Penalized indirect inference

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Parameter estimates of structural economic models are often difficult to interpret at the light of the underlying economic theory. Bayesian methods have become increasingly popular as a tool for conducting inference on structural models since priors offer a way to exert control over the estimation results. Similarly to Bayesian estimation, this paper proposes a penalized indirect inference estimator that allows researchers to obtain economically meaningful parameter estimates in a frequentist setting. The asymptotic properties of the estimator are established for both correctly and incorrectly specified models, as well as under strong and weak parameter identification. A Monte Carlo study reveals the role of the penalty function in shaping the finite sample distribution of the estimator. The advantages of using this estimator are highlighted in the empirical study of a state-of-the-art dynamic stochastic general equilibrium model.

Original languageEnglish
Pages (from-to)34-54
Number of pages21
JournalJournal of Econometrics
Volume205
Issue number1
DOIs
Publication statusPublished - Jul 2018

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Indirect Inference
Estimator
Structural Model
Economics
General Equilibrium
Economic Model
Equilibrium Model
Bayesian Estimation
Stochastic Dynamics
Parameter Identification
Penalty Function
Bayesian Methods
Monte Carlo Study
Estimate
Empirical Study
Asymptotic Properties
Identification (control systems)
Indirect inference

Keywords

  • DSGE
  • Indirect inference
  • Penalized estimation
  • Simulation-based methods

Cite this

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title = "Penalized indirect inference",
abstract = "Parameter estimates of structural economic models are often difficult to interpret at the light of the underlying economic theory. Bayesian methods have become increasingly popular as a tool for conducting inference on structural models since priors offer a way to exert control over the estimation results. Similarly to Bayesian estimation, this paper proposes a penalized indirect inference estimator that allows researchers to obtain economically meaningful parameter estimates in a frequentist setting. The asymptotic properties of the estimator are established for both correctly and incorrectly specified models, as well as under strong and weak parameter identification. A Monte Carlo study reveals the role of the penalty function in shaping the finite sample distribution of the estimator. The advantages of using this estimator are highlighted in the empirical study of a state-of-the-art dynamic stochastic general equilibrium model.",
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Penalized indirect inference. / Blasques, Francisco; Duplinskiy, Artem.

In: Journal of Econometrics, Vol. 205, No. 1, 07.2018, p. 34-54.

Research output: Contribution to JournalArticleAcademicpeer-review

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AU - Blasques, Francisco

AU - Duplinskiy, Artem

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AB - Parameter estimates of structural economic models are often difficult to interpret at the light of the underlying economic theory. Bayesian methods have become increasingly popular as a tool for conducting inference on structural models since priors offer a way to exert control over the estimation results. Similarly to Bayesian estimation, this paper proposes a penalized indirect inference estimator that allows researchers to obtain economically meaningful parameter estimates in a frequentist setting. The asymptotic properties of the estimator are established for both correctly and incorrectly specified models, as well as under strong and weak parameter identification. A Monte Carlo study reveals the role of the penalty function in shaping the finite sample distribution of the estimator. The advantages of using this estimator are highlighted in the empirical study of a state-of-the-art dynamic stochastic general equilibrium model.

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