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DSGE models are currently estimated with a two step approach: data is first filtered and then DSGE structural parameters are estimated. Two step procedures have problems, ranging from trend misspecification to wrong assumption about the correlation between trend and cycles.

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.

This article is related to the large recent literature on Phillips curves in sticky- price equilibrium models. It differs in allowing for the degree of price stickiness to be determined endogenously. A closed-form solution for short-term inflation is derived from the dynamic stochastic general equilibrium (DSGE) model with state-dependent pricing developed by Dotsey, King and Wolman.

Structural vector-autoregressions with long-run restrictions are extraordinarily sensitive to low-frequency correlations. This paper explores this sensitivity analytically and via simulations, focusing on the contentious issue of whether hours worked rise or fall when technology improves.

We use tests for multiple breaks at unknown points in the sample, and the Stock-Watson (1996, 1998) time-varying parameters median-unbiased estimation methodology, to investigate changes in the equilibrium rate of growth of labor productivity–both per hour and per worker–in the United States, the Eurozone Australia, and Japan over the post-WWII era. Results for the U.S.

Recent contributions have suggested that technology shocks have a negative impact on hours, contrary to the prediction of standard flexible-price models of the business cycle. Some authors have interpreted this finding as evidence in favour of sticky-price models, while others have either extended flexible-price models or disputed the empirical finding itself.

This paper studies procyclical productivity growth at the industry level in the U.S. and in three European countries (France, Germany and the Netherlands). Industry-specific demand-side instruments are used to examine the prevalence of non-constant returns to scale and unmeasured input utilization. For the aggregate U.S.

We carry out a detailed analysis of quarterly frequency dynamics in macroeconomic aggregates in twelve countries of Central and Eastern Europe. The facts we document include the variability and persistence in and the co-movement among output, and other major real and nominal variables. We find that consumption is highly volatile and government spending is procyclical.

This Paper examines the changing relationships between the G7 countries through VAR models for the quarterly growth rates, estimated both over sub-periods and using a rolling data window. Six trivariate models are estimated, all of which include the US and a European (E15) aggregate.

We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest estimating the appropriate grouping of time series simultaneously along with the group-specific model parameters. We cast estimation into the Bayesian framework and use Markov chain Monte Carlo simulation methods.