Euro Area Inflation Persistence in an Estimated Nonlinear DSGE Model

By Gianni Amisano, O. Tristani. Social Science Research Network, 2008

159 citations recorded by the source corpus.

En We estimate the approximate nonlinear solution of a small DSGE model on euro area data, using the conditional particle filter to compute the model likelihood. Our results are consistent with previous findings, based on simulated data, suggesting that this approach delivers sharper inference compared to the estimation of the linearised model. We also show that the nonlinear model can account for richer economic dynamics: the impulse responses to structural shocks vary depending on initial conditions selected within our estimation sample.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/6cf2faf1603435bc9d4c743bc6ebc2b6b224df02

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In Economics and Econometrics

Citations in the corpus

Estimating Macroeconomic Models: A Likelihood Approach (2007, unknown): “The use of simulation techniques for non-linear filtering can be traced back at least to Handschin and Mayne (1969).”
Estimation of ergodic agent-based models by simulated minimum distance (2015, methodology): “Particle filtering has found only a limited application to DSGE modeling (Amisano and Tristani, 2010; Andreasen, 2011; Fasolo, 2012), while no applications exist to AB models.17 Alternatively, one could think of using simulated empirical maximum likelihood to approximate the likelihood function, as ”
Agent based-stock flow consistent macroeconomics: Towards a benchmark model (2016, background): “Furthermore, since the quality of the log-linearized approximation deteriorates as we move away from the steady state (Amisano and Tristani, 2007; Brunnermeier and Sannikov, 2014), this hinders its efficacy in assessing the consequences of big shocks.”