Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least-squares estimation of cointegrating regressions between non-stationary and/or long memory stationary variables where the...

In this paper we analyse wine demand in Italy using microdata. Instead of estimating a traditional parametric model (like AIDS) we employed artificial neural networks (ANN) and evaluate the elasticities using two different methods, specific for the non parametric framework. We compared the...

In this paper, we present an analysis of farmed and wild fish in Italy using microdot. Instead of estimating a traditional parametric model such as AIDS, we employ Artificial Neural Networks (ANNs) and evaluate elasticities using a method specific of the non-parametric framework. As input...

Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least squares estimation of cointegrating regressions between nonstationary and/or long memory stationary variables where the...

In this paper, in order to investigate if a long memory model will provide good forecasts even if the real DGP is affected by level shifts (as suggested by Diebold, F.X., Inoue, A., 2001. Long memory and regime switching Journal of Econometrics, 105, 131-159) we compare via simulations the...

In this paper we present a spatial nonparametric analysis of local multipliers. Following Moretti (2010), we estimate the effect of an exogenous shock in the employment of the tradable sector on the employment in either the nontradable sector or the rest of the tradable sector using a...

In this paper we present a new procedure for nonparametric regression in case of spatially dependent data. In particular, we extend usual local linear regression (along the lines of Martins-Filho and Yao, 2009) and propose a two-step method where information on spatial dependence is incorporated...