Estimating the competitive storage model: A simulated likelihood approach
Tore Kleppe () and
Econometrics and Statistics, 2017, vol. 4, issue C, 39-56
A particle filter maximum likelihood estimator for the competitive storage model is developed. The estimator is suitable for inference problems in commodity markets where only reliable price data is available for estimation, and shocks are temporally dependent. The estimator efficiently utilizes the information present in the conditional distribution of prices when shocks are not iid. Compared to Deaton and Laroque’s composite quasi-maximum likelihood estimator, simulation experiments and real-data estimation show substantial improvements in both bias and precision. Simulation experiments also show that the precision of the particle filter estimator improves faster than for composite quasi-maximum likelihood with more price data. To demonstrate the estimator and its relevance to actual data, the storage model is fitted to data set of monthly natural gas prices. It is shown that the storage model estimated with the particle filter estimator beats, in terms of log-likelihood, commonly used reduced form time-series models such as the linear AR(1), AR(1)-GARCH(1,1) and Markov Switching AR(1) models for this data set.
Keywords: Commodity prices; Competitive storage model; Particle filter; Rational expectations; Simulated likelihood (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only. Contains open access articles
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:4:y:2017:i:c:p:39-56
Access Statistics for this article
Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi
More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().