The journal Computational Statistics and Data Analysis aims to have regular issues on computational econometrics. Of particular interest are papers in important areas of econometric applications where both computational techniques and numerical methods have a major impact. The goal is to provide sources of information about the most recent developments in computational econometrics that are currently scattered throughout publications in specialized areas.
Applied econometric practice is inherently computational, often substantially so. Existing algorithms, however, do not always embody the best of computational techniques, either for efficiency, stability, or conditioning. Likewise, environments for doing econometrics are inherently computer-based. Integrated packages for conducting econometrics have grown well over the years, but still leave much room for further development.
The first special issue dealing with computational econometrics [Belsley and Kontoghiorghes, 2003] featured articles examining filters, heuristic methods for estimation, MCMC, computational and numerical aspects for estimating large-scale models, and simulation methods, among other topics, indicated the importance of computing in econometrics, and highlighted research opportunities that exist in this discipline. The second special issue on computational econometrics [Belsley and Kontoghiorghes, 2005] considered papers addressing computational and numerical methods used in solving theoretical and practical issues associated with econometric algorithms, the impact of computing on econometrics, and specific applications involving computing and econometrics.
This third special issue comprises 14 studies that examine aspects of prediction, estimation and testing, applications of Monte Carlo and bootstrapping, and determinations of estimator distributions in numerous economic and financial contexts.
