Several principles are useful for econometric forecasters: keep the model simple, use all the data you can get, and use theory (not the data) as a guide to selecting causal variables. But theory gives little guidance on dynamics, that is, on which lagged values of the selected variables to use. Early econometric models failed in comparison with extrapolative methods because they paid too little...

This chapter explains the logic of structural econometric models and compares them to other types of econometric models. We provide a framework researchers can use to develop and evaluate structural econometric models. This framework pays particular attention to describing different sources of unobservables in structural models. We use our framework to evaluate several literatures in industrial...

A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication data Spanos, Aris, 1952– Probability Theory and Statistical Inference: econometric modeling with observational data / Aris Spanos p. cm. Includes bibliographical references (p.) and index. Contents Preface page xi Acknowledgments xxiv 1 An introduction to empirical modeling 1 1....

This paper introduces a new class of matrix-valued affine jump diffusions that are convenient for modeling multivariate risk factors in many financial and econometric problems. We provide an analytical transform analysis for this class of models, leading to an analytical treatment of a broad class of multivariate valuation and econometric problems. Examples of potential applications include fix...

We consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staffing of such centers. Our forecasting approach utilizes dimension reduction through a factor analy...

conventionally, regression and time series analyses have been employed in modeling water demand forecasts. in recent years, the relatively new technique of neural networks (nns) has been proposed as an efficient tool for modeling and forecasting. the objective of this study is to investigate the relatively new technique of gmdh – type neural networks for the use of forecasting long – term urban...

This paper presents theoretical models and their empirical results for the return and variance dynamics of German stocks. A factor structure is used in order to allow for a parsimonious modeling of the rst two moments of returns. Dynamic factor models with GARCH dynamics (GARCH(1,1)-M, IGARCH(1,1)-M, Nonlinear Asymmetric GARCH(1,1)-M and Glosten-Jagannathan-Runkle GARCH(1,1)-M) and three di ere...

We survey the literature on spatial bio-economic and land-use modelling and review thematic developments. Unobserved site specific heterogeneity is common in almost all of the surveyed works . Heterogeneity appears also to be a significant catalyst engendering significant methodological innovation. To better equip prototypes to adequately incorporate heterogeneity, we consider a smorgasbord of ...