While model selection is viewed as a fundamental task in data analysis, it imposesconsiderable effects on the subsequent inference. In applied statistics, it is common tocarry out a data-driven approach in model selection and draw inference conditional on theselected model, as if it is given a...

In latent growth modeling (LGM), model selection and inference are treated as separate stages of data analysis, but they are generally conducted on the assumption that the model is known a priori and thus model selection and inference are performed on the same data set. This two-step process...

Using a model selection approach, this thesis proposes a constructive data-and-theory-combined procedure to identify model structures in the framework of a linear simultaneous equations system based on observed data. A model structure is characterized by restrictions on the structural...

University of Minnesota Ph.D. dissertation. November 2010. Major: Statistics. Advisor: Dr. Yuhong Yang. 1 computer file (PDF); viii, 86 pages.

This paper provides an empirical comparison of various selection and penalized regression approaches for forecasting with vector autoregressive systems. In particular, we investigate the effect of the system size as well as the effect of various prior specification choices on the relative and...

The recent increase in the breath of computational methodologies has been matched with a corresponding increase in the difficulty of comparing the relative explanatory power of models from different methodological lineages. In order to help address this problem a universal information criterion...

The present paper aims to test a new model comparison methodology by calibrating and comparing three agent-based models of financial markets on the daily returns of 18 indices. The models chosen for this empirical application are the herding model of Gilli & Winker, its asymmetric version by...

The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations for finding the confidence set use an elimination approach where one starts with the full collection of models and successively eliminates the...

Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised...