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HAAVELMO’S PROBABILITY APPROACH AND THE COINTEGRATED VAR

Published online by Cambridge University Press:  08 July 2014

Katarina Juselius*
Affiliation:
University of Copenhagen
*
*Address correspondence to Katarina Juselius, Department of Economics, University of Copenhagen, ∅ster Farimasgade 5, 1353 Copenhagen K, Denmark; e-mail: [email protected].

Abstract

Some key econometric concepts and problems of great importance to Trygve Haavelmo and Ragnar Frisch are discussed within the general framework of a cointegrated VAR. The focus is on problems typical of time-series data such as multicollinearity, spurious correlation and regression, time dependent residuals, model selection, missing simultaneity, autonomy, and identification. The paper argues that the more recent development of unit root econometrics has been instrumental for a solution to the above problems.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Aldrich, J. (1989) Autonomy. Oxford Economic Papers, New Series 41(1), 1534. History and Methodology of Econometrics.Google Scholar
Andersen, T.W. (1991) Trygve Haavelmo and simultaneous equation models. Scandinavian Journal of Statistics 18, 119.Google Scholar
Bjerkholt, O. (2011) Trygve Haavelmo, Ragnar Frisch and the Probability Approach. The International Journal of Applied Economics and Econometrics 21(3), 96117.Google Scholar
Castle, J.L., Fawcett, N.W.P., & Hendry, D.F. (2010) Forecasting with equilibrium-correction models during structural breaks. Journal of Econometrics 158, 2538.Google Scholar
Clements, M.P. & Hendry, D.F. (1999) Forecasting Non-stationary Economic Time Series. MIT Press.Google Scholar
Davidson, J. (1988) Structural relations, cointegration and identification: Some simple results and their application. Journal of Econometrics 87(1), 87113.Google Scholar
Dennis, J.G., Hansen, H., Johansen, S., & Juselius, K. (2006) CATS in RATS. Cointegration Analysis of Time Series. Version 2. Estima.Google Scholar
Doornik, J.A. & Hendry, D.F. (2006) Empirical Econometric Modelling Using PcGive, vol. I–III. Timberlake Consultants Press.Google Scholar
Engle, R.F., Hendry, D.F., & Richard, J.-F. (1983) Exogeneity. Econometrica 51(2), 277304.Google Scholar
Frisch, R. (1934) Statistical Confluence Analysis by Means of Complete Regression Systems, pp. 5–8. University Institute of Economics.Google Scholar
Frisch, R. (1938) Statistical Versus Theoretical Relations in Economic Macrodynamics. Unpublished Memorandum, University of Oslo.Google Scholar
Frisch, R. & Waugh, F. (1933) Partial time regressions as compared with individual trends. Econometrica 1(4), 387401.Google Scholar
Haavelmo, T. (1943) The statistical implications of a system of simultaneous equations. Econometrica 11, 112.CrossRefGoogle Scholar
Haavelmo, T. (1944) The probability approach to econometrics. Econometrica 12(Supplement),1118.Google Scholar
Haavelmo, T. (1954) Structural Models and Econometrics. Unpublished paper presented at the Econometric Society Meeting 1954 in Stockholm. Available athttp://www.sv.uio.no/econ/english/research/networks/haavelmo-network/publications/files/th1955b-es-1954-uppsala.pdfGoogle Scholar
Haavelmo, T. & Staehle, H. (1941) The Elements of Frisch’s Confluence Analaysis. Hecto., Harvard University.Google Scholar
Hansen, H. & Johansen, S. (1999) Some tests for parameter constancy in cointegrated VAR models. The Econometrics Journal 2(2), 306333.Google Scholar
Hendry, D.F. & Mizon, G.E. (1993) Evaluating econometric models by encompassing the VAR. In Phillips, P.C. (ed.), Models, Methods and Applications of Econometrics, pp. 272300. Blackwell.Google Scholar
Hendry, D.F. & Morgan, M.S. (1989) A re-analysis of confluence analysis. Oxford Economic Papers 41(1), 3552. History and Methodology of Econometrics.Google Scholar
