Tractable Likelihood-Based Estimation of Non-Linear DSGE Models Using Higher-Order Approximations

Kollmann, Robert (2016): Tractable Likelihood-Based Estimation of Non-Linear DSGE Models Using Higher-Order Approximations.

Abstract

This paper discusses a tractable approach for computing the likelihood function of non-linear Dynamic Stochastic General Equilibrium (DSGE) models that are solved using second- and third order accurate approximations. By contrast to particle filters, no stochastic simulations are needed for the method here. The method here is, hence, much faster and it is thus suitable for the estimation of medium-scale models. The method assumes that the number of exogenous innovations equals the number of observables. Given an assumed vector of initial states, the exogenous innovations can thus recursively be inferred from the observables. This easily allows to compute the likelihood function. Initial states and model parameters are estimated by maximizing the likelihood function. Numerical examples suggest that the method provides reliable estimates of model parameters and of latent state variables, even for highly non-linear economies with big shocks.

• Item Type: MPRA Paper
• Language: English
• Keywords: Likelihood-based estimation of non-linear DSGE models, higher-order approximations, pruning, latent state variables
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles

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