This paper demonstrates a mechanism by which environmental shocks in biological production processes can lead to extreme price movements and thus be a contributing factor to short‐term food price volatility. In biological production processes, environmental shocks can lead to a stock‐out when the harvest transitions to a new stock (year class) with a different marginal value. The result in the market is a temporary price spike, or bubble, bounded by the marginal value of the new stock. We highlight this phenomenon in a cohort, or year class, biological production setting. Each year class in the model is a finite “non‐renewable” capital stock, and capital theory is used to solve for the stochastic dynamic competitive equilibrium. The model is parameterized to be representative of the Norwegian salmon aquaculture industry. Results suggest that the model can replicate much of the observed patterns in price, harvest, and capital stock dynamics, including the infrequent occurrence of extremely high prices in the market.

Quantifying non-linear dependence structures between two random variables is a challenging task. There exist several bona-fide dependence measures able to capture the strength of the non-linear association, but they typically give little information about how the variables are associated. This problem has been recognized by several authors and has given rise to the concept of local measures of dependence. A local measure of dependence is able to capture the "local" dependence structure in a particular region. The idea is that the global dependence structure is better described by a portfolio of local measures of dependence computed in different regions than a one-number measure of dependence. This paper introduces the R package localgauss which estimates and visualizes a measure of local dependence called local Gaussian correlation. The package provides a function for estimation, a function for local independence testing and corresponding functions for visualization purposes, which are all demonstrated with examples.

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We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian statespace models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and potentially high-dimensional state trajectories. We propose to combine PG with a generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially improve the mixing and the efficiency of the PG draws from the posterior of the states and the parameters relative to existing PG implementations. The efficiency gains achieved by PEIS are illustrated in PG applications to a univariate stochastic volatility model for asset returns, a non-Gaussian nonlinear local-level model for interest rates, and a multivariate stochastic volatility model for the realized covariance matrix of asset returns. JEL classification: C11; C13; C15; C22.

We explore the construction of new symplectic numerical integration schemes to be used in Hamiltonian Monte Carlo and study their efficiency. Two integration schemes from Blanes et al. (2014), and a new scheme based on optimal acceptance probability, are considered as candidates to the commonly used leapfrog method. All integration schemes are tested within the framework of the No-U-Turn sampler (NUTS), both for a logistic regression model and a student t-model. The results show that the leapfrog method is inferior to all the new methods both in terms of asymptotic expected acceptance probability for a model problem and the and efficient sample size per computing time for the realistic models.