Friday, February 7, 2014

Vintage Years in Econometrics - The 1960's

Remember that saying - "if you can remember the 60's you probably weren't there"? Well, with that said, and continuing from my earlier posts about vintage years for econometrics in the 1930's, 1940's, and 1950's, here's my take on the 1960's.

Once again, let me note that "in econometrics, what constitutes quality and importance is partly a matter of taste - just like wine! So, not all of you will agree with the choices I've made in the following compilation."

  • Chow, G. C., Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591–605.1
  • Griliches, Z. and Y. Grunfeld, Is aggregation necessarily bad? Review of Economics and Statistics, 42, 1-13. 
  • Solow, R. M., On a family of lag distributions. Econometrica, 28, 393–406.
  • Wold, H., A generalization of causal chain models. Econometrica, 28, 443-463.
  • Arrow, K. J., H. B. Chenery, B. S. Minhas, and R. M. Solow, Capital-labor substitution and economic efficiency. Review of Economics and Statistics,  43, 225–250.
  • Mundlak, Y., Empirical production function free of management bias. Journal of Farm Economics, 43, 44–56. 2
  • Sargan, J. D., The maximum likelihood estimation of economic relationships with autoregressive residuals. Econometrica, 29, 414-426.
  • Bergstrom, A. R., The exact sampling distributions of least squares and maximum likelihood estimators of the marginal propensity to consume. Econometrica, 30, 480-490. 
  • Klein, L. R., An Introduction to Econometrics, Prentice-Hall, Englewood Cliffs, N.J..
  • Zellner, A.An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. Journal of the American Statistical Association57, 348–368.3
  • Zellner, A. and H. Theil, Three-stage least squares: Simultaneous estimation of simultaneous equations. Econometrica, 30, 54-78.
  • Johnston, J.,  Econometric Methods, first ed., McGraw-Hill, New York.4
  • Lovell, M. C., Seasonal adjustment of economic time series and multiple regression analysis. Journal of the American Statistical Association, 58, 993-1010.5
  • Theil, H., On the use of incomplete prior information in regression analysis. Journal of the American Statistical Association, 58,  401-414.
  • Barten, A. P., Consumer demand functions under conditions of almost additive preferences. Econometrica, 32, 1-38.
  • Box, G. E. P. and D. R. Cox, An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26, 211–252.
  • Chipman, J. S., On least squares with insufficient observations. Journal of the American Statistical Association, 59, 1078-1111.
  • Chipman, J. S. and M. M. Rao, On the treatment of linear restrictions in regression analysis. Econometrica, 32, 198-209.
  • Chow, G. C., A comparison of alternative estimators for simultaneous equations. Econometrica, 32, 532-553. 
  • Goldberger, A. S., Econometric Theory, Wiley, New York.
  • Malinvaud, E., Methodes Statistiques de l'Econometrie, Dunod, Paris.
  • Sargan, J. D., Wages and prices in the United Kingdom: A study in econometric methodology. In P. E. Hart, G. Mills, and J. K. Whitaker (eds.), Econometric Analysis for National Economic Planning, Vol. 16 of Colston Papers. Butterworth, London.6
  • Almon, S., The distributed lag between capital appropriations and expenditures. Econometrica, 33, 178-196.
  • Duesenberry, J. S., G. Fromm, L. R. Klein, and E. Kuh (eds.), The Brookings Quarterly Model of the United States, North-Holland, Amsterdam.
  • Goldfeld, A. S. and R. E. Quandt, Some tests for homoscedasticity. Journal of the American Statistical Association, 60, 539-547.
  • Goldfeld, A. S. and R. E. Quandt, Nonlinear simultaneous equations models: Estimation and prediction, International Economic Review, 9, 113-136.
  • Summers, R. L., A capital intensive approach to the small sample properties of various simultaneous equation estimators," Econometrica, 33, 1-41.
  • Theil, H.,The information approach to demand analysis. Econometrica, 33, 67-87
  • Balestra, P. and M. Nerlove, Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34, 585-612.
  • Christ, C., Econometric Models and Methods, Wiley, New York.
  • Eisenpress, H.  and J. Greenstadt, The estimation of nonlinear econometric systems. Econometrica, 34, 851-861.
  • Fisher, F. M., The Identification Problem in Econometrics, McGraw-Hill, New York,
  • Jorgenson, D., Rational distributed lag functions. Econometrica, 34, 135-149.
  • Judge, G. G. and T. Takayama, Inequality restrictions in regression analysis. Journal of the American Statistical Association, 61, 166-181.
  • Malinvaud, E.Statistical Methods of Econometrics, First English Edition, North-Holland, Amsterdam.
  • Nerlove, M. and K. F. Wallis, Use of the Durbin-Watson statistics in inappropriate situations. Econometrica, 34, 235-238.7
  • Griliches, Z., Distributed lags: A survey. Econometrica, 35, 16-49. 
  • Jorgenson, D. W., Seasonal adjustment of data for econometric analysis. Journal of the American Statistical Association, 62, 137-140.
  • Steckler, H. O., Data revisions and economic forecasting. Journal of the American Statistical Association, 62, 470-483.
  • Barten, A. P., Estimating demand equations. Econometrica, 36, 213-251.
  • Chetty, V. K., Pooling of time series and cross section data. Econometrica, 36, 279-290.
  • Hymans, S. H., Simultaneous confidence intervals in econometric forecasting. Econometrica, 36, 18-30.
  • Kadiyala, K. R., A transformation used to circumvent the problem of autocorrelation. Econometrica, 36, 93-96.8
  • Granger, C. W. J.Investigating causal relations by econometric models and cross-spectral methods. Econometrica37,424–438.9
  • Ramsey, J. B., Tests for specification errors in classical linear least squares regression analysis. Journal of the Royal Statistical Society, B, 31, 350–371.10
  • Sawa, T., The exact sampling distribution of ordinary least squares and two stage least squares estimators. Journal of the American Statistical Association, 64, 923-937. 

Best vintages of the 1960's - 1962 and 1964!

Tasting Notes:
1. The Chow test.
2. The beginning of the literature on panel data.
3. The introduction of the Seemingly Unrelated Regression Equations model.
4. The first edition of a classic text, which popularized the now-standard matrix notation for the linear regression model. (We used this in the first undergraduate econometrics course that I took.)
5. Think, Frisch-Waugh-Lovell Theorem.
6. Arguably one of the most important papers of all time relating to econometric methodology.
7. The Durbin-Watson statistic is biased towards the value of 2 if the model includes a lagged dependent variable.
8. This paper provided the transformation for the first observation on the data when applying GLS to models with autocorrelated errors. Prior to this, the first observation was "dropped" from the regression when applying the Cochrane-Orcutt transformation.
9. The biggie! What more is there to say!
10. The RESET test, and much more. This paper was highly influential in the context of model specification testing, and led to many more "variable addition" tests by other authors.

© 2014, David E. Giles


  1. Even though the notation was nonstandard, and I think only the French could fully appreciate the geometric perspective that it emphasized, I simply loved Malinvaud's textbook.

    1. Me too - there is still material in it that is hard to find elsewhere.