Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-01T23:01:10.246Z Has data issue: false hasContentIssue false
  • English
  • Français

TRYGVE HAAVELMO’S EXPERIMENTAL METHODOLOGY AND SCENARIO ANALYSIS IN A COINTEGRATED VECTOR AUTOREGRESSION

Published online by Cambridge University Press:  31 July 2014

Kevin Hoover  and
Katarina Juselius
Kevin Hoover*
Affiliation:
Duke University
Katarina Juselius
Affiliation:
University of Copenhagen
*
*Address correspondence to Kevin D. Hoover, Department of Economics and Department of Philosophy, Duke University, Box 90097, Durham, NC, USA 27708-0097; e-mail: kd.hoover@duke.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

The paper provides a careful, analytical account of Trygve Haavelmo’s use of the analogy between controlled experiments common in the natural sciences and econometric techniques. The experimental analogy forms the linchpin of the methodology for passive observation that he develops in his famous monograph, The Probability Approach in Econometrics (1944). Contrary to some recent interpretations of Haavelmo’s method, the experimental analogy does not commit Haavelmo to a strong apriorism in which econometrics can only test and reject theoretical hypotheses, rather it supports the acquisition of knowledge through a two-way exchange between theory and empirical evidence. Once the details of the analogy are systematically understood, the experimental analogy can be used to shed light on theory-consistent cointegrated vector autoregression (CVAR) scenario analyses. A CVAR scenario analysis can be interpreted as a clear example of Haavelmo’s ‘experimental’ approach; and, in turn, it can be shown to extend and develop Haavelmo’s methodology and to address issues that Haavelmo regarded as unresolved.

Type
ARTICLES
Information
Econometric Theory , Volume 31 , Issue 2: Haavelmo Memorial Issue: Part Two , April 2015 , pp. 249 - 274
Copyright
Copyright © Cambridge University Press 2014 

