The New-Keynesian Phillips curve has recently become an important ingredient in monetary policy models. However, using limited information methods, the empirical support for the New-Keynesian Phillips curve appear to be mixed. This paper argues, by means of Monte Carlo simulations with a simple...

We propose non-nested tests for competing conditional moment restriction models using a method of empirical likelihood. Our tests are based on the method of conditional empirical likelihood developed by Kitamura, Tripathi and Ahn (2004) and Zhang and Gijbels (2003). By using the conditional...

The stochastic frontier analysis (Aigner et al., 1977, Meeusen and van de Broeck, 1977) is widely used to estimate individual efficiency scores. The basic idea lies in the introduction of an additive error term consisting of a noise and an inefficiency term. Most often the assumption of a...

We compare the performance of maximum likelihood (ML) and simulated method of moments (SMM) estimation for dynamic discrete choice models. We construct and estimate a simplified dynamic structural model of education that captures some basic features of educational choices in the United States in...

Particle Markov Chain Monte Carlo (PMCMC) is a widely used method to handle estimation problem in the context of nonlinear structural dynamic models whose likelihood function is analytically intractable. PMCMC can be constructed upon a GMM likelihood representation when one does not want to rely...

Particle Markov Chain Monte Carlo (PMCMC) is a widely used method to handle estimation problem in the context of nonlinear structural dynamic models whose likelihood function is analytically intractable. PMCMC can be constructed upon a GMM likelihood representation when one does not want to rely...

We propose a new class of models specifi cally tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models...