Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-29T00:47:12.320Z Has data issue: false hasContentIssue false
  • English
  • Français

On Rereading Haavelmo: A Retrospective View of Econometric Modeling

Published online by Cambridge University Press:  18 October 2010

Aris Spanos
Aris Spanos
Affiliation:
Virginia Polytechnic Institute and State University
Get access Rights & Permissions [Opens in a new window]

Abstract

The main aim of the paper is to reevaluate the methodological contributions of Tinbergen and Haavelmo in the context of the current discussions on econometric modeling and propose a reformulation of the Haavelmo methodology. The paper argues that the textbook methodology constitutes a less flexible version of Tinbergen's approach and apart from the probabilistic language, it has little in common with the methodology in Haavelmo's 1944 monograph, commonly acknowledged as having founded modern econometrics. The methodology in this monograph includes several important elements which have either been discarded or never fully integrated within the textbook approach. A re-synthesis of these elements gives rise to an alternative methodological framework. This framework can be used to meet most of the objections to the textbook methodology and provides a framework in the context of which the recent methodological controversies can be evaluated.

Type
Articles
Information
Econometric Theory , Volume 5 , Issue 3 , December 1989 , pp. 405 - 429
Copyright
Copyright © Cambridge University Press 1989

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1.Christ, C.F.Early progress in estimating quantitative economic relationships in America. American Economic Review 75 (1985): 39–52.Google Scholar
2.Cochrane, W. & Orcutt, G.. Application of least-squares regression to relationships containing autocorrelated error terms. Journal of the American Statistical Association 44 (1949): 32–61.Google Scholar
3.Doob, J.L.Stochastic processes. New York: John Wiley and Sons, 1953.Google Scholar
4.Durbin, J. & Watson, G.S.. Testing for serial correlation in least-squares regression, I. Biometrica 37 (1950): 409–428.Google ScholarPubMed
5.Engle, R.F., Hendry, D.F., & Richard, J. F.. Exogeneity, Econometrica 51 (1983): 277–304.CrossRefGoogle Scholar
6.Epstein, R.J.A history of econometrics. Amsterdam: North-Holland, 1987.Google Scholar
7.Fox, K.A. Econometrics needs a history: two cases of conspicuous neglect. In Sen-gupta, J.K. & Kadekodi, G.K. (eds.), Econometrics of Planning and Efficiency, The Netherlands: Martinus Nijhoff, Dordrecht, 1988.Google Scholar
8.Frisch, R.Editorial. Econometrica 1 (1933): 1–4.Google Scholar
9.Frisch, R.Report of the Oxford Meeting. Econometrica 5 (1937): 361–383.Google Scholar
10.Frisch, R.Statistical versus theoretical relations in economic macro-dynamics. A memorandum presented at the Cambridge Business Cycle Conference, July 18–20, 1938.Google Scholar
11.Gilbert, C.L.The development of British econometrics 1945–1985. Oxford Institute of Economics and Statistics, Discussion Paper No. 8, 1987.Google Scholar
12.Goldberger, A.S.Econometric theory. New York: Wiley, 1964.Google Scholar
13.Granger, C.W.J. & Newbold, P.. Forecasting economic time series. London: Academic Press, 1977.Google Scholar
14.Griliches, Z. & Intriligator, M.D. (eds.) Handbook of econometrics, vol. I. Amsterdam: North–Holland, 1983.Google Scholar
15.Haavelmo, T.The statistical implications of a system of simultaneous equations. Econometrica 11 (1943): 1–12.CrossRefGoogle Scholar
16.Haavelmo, T.The probability approach in econometrics. Econometrica (Suppl.) 12 (1944): 1–115.CrossRefGoogle Scholar
17.Hendry, D.F.Econometric modeling: the consumption function in retrospect. The Scottish Journal of Political Economy 30 (1983): 193–220.CrossRefGoogle Scholar
18.Hendry, D.F. & Spanos, A.. Disequilibrium and latent variables. London School of Economics, Unpublished Paper, 1980.Google Scholar
