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Structural Multi-Equation Macroeconomic Models: Identification-Robust Estimation and Fit

Author

Listed:
  • Jean-Marie Dufour
  • Lynda Khalaf
  • Maral Kichian

Abstract

Weak identification is likely to be prevalent in multi-equation macroeconomic models such as in dynamic stochastic general equilibrium setups. Identification difficulties cause the breakdown of standard asymptotic procedures, making inference unreliable. While the extensive econometric literature now includes a number of identification-robust methods that are valid regardless of the identification status of models, these are mostly limited-information-based approaches, and applications have accordingly been made on single-equation models such as the New Keynesian Phillips Curve. In this paper, we develop a set of identification-robust econometric tools that, regardless of the model's identification status, are useful for estimating and assessing the fit of a system of structural equations. In particular, we propose a vector auto-regression (VAR) based estimation and testing procedure that relies on inverting identification-robust multivariate statistics. The procedure is valid in the presence of endogeneity, structural constraints, identification difficulties, or any combination of these, and also provides summary measures of fit. Furthermore, it has the additional desirable features that it is robust to missing instruments, errors-in-variables, the specification of the data generating process, and the presence of contemporaneous correlation in the disturbances. We apply our methodology, using U.S. data, to the standard New Keynesian model such as the one studied in Clarida, Gali, and Gertler (1999). We find that, despite the presence of identification difficulties, our proposed method is able to shed some light on the fit of the considered model and, particularly, on the nature of the NKPC. Notably our results show that (i) confidence intervals obtained using our system-based approach are generally tighter than their single-equation counterparts, and thus are more informative, (ii) most model coefficients are significant at conventional levels, and (iii) the NKPC is preponderantly forward-looking, though not purely so.

Suggested Citation

  • Jean-Marie Dufour & Lynda Khalaf & Maral Kichian, 2009. "Structural Multi-Equation Macroeconomic Models: Identification-Robust Estimation and Fit," Staff Working Papers 09-19, Bank of Canada.
  • Handle: RePEc:bca:bocawp:09-19
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    Cited by:

    1. John J. Heim, 2016. "Do government stimulus programs have different effects in recessions, or by type of tax or spending program?," Empirical Economics, Springer, vol. 51(4), pages 1333-1368, December.
    2. Efrem Castelnuovo & Luca Fanelli, 2015. "Monetary Policy Indeterminacy and Identification Failures in the U.S.: Results from A Robust Test," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(6), pages 924-947, September.
    3. Zhongjun Qu, 2011. "Inference and Speci?cation Testing in DSGE Models with Possible Weak Identification," Boston University - Department of Economics - Working Papers Series WP2011-058, Boston University - Department of Economics.
    4. Dufour, Jean-Marie & Khalaf, Lynda & Kichian, Maral, 2010. "Estimation uncertainty in structural inflation models with real wage rigidities," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2554-2561, November.
    5. Fanelli, Luca, 2012. "Determinacy, indeterminacy and dynamic misspecification in linear rational expectations models," Journal of Econometrics, Elsevier, vol. 170(1), pages 153-163.

    More about this item

    Keywords

    Inflation and prices; Econometric and statistical methods;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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