Bayesian Analysis of Dynamic Multivariate Models with Multiple Structural Breaks

This paper considers a vector autoregressive model or a vector error correction model with multiple structural breaks in any subset of parameters, using a Bayesian approach with Markov chain Monte Carlo simulation technique. The number of structural breaks is determined as a sort of model selection by the posterior odds. For a cointegrated model, cointegrating rank is also allowed to change with breaks. Bayesian approach by Strachan (Journal of Business and Economic Statistics 21 (2003) 185) and Strachan and Inder (Journal of Econometrics 123 (2004) 307) are applied to estimate the cointegrating vectors. As empirical examples, we investigate structural changes in the predictive power of the yield curve and the US term structure of interest rates. We find strong evidence of three structural changes in both applications.Bayesian inference, Structural break, Cointegration, Bayes factor

Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management

This article addresses the issue of computing the expected cost functions from a probabilistic model of the air traffic flow and capacity management. The Clenshaw-Curtis quadrature is compared to Monte-Carlo algorithms defined specifically for this problem. By tailoring the algorithms to this model, we reduce the computational burden in order to simulate real instances. The study shows that the Monte-Carlo algorithm is more sensible to the amount of uncertainty in the system, but has the advantage to return a result with the associated accuracy on demand. The performances for both approaches are comparable for the computation of the expected cost of delay and the expected cost of congestion. Finally, this study shows some evidences that the simulation of the proposed probabilistic model is tractable for realistic instances.Comment: Interdisciplinary Science for Innovative Air Traffic Management (2013

Sequential Quasi-Monte Carlo

We derive and study SQMC (Sequential Quasi-Monte Carlo), a class of algorithms obtained by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as an extension of, the array-RQMC algorithm of L'Ecuyer et al. (2006). The complexity of SQMC is $O(N \log N)$, where $N$ is the number of simulations at each iteration, and its error rate is smaller than the Monte Carlo rate $O_P(N^{-1/2})$. The only requirement to implement SQMC is the ability to write the simulation of particle $x_t^n$ given $x_{t-1}^n$ as a deterministic function of $x_{t-1}^n$ and a fixed number of uniform variates. We show that SQMC is amenable to the same extensions as standard SMC, such as forward smoothing, backward smoothing, unbiased likelihood evaluation, and so on. In particular, SQMC may replace SMC within a PMCMC (particle Markov chain Monte Carlo) algorithm. We establish several convergence results. We provide numerical evidence that SQMC may significantly outperform SMC in practical scenarios.Comment: 55 pages, 10 figures (final version

Application of Sequential Quasi-Monte Carlo to Autonomous Positioning

Sequential Monte Carlo algorithms (also known as particle filters) are popular methods to approximate filtering (and related) distributions of state-space models. However, they converge at the slow $1/\sqrt{N}$ rate, which may be an issue in real-time data-intensive scenarios. We give a brief outline of SQMC (Sequential Quasi-Monte Carlo), a variant of SMC based on low-discrepancy point sets proposed by Gerber and Chopin (2015), which converges at a faster rate, and we illustrate the greater performance of SQMC on autonomous positioning problems.Comment: 5 pages, 4 figure

Bootstrap Hypothesis Testing

This paper surveys bootstrap and Monte Carlo methods for testing hypotheses in econometrics. Several different ways of computing bootstrap P values are discussed, including the double bootstrap and the fast double bootstrap. It is emphasized that there are many different procedures for generating bootstrap samples for regression models and other types of model. As an illustration, a simulation experiment examines the performance of several methods of bootstrapping the supF test for structural change with an unknown break point.bootstrap test, supF test, wild bootstrap, pairs bootstrap, moving block bootstrap, residual bootstrap, bootstrap P value

Super-Droplet Method for the Numerical Simulation of Clouds and Precipitation: a Particle-Based Microphysics Model Coupled with Non-hydrostatic Model

A novel simulation model of cloud microphysics is developed, which is named Super-Droplet Method (SDM). SDM enables accurate calculation of cloud microphysics with reasonable cost in computation. A simple SDM for warm rain, which incorporates sedimentation, condensation/evaporation, stochastic coalescence, is developed. The methodology to couple SDM and a non-hydrostatic model is also developed. It is confirmed that the result of our Monte Carlo scheme for the coalescence of super-droplets agrees fairly well with the solution of stochastic coalescence equation. A preliminary simulation of a shallow maritime cumulus formation initiated by a warm bubble is presented to demonstrate the practicality of SDM. Further discussions are devoted for the extension and the computational efficiency of SDM to incorporate various properties of clouds, such as, several types of ice crystals, several sorts of soluble/insoluble CCNs, their chemical reactions, electrification, and the breakup of droplets. It is suggested that the computational cost of SDM becomes lower than spectral (bin) method when the number of attributes $d$ becomes larger than some critical value, which may be $2\sim4$