In this paper we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the...

This paper is concerned with efficient GMM estimation and inference in GARCH models. Sufficient conditions for the estimator to be consistent and asymptotically normal are established for the GARCH(1,1) conditional variance process. In addition efficiency results are obtained in the general...

This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling...

In this paper I define an evolutionary stability criterion for learning rules. Using Monte Carlo simulations, I then apply this criterion to a class of learning rules that can be represented by Camerer and Ho's (1999) model of learning. This class contains perturbed versions of reinforcement and...

The central bank's optimal reaction to foreign and domestic shocks is analyzed in an inflation targeting model allowing for incomplete exchange rate pass-through. Limited pass-through is incorporated through nominal rigidities in an aggregate supply-aggregate demand model derived from some...

The central bank's optimal objective function is analyzed in a small open economy model allowing for incomplete exchange rate pass-through. The results indicate that social welfare can only be marginally improved by including an explicit exchange-rate term in the delegated objective function,...

The performance of various monetary rules is investigated in an open economy with incomplete exchange rate pass-through. Implementing monetary policy through an exchange-rate augmented policy rule does not improve social welfare compared to using an optimized Taylor rule, irrespective of the...

We show how it is possible to generate multivariate data which have moments arbitrary close to the desired ones. They are generated as linear combinations of variables with known theoretical moments. It is shown how to derive the weights of the linear combinations in both the univariate and the...