Indirect Inference (I‐I) estimation of structural parameters θ requires matching observed and simulated statistics, which are most often generated using an auxiliary model that depends on instrumental parameters β. The estimators of the instrumental parameters will encapsulate the...

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the...

I structurally estimate an incomplete markets life-cycle model with endogenous labor supply using data on the joint distribution of wages, hours, and consumption. The model is successful at matching the evolution of both the first and second moments of the data over the life cycle. The key...

We present a fast and accurate computational method for solving and estimating a class of dynamic programming models with discrete and continuous choice variables. The solution method we develop for structural estimation extends the en- dogenous grid-point method (EGM) to discrete-continuous...

In this study, we developed an inference procedure for the neural network using the bootstrap approach, and applied it to the market efficiency of the Nigerian exchange rate. Data used are exchange rate values from 2001 to 2015. We conducted a test on the market efficiency hypothesis, including...

Future market risk has always been a critical question in decision support processes. FORESIM is a simulation technique that models shipping markets (developed recently). In this paper we present the application of this technique in order to obtain useful information regarding future values of...

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in...

This paper offers an exhaustive analysis of the effectiveness of several models and methodologies that are commonly used to forecast financial failure: Linear, MDA, Logit, and artificial neural network. Our main aim is to evaluate their relative strengths and weaknesses, in terms of technical...