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

Aldrich, J. (1989) Autonomy. Oxford Economic Papers N.S. 41(1), 15–34.CrossRefGoogle Scholar
Angrist, J.D. & Pischke, J.-S. (2010) The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives 24(2), 3–30.Google Scholar
Boumans, M. (2005) How Economists Model the World into Numbers. Routledge.Google Scholar
Boumans, M. (2012) Haavelmo’s Methodology of Empirical Science Outside the Laboratory. Unpublished typescript, University of Amsterdam.Google Scholar
Congressional Budget Office (1995) CBO’s Method for Estimating Potential Output. CBO Memorandum, October 1995. Available fromftp://ftp.cbo.gov/3xx/doc312/potout.pdf.Google Scholar
Duarte, P.G. & Hoover, K.D. (2012) Observing shocks. In Maas, H. & Morgan, M. (eds.), Histories of Observation in Economics. Supplement to History of Political Economy 44(5), 226–249.Google Scholar
Eichenbaum, M. (1995) Some comments on the role of econometrics in economic theory. The Economic Journal 105(433), 1609–1621.Google Scholar
Evans, G.W. & Honkapohja, S. (2001) Learning and Expectations in Macroeconomics. Princeton University Press.Google Scholar
Fisher, R.A. (1935) The Design of Experiments. Oliver and Boyd.Google Scholar
Friedman, M. (1956) The quantity theory of money: A restatement. In Friedman, M. (ed.), Studies in the Quantity Theory of Money. University of Chicago Press.Google Scholar
Friedman, M. (1969) The optimal quantity of money. In The Optimal Quantity of Money and Other Essays. Macmillan.Google Scholar
Friedman, M. (1974) A theoretical framework for monetary analysis. In Gordon, R.J. (ed.), Milton Friedman’s Monetary Framework. University of Chicago Press.Google Scholar
Frisch, R. (1932) New orientation of economic theory. Economics as an experimental science. Nordic Statistical Journal 4, 97–111.Google Scholar
Frisch, R. (1938) Statistical Versus Theoretical Relations in Economic Macrodynamics. Unpublished memorandum, University of Oslo. Downloadable fromhttp://www.sv.uio.no/econ/om/tall-og-fakta/nobelprisvinnere/ragnar-frisch/published-scientific-work/rf-published-scientific-works/rf1938e.pdf.Google Scholar
Frydman, R. & Goldberg, M. (2007) Imperfect Knowledge Economics: Exchange Rates and Risk. Princeton University Press.Google Scholar
Frydman, R. & Goldberg, M. (2011) Beyond Mechanical Markets: Risk and the Role of Asset Price Swings. Princeton University Press.Google Scholar
Giese, J.V. (2008) Level, slope, curvature: Characterizing the yield curve in a cointegrated VAR model. Economics: The Open-Access, Open-Assessment E-Journal 2, 28. Downloadable fromhttp://dx.doi.org/10.5018/economics-ejournal.ja.2008-28.Google Scholar
Haavelmo, T. (1943) The statistical implications of a system of simultaneous equations. Econometrica 11(1), 1–12.Google Scholar
Haavelmo, T. (1944) The probability approach to econometrics. Econometrica 12(Supplement), iii-118.Google Scholar
Haavelmo, T. (1950) Remarks on Frisch’s confluence analysis and its use in econometrics. In Koopmans, T.C. (ed.), Statistical Inference in Dynamic Economic Models, pp. 258–265. Cowles Commission Monograph, vol. 10. Wiley.Google Scholar
Haavelmo, T. (1954) Structural Models and Econometrics. Unpublished typescript, University of Oslo. Downloadable fromhttp://www.sv.uio.no/econ/english/research/networks/haavelmo-network/publications/files/th1955b-es-1954-uppsala.pdf.Google Scholar
Heckman, J.J. (1992) Haavelmo and the birth of modern econometrics: A review of the history of econometric ideas by M. Morgan. Journal of Economic Literature 30(2), 876–886.Google Scholar
Heckman, J.J. (2000) Causal parameters and policy analysis in economics: A twentieth century perspective. Quarterly Journal of Economics 115(1), 45–97.Google Scholar
Hendry, D.F. & Mizon, G.E. (2010) On the Mathematical Basis of Inter-temporal Optimization. Discussion paper, 497, University of Oxford, Economics Department.Google Scholar
Hendry, D.F., Spanos, A., & Ericsson, N.R. (1989) The contributions to econometrics in Trygve Haavelmo. Sosialøkonomen no. 11, 12–17.Google Scholar
Hoover, K.D. (1994) Econometrics as observation: The Lucas critique, causality and the nature of econometric inference. Journal of Economic Methodology 1(1), 65–80.Google Scholar
Hoover, K.D. (2012a) Pragmatism, perspectival realism, and econometrics. In Lehtinen, A., Kuorikoski, J., & Ylikoski, P. (eds.), Economics for Real: Uskali Mäki and the Place of Truth in Economics, pp. 223–240. Routledge.Google Scholar
Hoover, K.D. (2012b) Applied Intermediate Macroeconomics. Cambridge University Press.Google Scholar
Hoover, K.D. (2014) On the reception of Haavelmo’s econometric thought. Journal of the History of Economic Thought 36, 45–65.Google Scholar
Hoover, K.D., Johansen, S., & Juselius, K. (2008) Allowing the data to speak freely: The macroeconometrics of the cointegrated vector autoregression. American Economic Review 98(2), 251–255.Google Scholar
Johansen, S. (1995) Identifying restrictions of linear equations. With applications to simultaneous equations and cointegration. Journal of Econometrics 69(1), 111–132.Google Scholar
Juselius, K. (1993) VAR modelling and Haavelmo’s probability approach to macroeconomic modelling. Empirical Economics 18, 595–622.Google Scholar
Juselius, K. (1999) Models and relations in economics and econometrics. Journal of Economic Methodology 6(2), 259–290.CrossRefGoogle Scholar
Juselius, K. (2006) The Cointegrated VAR Model. Methodology and Applications. Oxford University Press.Google Scholar
Juselius, K. (2012) Testing the Rational Expectations Hypothesis Against the Imperfect Knowledge Hypothesis in a Model for Foreign Exchange: A Scenario Analysis. Working paper, Department of Economics, University of Copenhagen. Available fromhttp://www.econ.ku.dk/okokj/papers/RE_IKEscenarios.pdf.Google Scholar
Juselius, K. (2014) Haavelmo’s probability approach and the cointegrated VAR. Econometric Theory, doi:10.1017/S0266466614000279.Google Scholar
Juselius, K. & Franchi, M. (2007) Taking a DSGE model to the data meaningfully. Economics – The Open-Access, Open-Assessment E-Journal no. 4. Available fromhttp://www.economics-ejournal.org/economics/journalarticles/2007-4.Google Scholar
Juselius, K. & Johansen, S. (2006) Extracting information from the data: A European view on empirical macro. In Colander, D. (ed.), Post Walrasian Macroeconomics: Beyond the Dynamic Stochastic General Equilibrium Model, pp. 301–334. Cambridge University Press.Google Scholar
Keynes, J.M. (1936) The General Theory of Employment Interest and Money. Macmillan.Google Scholar
Kongsted, H.C. (2005) Testing the nominal-to-real transformation. Journal of Econometrics 124(2), 202–225.Google Scholar
Kydland, F.E. & Prescott, E.C. (1996) The computational experiment: An econometric tool. Journal of Economic Perspectives 10(1), 69–85.Google Scholar
Lucas, R.E. Jr. (1976) Econometric policy evaluation: A critique. Reprinted in Lucas (1981, pp. 104–130).Google Scholar
Marschak, J. (1953) Economic measurements for policy and prediction. In Hood, W.C. & Koopmans, T. (eds.), Studies in Econometric Method, pp. 1–26. Cowles Commission Monograph, vol. 14. Wiley.Google Scholar
Morgan, M.S. (1990) The History of Econometric Ideas. Cambridge University Press.Google Scholar
Russell, B. (1927) The Analysis of Matter. Harcourt, Brace.Google Scholar
Sargent, T.J. (1987) Dynamic Macroeconomics. Harvard University Press.Google Scholar
Sargent, T.J. (1994) Bounded Rationality in Macroeconomics: The Arne Ryde Memorial Lectures. Oxford University Press.Google Scholar
Sargent, T.J. & Lundqvist, L. (2004) Recursive Macroeconomic Theory, 2nd ed. MIT Press.Google Scholar