pagan, A. R. 1989. “20 Years After: Econometrics 1966-1986.” In Cornet, B., and Tulkens, H. (eds.), Contributions to Operations Research and Econometrics, XXth Anniversary of CORE. Cambridge, MA: MIT Press.Google Scholar
Perron, P., and Ng, S. 1996. “Useful Modifications to Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties.” Review of Economic Studies 63: 435–65.Google Scholar
Phillips, P. C. B. 1995. “Bayesian Model Selection and Prediction with Empirical Applications.” With comments by Palm, F. C. and Richard, J. F. and reply by Phillips, . Journal of Econometrics 69: 289–365.Google Scholar
Rao, C. R. 1952. Advanced Statistical Methods in Biometric Research. New York: Wiley Google Scholar
Sargan, J. D. 1964. “Wages and Prices in the United Kingdom: A Study in Econometric Methodology.” In Hart, P. E., Mills, G. and Whitaker, J. K. (eds.), Econometric Analysis for Economic Planning. London: Butterworths. Reprinted in D. F. Hendry and K. F. Wallis (eds.) Econometrics and Quantitative Economics. Oxford: Blackwell, 275-314.Google Scholar
Smith, R. M. 1992. “Error Correction, Attractors, and Cointegration: Substantive and Methodological Issues.” Political Analysis 4: 249–54.Google Scholar
Snyder, R. D. 1990. “Why Kalman Filter?” Working paper #11/90, Monash University.Google Scholar
Sorenson, H. W. 1970. “Least Squares Estimation from Gauss to Kalman.” IEEE Spectrum 7: 63–68.Google Scholar
Stock, J. H. 1994. “Unit Roots, Structural Breaks and Trends.” In Handbook Google Scholar
of Econometrics, vol. IV, Engle, R. F. and McFadden, D. (eds.), Amsterdam: Elsevier.Google Scholar
Uhlig, H. 1994. “What Macroeconomists Should Know about Unit Roots: A Bayesian Perspective.” Econometric Theory 10: 645–71.Google Scholar
Williams, J. T. 1992a. “Dynamic Change, Specification Uncertainty, and Bayesian Vector Autoregression Analysis.” Political Analysis 4: 97–125.Google Scholar
Williams, J. T. 1992b. “What Goes Around Comes Around: Unit Root Tests and Cointegration.” Political Analysis 4: 229–35.Google Scholar
Zivot, E. 1994. “A Bayesian Analysis of the Unit Root Hypothesis within an Unobserved Components Model.” Econometric Theory 10: 552–78.Google Scholar