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Likelihood ratio testing in linear state space models: An application to dynamic stochastic general equilibrium models

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  • Komunjer, Ivana
  • Zhu, Yinchu

Abstract

This paper considers the problem of hypothesis testing in linear Gaussian state space models. We consider two hypotheses of interest: a simple null and a hypothesis of explicit parameter restrictions. We derive the asymptotic distributions of the corresponding likelihood ratio test statistics and compute the Bartlett adjustments. The results are non-trivial because the unrestricted state space model is not (even locally) identified. We apply our analysis to test the validity of the Dynamic Stochastic General Equilibrium (DSGE) models. A Monte Carlo exercise illustrates our findings and confirms the importance of Bartlett corrections at sample sizes typically encountered in macroeconomics.

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  • Komunjer, Ivana & Zhu, Yinchu, 2020. "Likelihood ratio testing in linear state space models: An application to dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 218(2), pages 561-586.
    • Handle: RePEc:eee:econom:v:218:y:2020:i:2:p:561-586
    DOI: 10.1016/j.jeconom.2020.04.029
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    More about this item

    Keywords

    Linear Gaussian state space models; Likelihood ratio test; Bartlett adjustment;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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