Abstract
We propose a State-Space Model (SSM) for commodity prices that combines the competitive storage model with a stochastic trend. This approach fits into the economic rationality of storage decisions and adds to previous deterministic trend specifications of the storage model. For a Bayesian posterior analysis of the SSM, which is nonlinear in the latent states, we used a Markov chain Monte Carlo algorithm based on the particle marginal Metropolis–Hastings approach. An empirical application to four commodity markets showed that the stochastic trend SSM is favored over deterministic trend specifications. The stochastic trend SSM identifies structural parameters that differ from those for deterministic trend specifications. In particular, the estimated price elasticities of demand are typically larger under the stochastic trend SSM.
Highlights
We propose to use the Particle Marginal Metropolis–Hastings (PMMH) approach as developed by Andrieu et al (2010), which is well suited for a posterior analysis of our proposed storage State-Space Model (SSM) as it can cope with the discontinuity of the gradients of f ( x ) and is easy to implement
We proposed a stochastic trend competitive storage model for commodity prices, which defines a nonlinear State-Space Model (SSM)
Our stochastic trend approach fits into the economic rationality of the competitive storage model and is sufficiently flexible to account for the variation in the observed prices that the competitive storage model is not intended to explain
Summary
How to deal with trends when confronting economic theories with real-world data is an important issue. Using a stochastic trend specification to account for nonstationary price data, our empirical approach aims at fitting into the economic rationality of the stationary storage model so that it preserves theoretical coherence, promising meaningful estimates of the structural parameters. The stochastic trend as used in our SSM approach represents, in Bayesian terms, a hierarchical prior for the low-frequency price component, which is consistent with the rationality of the economic model, and flexible in its design to account for variation that the storage model is not intended to explain This makes our approach applicable to a broad range of commodity markets and different sampling frequencies. We discuss the findings before we offer some concluding remarks (Section 6)
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