Macroeconomics and Reality

Abstract

Existing strategies for econometric analysis related to macroeconomics are subject to a number of serious objections, some recently formulated, some old. These objections are summarized in this paper, and it is argued that taken together they make it unlikely that macroeconomic models are in fact over identified, as the existing statistical theory usually assumes. The implications of this conclusion are explored, and an example of econometric work in a non-standard style, taking account of the objections to the standard style, is presented.THE STUDY OF THE BUSINESS cycle, fluctuations in aggregate measures of economic activity and prices over periods from one to ten years or so, constitutes or motivates a large part of what we call macroeconomics. Most economists would agree that there are many macroeconomic variables whose cyclical fluctuations are of interest, and would agree further that fluctuations in these series are interrelated. It would seem to follow almost tautologically that statistical models involving large numbers of macroeconomic variables ought to be the arena within which macroeconomic theories confront reality and thereby each other.Instead, though large-scale statistical macroeconomic models exist and are by some criteria successful, a deep vein of skepticism about the value of these models runs through that part of the economics profession not actively engaged in constructing or using them. It is still rare for empirical research in macroeconomics to be planned and executed within the framework of one of the large models. In this lecture I intend to discuss some aspects of this situation, attempting both to offer some explanations and to suggest some means for improvement.I will argue that the style in which their builders construct claims for a connection between these models and reality-the style in which "identification" is achieved for these models-is inappropriate, to the point at which claims for identification in these models cannot be taken seriously. This is a venerable assertion; and there are some good old reasons for believing it;2 but there are also some reasons which have been more recently put forth. After developing the conclusion that the identification claimed for existing large-scale models is incredible, I will discuss what ought to be done in consequence. The line of argument is: large-scale models do perform useful forecasting and policy-analysis functions despite their incredible identification; the restrictions imposed in the usual style of identification are neither essential to constructing a model which can perform these functions nor innocuous; an alternative style of identification is available and practical.Finally we will look at some empirical work based on an alternative style of macroeconometrics. A six-variable dynamic system is estimated without using ' Research for this paper was supported by NSF Grant Soc-76-02482. Lars Hansen executed the computations. The paper has benefited from comments by many people, especially Thomas J. Sargent and Finn Kydland.T....

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“…References Generalized Method of Moments as employed (Oraboune, 2008;Seetanah, 2012) Three log forms model with Fixed/random effects techniques (Ravallion and Datt, 1996;Datt and Ravallion, 2002;Ghura et al, 2002) Simultaneous equations Fan et al (2000) Neo-classical production functions such as Cobb-Douglas or log linear production function Fan et al (2004); (Munnell, 1992;Gramlich, 1994;Sturm et al, 1998;Romp and De-Haan, 2005) Simultaneous Equations (A) The Human Capital Channel, (B) The Market Access Channel, and (C) The Labor Activities Channel (Mustajab, 2009;Gachassin et al, 2010) Panel data and Dynamic Panel Analysis Seeanah et al (2009);Wooldridge (2002) Vector Autoregression (VAR) and Vector Error Correction Models (VECM) Perron (1990); Toda and Phillips (1993;1994); Dufour and Renault (1998);Ramirez (2004);Lütkepohl (2005) Structural Vector Autoregressive (SVAR) Sims (1980a;1980b); Amisano and Giannini (1997); Arellano and Bover (1995); Saikkonen and Lütkepohl (2002); Sarte (1997);Ogun (2010) In this study the impact of road infrastructure on Malawi's poverty is assessed from a macroeconomic perspective. The lack of clear theoretical guidance on the choice of regressors, for the poverty equation, leads to a wide set of possible specifications and model uncertainty which in turn often results in contradictory conclusions.…”

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