Real Business Cycles: A Reader

By James E. Hartley; Kevin D. Hoover et al. | Go to book overview

CHAPTER 18

JOURNAL OF APPLIED ECONOMETRICS, VOL. 9, S123-S144 (1994)

STATISTICAL INFERENCE IN CALIBRATED MODELS

FABIO CANOVA

Department of Economics, Universitat Pompeu Fabra, Balmes 132, 08008 Barcelona, Spain and Department of Economics, Università di Catania, 95100 Catania, Italy, and CEPR


SUMMARY

This paper describes a Monte Carlo procedure to assess the performance of calibrated dynamic general equilibrium models. The procedure formalizes the choice of parameters and the evaluation of the model and provides an efficient way to conduct a sensitivity analysis for perturbations of the parameters within a reasonable range. As an illustration the methodology is applied to two problems: the equity premium puzzle and how much of the variance of actual US output is explained by a real business cycle model.


1. INTRODUCTION

The current macroeconometrics literature has proposed two ways to confront general equilibrium rational expectations models with data. The first, an estimation approach, is the direct descendant of the econometric methodology proposed 50 years ago by Haavelmo (1944). The second, a calibration approach, finds its justification in the work of Frisch (1933) and is closely linked to the computable general equilibrium literature surveyed e.g. in Shoven and Whalley (1984).

The two methodologies share the same strategy in terms of model specification and solution. Both approaches start from formulating a fully specified general equilibrium dynamic model and in selecting convenient functional forms for preferences, technology, and exogenous driving forces. They then proceed to find a decision rule for the endogenous variables in terms of the exogenous and predetermined variables (the states) and the parameters. When the model is nonlinear, closed-form expressions for the decision rules may not exist and both approaches rely on recent advantages in numerical methods to find an approximate solution which is valid either locally or globally (see e.g. the January 1990 issue of the Journal of Business and Economic Statistics for a survey of the methods and Christiano, 1990, and Dotsey and Mao, 1991, for a comparison of the accuracy of the approximations).

It is when it comes to choosing the parameters to be used in the simulations and in evaluating the performance of the model that several differences emerge. The first procedure attempts to find the parameters of the decision rule that best fit the data either by maximum likelihood (ML) (see e.g. Hansen and Sargent, 1979, or Altug, 1989) or generalized method of moments (GMM) (see e.g. Hansen and Singleton, 1983, or Burnside et al., 1993). The validity of the specification is examined by testing restrictions, by general goodness of fit tests or by comparing the fit of two nested models. The second approach ‘calibrates’ parameters using a set of alternative rules which includes matching long-run averages, using previous microevidence or a priori selection, and assesses the fit of the model with an informal distance criterion.

These differences are tightly linked to the questions the two approaches ask. Roughly speaking, the estimation approach asks the question ‘Given that the model is true, how false is

CCC 0883-7252/94/0S0S123-22

Received July 1992

© 1994 by John Wiley & Sons, Ltd.

Revised August 1994

-333-

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Real Business Cycles: A Reader
Table of contents

Table of contents

  • Title Page iii
  • Contents vii
  • Acknowledgements xi
  • Part I - Introduction 1
  • Chapter 1 - The Limits of Business Cycle Research 3
  • Notes 34
  • Chapter 2 - A User's Guide to Solving Real Business Cycle Models 43
  • Part II - The Foundations of Real Business Cycle Modeling 55
  • Chapter 3 57
  • Chapter 4 83
  • References 96
  • Chapter 5 97
  • Chapter 6 102
  • Chapter 7 108
  • Part III - Some Extensions 147
  • Chapter 8 149
  • Chapter 9 168
  • References 178
  • Chapter 10 - Current Real-Business-Cycle Theories and Aggregate Labor-Market Fluctuations 179
  • Chapter 11 - The Inflation Tax in a Real Business Cycle Model 200
  • Part IV - The Methodology of Equilibrium Business Cycle Models 217
  • Chapter 12 219
  • Chapter 13 237
  • Chapter 14 254
  • Chapter 15 272
  • Part V - The Critique of Calibration Methods 293
  • Chapter 16 295
  • Chapter 17 - Measures of Fit for Calibrated Models 302
  • Chapter 18 333
  • Chapter 19 355
  • Part VI - Testing the Real Business Cycle Model 381
  • Chapter 20 - Business Cycles: Real Facts and a Monetary Myth 383
  • References 398
  • Chapter 21 399
  • Chapter 22 - Evaluating a Real Business Cycle Model 431
  • Chapter 23 462
  • Chapter 24 496
  • Chapter 25 513
  • Chapter 26 - Did Technology Shocks Cause the 1990-1991 Recession? 533
  • Part VII - The Solow Residual 541
  • Chapter 27 - Technical Change and the Aggregate Production Function 543
  • Chapter 28 552
  • Chapter 29 564
  • Chapter 30 - Output Dynamics in Real-Business-Cycle Models 571
  • Part VIII - Filtering and Detrending 591
  • Chapter 31 - Postwar U. S. Business Cycles: an Empirical Investigation 593
  • Chapter 32 609
  • Chapter 33 626
  • Index 652
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