19.Hendry, D.F. & Richard, J.F.. On the formulation of empirical models in dynamic econometrics. Journal of Econometrics 20 (1982): 3–33.CrossRefGoogle Scholar
20.Hendry, D.F. & Richard, J.F.. The econometric analysis of economic time series. International Statistical Review 51 (1983): 111–163.CrossRefGoogle Scholar
21.Hendry, D.F.PC-GIVE: an interactive econometric modeling system. Institute of Economics and Statistics, University of Oxford, 1989.Google Scholar
22.Hood, W.C. & Koopmans, T.C. (eds.) Studies in econometric method. Cowles Commission monograph No. 14. New York: John Wiley, 1983.Google Scholar
23.Johnston, J.Econometric methods, 2nd edition (1972). New-York: MacGraw-Hill, 1963.Google Scholar
24.Judge, G.G. & Bock, M.E.. Biased estimation. In Griliches, Z. & Intriligator, M.D. (eds.), Handbook of Econometrics, Vol. I, Chapter 10. Amsterdam: North-Holland, 1983.Google Scholar
25.Judge, G.C., Griffiths, W.E., Hill, R.C., Luthepohl, H., & Lee, T.C.. The theory and practice of econometrics, 2nd Edition. New York: John Wiley & Sons, 1985.Google Scholar
26.Keynes, J.M.Professor Tinbergen's method. Economic Journal 49 (1939): 8–68.CrossRefGoogle Scholar
27.Koopmans, T.C.Linear regression analysis of economic time series. Netherlands Economic Institute (Haarlem), 1937.Google Scholar
28.Koopmans, T.C.Statistical estimation of simultaneous economic relations. Journal of the American Statistical Association 40 (1945): 448–466.CrossRefGoogle Scholar
29.Koopmans, T.C.Measurement without theory. Review of Economics and Statistics 29 (1947): 161–172.CrossRefGoogle Scholar
30.Koopmans, T.C.The econometric approach to business fluctuations. American Economic Review (Papers and proceedings) 39 (1949): 64–72.Google Scholar
31.Koopmans, T.C. (ed.). Statistical inference in dynamic economic models. Cowles Commission Monograph No. 10. New York: Wiley, 1950.Google Scholar
32.Koopmans, T.C., Rubin, H., & Leipnik, R.B.. Measuring the equation systems of dynamic economics. In Koopmans, T.C. (ed.), Statistical Inference in Dynamic Economic Models. New York: Wiley, 1950.Google Scholar
33.Koopmans, T.C. & Reiersol, O.. The identification of structural characteristics. Annals of Mathematical Statistics 21 (1950): 165–81.CrossRefGoogle Scholar
34.Learner, E.E.Specification searches: ad hoc inference with nonexperimental data. New York: John Wiley and Sons, 1978.Google Scholar
35.Lucas, R.E. & Rapping, L.A.. Real wages, employment and inflation. Journal of Political Economy 77 (1969): 721–754.CrossRefGoogle Scholar
36.Lucas, R.E. & Sargent, T.J.. Rational expectations and econometric practice. London: George Allen and Unwin, 1981.Google Scholar
37.Mann, H.B. & Wald, A.. On the statistical treatment of linear stochastic difference equations. Econometrica 11 (1943): 173–220.CrossRefGoogle Scholar
38.McAleer, M., Pagan, A.R., & Volker, P.A.. What will take the con out of econometrics. American Economic Review 75 (1985): 293–313.Google Scholar
39.Morgan, M.Errors in variables or in equations? a study in the history of econometric thought. Mimeo, London School of Economics, 1982.Google ScholarPubMed
40.Morgan, M. Haavelmo's probability revolution. In Kruger, L., Gingerhzer, G., & Morgan, M. (eds.), The Probabilistic Revolution, Vol. 2, Chapter 8. Cambridge, Massachusetts: MIT Press, 1987.Google Scholar
41.Naylor, T.H., Seaks, T.G., & Wichern, D.W.. Box-Jenkins methods: an alternative to econometric models. International Statistical Review 40 (1972): 123–137.CrossRefGoogle Scholar
42.Nicholls, D.F. & Pagan, A.R.. Varying coefficient regression. In Hannan, E.J., Krish-naiah, P.R., & Rao, M.M. (eds.), Handbook of Statistics, Vol. 5, pp. 413–449. Elsevier Science Publishers, 1985.Google Scholar
43.Pagan, A.R.Some identification and estimation results for regression models with stochastically varying coefficients. Journal of Economtrics 18 (1980): 251–261.Google Scholar
