Finding good predictors for inflation by shotgun stochastic search

Konferenzbeitrag

This paper evaluates a novel sampling algorithm, called shotgun stochastic search (S³), for Bayesian model averaging in the context of finding predictors for inflation when the set of potential predictors is large. This is a relevant case in the forecasting literature, where often hundreds of predictors are compared with autoregressive distributed lag models for inflation. With such a large model space, standard Bayesian approaches like MCMC model composition (MC³) tend to converge slowly. On the other hand, S³ systematically searches in the neighborhood of good models and concentrates on regions of high posterior probability in the model space. We carry out a Monte Carlo simulations to compare the computational efficiency of S³ to MC³, based on standard data generating processes from the literature. When many potential predictors are available, S³ outperforms MC³. In an empirical exercise, we apply the two algorithms to find predictors for US inflation from a set of about one hundred indicators and their lags. S³ absorbs posterior mass much quicker than MC³ and makes Bayesian estimation of the standard inflation equations with many predictors computationally feasible.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Forecasting Methods ; No. A11-V2

Classification and Topics

• Classification: Wirtschaft ; Price Level; Inflation; Deflation ; Model Evaluation, Validation, and Selection ; Bayesian Analysis: General

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Scharnagl, Michael ; Schumacher, Christian
• Event: Veröffentlichung
• (who): Verein für Socialpolitik
• (where): Frankfurt a. M.
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/37181
• Last update: 10.03.2025, 11:44 AM CET