Indirect estimation of stochastic differential equation models: some computational experiments
Computational Economics - Special issue: computational economics and statistics at the Certosa
Estimating binary multilevel models through indirect inference
Computational Statistics & Data Analysis
A global optimization heuristic for estimating agent based models
Computational Statistics & Data Analysis - Special issue: Computational econometrics
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Among the simulation-based methods, indirect estimation techniques like Indirect Inference (INDINF) and Efficient Method of Moments (EMM) provide a simple solution to many computational problems associated with intractable Likelihood functions. Optimisation of the objective function can be critical in presence of not continuous response variables like, for instance, binary choice or discrete choice models, limited dependent variables, switching regime models. In particular, gradient-based optimisation algorithms can face difficulties when the not continuous response involves discontinuities in the objective function. A simple computational tool is suggested to ''empirically'' solve the problem. The case study is EMM applied to the autoregressive model with exponential marginal distribution (EAR). The proposed solution is also compared with the performance of the Conditional Least Squares estimation, suitable for this autoregressive model, by a set of Monte Carlo experiments.