44.Pagan, A.R. Model evaluation by variable addition. In Hendry, D.F. & Wallis, K. (eds.), Econometrics and Quantitative Economics. Oxford: Blackwell, 1984.Google Scholar
45.Pagan, A.R.Time series behaviour and dynamic specification. Oxford Bulletin of Economics and Statistics 47 (1985): 199–211.CrossRefGoogle Scholar
46.Pagan, A.R.Three econometric methodologies: a critical appraisal. Journal of Economic Surveys 1 (1987): 3–24.CrossRefGoogle Scholar
47.Pagan, A.R. & Hall, A.D.. Diagnostic tests as residual analysis. Econometric Reviews 2 (1983): 159–218.CrossRefGoogle Scholar
48.Phillips, P.C.B.Understanding spurious regressions in econometrics. Journal of Econometrics 33 (1986): 311–340.CrossRefGoogle Scholar
49.Phillips, P.C.B.Time–series regression with a unit root. Econometrica 55 (1987): 277–301.CrossRefGoogle Scholar
50.Phillips, P.C.B.Optimal inference in cointegrated systems. Cowles Foundation Discussion Paper No. 866, Yale University, 1988.Google Scholar
51.Phillips, P.C.B.Reflections on econometric methodology. Cowles Foundation Discussion Paper No. 893, Yale University, 1988.CrossRefGoogle Scholar
52.Phillips, P.C.B. & Durlauf, S.N.. Multiple time-series regression with integrated processes. The Review of Economic Studies LIII (1986): 473–496.CrossRefGoogle Scholar
53.Sargan, J.D. Wages and prices in the U.K.; a study in econometric methodology. In Hart, P.E., Mills, G., & Whitaker, J.K. (eds.), Econometric Analysis for National Economic Planning. London: Butterworths, 1964.Google Scholar
54.Schultz, H.The theory and measurement of demand. Chicago, Illinois: University of Chicago Press, 1938.Google Scholar
55.Sims, C.A.Macroeconomics and reality. Econometrica 48 (1980): 1–48.CrossRefGoogle Scholar
56.Spanos, A.Instrumental variables and simultaneity: a finite sample interpretation. Mimeo, VPI and State University, 1986.Google Scholar
57.Spanos, A.Statistical foundations of econometric modeling. Cambridge: Cambridge University Press, 1986.CrossRefGoogle Scholar
58.Spanos, A.Error-autocorrelation revisited: the AR(1) case. Econometric Reviews 6 (1987): 285–294.CrossRefGoogle Scholar
59.Spanos, A.The early empirical findings on the consumption function: stylized facts or fiction. Oxford Economic Papers 41 (1989): 150–169.CrossRefGoogle Scholar
60.Spanos, A.Towards a unifying methodologicial framework for econometric modeling. Economic Notes (1988): 107–134.Google Scholar
61.Spanos, A.The student's t and elliptical linear regression models. Mimeo, VPI and State University, 1988.Google Scholar
62.Spanos, A. Econometric modeling with panel data: the role of statistical adequacy. Papers and Proceedings of the Conference on the A nalysis of the Company Gro wth, l'industria, (Italy) IV, Vol. 2, 1988.Google Scholar
63.Spanos, A. Unit roots and their dependence on the conditioning information set. Forthcoming in Advances in Econometrics (1989).Google Scholar
64.Spanos, A. The simultaneous equations model revisited: statistical adequacy and identification. Forthcoming in Journal of Econometrics (1990).Google Scholar
65.Stigler, G.J.The early history of empirical studies of consumer behavior. Journal of Political Economy 62 (1954): 95–113.CrossRefGoogle Scholar
66.Stigler, G.J.Henry L. Moore and statistical economics. Econometrica 30 (1962): 1–21.CrossRefGoogle Scholar
67.Stone, R.The measurement of consumers' expenditure and behavior in the United Kingdom 1920–1938. Cambridge: Cambridge University Press, 1954.Google Scholar
68.Tinbergen, J.Statistical testing of business cycle theories, vols I & II. Geneva: League of Nations, 1939.Google Scholar
69.Tinbergen, J.On a method of statistical business-cycle research: a reply. Economic Journal, 50 (1940): 141–154.CrossRefGoogle Scholar
70.Vining, R.Koopmans on the choice of variables to be studied and of methods of measurement. Review of Economics and Statistics 31 (1949): 77–94.CrossRefGoogle Scholar
71.Working, E.What do statistical demand curves show? Quarterly Journal of Economics 41 (1927): 212–235.CrossRefGoogle Scholar