Hendry, D.F. & Morgan, M.S. (1995) The Foundations of Econometric Analysis. Cambridge University Press.Google Scholar
Hendry, D.F. & Richard, J.F. (1983) The econometric analysis of economic time-series (with discussion). International Statistical Review 51, 111163.Google Scholar
Hendry, D.F., Spanos, A., & Ericsson, N.R. (1989) The contributions to econometrics in Trygve Haavelmo’s ‘The Probability Approach in Econometrics’. Sosialøkonomen 43(11), 1217.Google Scholar
Hoover, K.D. (2012) The Role of Hypothesis Testing in the Molding of Econometric Models. CHOPE Working paper no. 2012-03. Available at SSRN: http://ssrn.com/abstract=2001481.Google Scholar
Hoover, K.D., Johansen, S., & Juselius, K. (2009) Allowing the data to speak freely: The macroeconometrics of the cointegrated vector autoregression. American Economic Review 98, 251255.Google Scholar
Hoover, K.D. & Juselius, K. (2012) Experiments, Passive Observation and Scenario Analysis: Trygve Haavelmo and the Cointegrated Vector Autoregression. University of Copenhagen Department of Economics Discussion paper no. 12-16. Available at SSRN: http://ssrn.com/abstract=2171700.Google Scholar
Johansen, S. (1988) Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12(213), 231254.Google Scholar
Johansen, S. (1995) Identifying restrictions of linear equations. With applications to simultaneous equations and cointegration. Journal of Econometrics 69(1), 111132.Google Scholar
Johansen, S. (1996) Likelihood-Based Inference in Cointegrated Vector Autoregressive Models.Oxford University Press.Google Scholar
Johansen, S. (2012) The analysis of nonstationary time series using regression, correlation and cointegration. Contemporary Economics 6, 4057.Google Scholar
Johansen, S. & Juselius, K. (1994) Identification of the long-run and short-run structure: An application to the ISLM model. Journal of Econometrics 63, 736.Google Scholar
Juselius, K. (2006) The Cointegrated VAR Model: Methodology and Applications. Oxford University Press.Google Scholar
Juselius, K. & Juselius, M. (2014) Balance sheet recessions and time-varying coefficients in a Phillips curve relationship: An application to Finnish data. In Haldrup, N., Meitz, M., & Saikkonen, P. (eds.), Essays in Nonlinear Time Series Econometrics: Festschrift in Honour of Timo Teräsvirta. Oxford University Press. Forthcoming.Google Scholar
Koopmans, T.C., Rubin, H., & Leipnik, R.B. (1950) Measuring the equation systems of dynamic economics. In Koopmans, T.C. (ed.), Statistical Inference in Dynamic Economic Models. Cowles Commission Research. John Wiley & Sons, Inc.Google Scholar
Lehfeldt, R.A. (1914) The elasticity of the demand for wheat. The Economic Journal 24, 212217.CrossRefGoogle Scholar
Morgan, M.S. (1990) The History of Econometric Ideas. Cambridge University Press.Google Scholar
Phelps, E. (1994) Structural Slumps. Princeton University Press.Google Scholar
Phillips, P.C.B. (1986) Understanding spurious regressions in econometrics. Journal of Econometrics 33, 311340.Google Scholar
Phillips, P.C.B. (1987) Time series regression with a unit root. Econometrica 55, 2773011.Google Scholar
Phillips, P.C.B. & Durlauf, S.N. (1986) Multiple time series regression with integrated processes. Review of Economic Studies 53, 473495.Google Scholar
Stock, J.H. (1987) Asymptotic properties of least squares estimators of cointegrating vectors.Econometrica 55, 10351056.CrossRefGoogle Scholar
Wald, A. (1950) A note on the identification of economic relations. In Koopmans, T.C. (ed.), Statistical Inference in Dynamic Economic Models. Cowles Commission Research.Google Scholar
Yule, U. (1926) Why do we sometimes get nonsense-correlations between time series? – A study in sampling and the nature of time series. Journal of the Royal Statistical Society 89, 163.Google Scholar