We investigate identifiability issues in DSGE models and their consequences for parameter estimation and model evaluation when the objective function measures the distance between estimated and model impulse responses. We show that observational equivalence, partial and weak identification...

This paper proposes a worst-case approach for estimating econometric models containing unobservable variables. Worst-case estimators are robust against the averse effects of unobservables and, unlike the classical literature, there are no assumptions made about the statistical nature of the...

Fully specified DSGE models are increasingly successful in explaining observed macroeconomic data. Thinking about the specification of a certain equation in a DSGE approach has the drawback of imposing many implicit priors on the specification of the remaining equations. Mis-specifications in...

Neyman and Scott define the incidental-parameter problem. In panel data with $T$ observations per individual, the estimator of the common parameter is usually constistent with O(1/T). This paper shows that the integrated likelihood estimator becomes consistent with O(1/T^2) if an...

This paper develops a general method for conducting exact small-sample inference in models which allow the estimator of the (scalar) parameter of interest to be expressed as the root of an estimating function, and which is particularly simple to implement for linear models with a covariance...

In this paper we attempt to provide evidence on the structure of technology in the three industrial branches of French economy, when capital is treated as quasi-fixed input. However instead of choosing a single functional form as an approximation to the variable cost function, modified versions...

We present a multivariate generalization of the simple markov-switching model. We allow for the introduction of several latent processes that have a simple parametric distribution. The matrix-variate bernoulli distribution yields a flexible yet parsimonious pattern of dependence between the...

Non-linear modeling approaches, including Smooth Transition Autoregressive (STAR) models, have attracted a great deal of attention over the last two decades. The empirical application of these models, however, is not always a straightforward task. In particular, parameter estimation and...