Articles

A bounded rationality model of information search and choice in preference measurement

L. C. YANG, O. TOUBIA, M. DE JONG

Journal of Marketing Research

Forthcoming

Departments: Marketing


A Mathematical Turn in Business Regulation: The Rise of Legal Indicators

D. RESTREPO AMARILES

International Journal of Law in Context

Forthcoming

Departments: Tax & Law


A Two-sided Matching Approach for Partner Selection and Assessing Complementarities in Partners’ Attributes in Inter-firm Alliances

D. MINDRUTA, M. MOEEN, R. AGARWAL

Strategic Management Journal

Forthcoming

Departments: Strategy & Business Policy, GREGHEC (CNRS)


Accurate Methods for Approximate Bayesian Computation Filtering

L. CALVET, V. CZELLAR

Journal of Financial Econometrics

Forthcoming

Departments: Finance, GREGHEC (CNRS), Economics & Decision Sciences

Keywords: bandwidth, kernel density estimation, likelihood estimation, model selection, particle filter, state-space model, value-at-risk forecasts


The Approximate Bayesian Computation (ABC) filter extends the particle filtering methodology to general state-space models in which the density of the observation conditional on the state is intractable. We provide an exact upper bound for the mean squared error of the ABC filter, and derive sufficient conditions on the bandwidth and kernel under which the ABC filter converges to the target distribution as the number of particles goes to infinity. The optimal convergence rate decreases with the dimension of the observation space but is invariant to the complexity of the state space. We show that the adaptive bandwidth commonly used in the ABC literature can lead to an inconsistent filter. We develop a plug-in bandwidth guaranteeing convergence at the optimal rate, and demonstrate the powerful estimation, model selection, and forecasting performance of the resulting filter in a variety of examples

Ambiguity and the Bayesian Approach

I. GILBOA, M. MARINACCI

Advances in Economics and Econometrics: Theory and Applications

Forthcoming

Departments: Economics & Decision Sciences, GREGHEC (CNRS)