A Bayesian Nonparametric Investigation of the Predictive Effect of Exchange Rates on Commodity Prices

This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries: Canada, Australia, and New Zealand. We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index. The mixing weights follow a set of Probit stick-breaking priors that are time-varying. We find that exchange rates have a positive predictive effect in general, but accounting for time variation does not improve forecasting performance. By contrast, the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases, which is important in forecasting both the mean and the density of commodity prices one period ahead. The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.     

Modeling electric taxis' charging behavior using real-world data

Charging behavior is critical to the development and deployment of electric vehicle (EV) systems, given its impacts in EV adoption, the energy and environmental performance of EVs, potential load change to the electric grid, etc. However, the general characteristics of practical charging behavior have not been well studied. Existing studies are mostly based on travel data from conventional internal combustion engine vehicles, modeled with assumed and simplified charging scenarios. The use of public charging infrastructure is often neglected. Few studies evaluate real-world charging behaviors of EVs currently in operation using public charging stations. To address this gap, this study analyzes the data of 39,372 charging events from 129 unique electric taxis in Shenzhen, China to study the distributions of daily charging frequency, charging start time, and charging duration. The insights we learned from this study are: 1) the daily frequency for a vehicle to visit charging stations is unlikely to exceed five times; 2) the distribution of charging start time have multiple peaks and can be fitted with Gaussian Mixture Models; 3) charging duration is influenced by charging start time; and 4) charging dynamics can be modeled using the distributions of daily charging frequency, charging start time, and charging duration. Results from this study can inform charging behavior modeling for EVs and charging infrastructure development.     

基于ATM网络的数据采集接入电路设计与实现

阐述基于ATM(异步传输模式)网络的数据采集接入电路的设计方法，介绍了系统接入电路中的核心电路，即差分混合接入电路，证明了实现发送与接收之间的最小干扰关系，并根据线路要求采用了ADSL网络专用的AD8016作为放大器，最后给出了实际应用电路。     

Spatial clustering of time series via mixture of autoregressions models and Markov random fields

Time series data arise in many medical and biological imaging scenarios. In such images, a time series is obtained at each of a large number of spatially dependent data units. It is interesting to organize these data into model‐based clusters. A two‐stage procedure is proposed. In stage 1, a mixture of autoregressions (MoAR) model is used to marginally cluster the data. The MoAR model is fitted using maximum marginal likelihood (MMaL) estimation via a minorization–maximization (MM) algorithm. In stage 2, a Markov random field (MRF) model induces a spatial structure onto the stage 1 clustering. The MRF model is fitted using maximum pseudolikelihood (MPL) estimation via an MM algorithm. Both the MMaL and MPL estimators are proved to be consistent. Numerical properties are established for both MM algorithms. A simulation study demonstrates the performance of the two‐stage procedure. An application to the segmentation of a zebrafish brain calcium image is presented.     

Eksamen i Forsikringsvidenskab og Statistik ved Köbenhavns Universitet. 2. Del. Den skriftlige Pröve

Abstract

This paper develops a Pareto scale-inflated outlier model. This model is intended for use when data from some standard Pareto distribution of interest is suspected to have been contaminated with a relatively small number of outliers from a Pareto distribution with the same shape parameter but with an inflated scale parameter. The Bayesian analysis of this Pareto scale-inflated outlier model is considered and its implementation using the Gibbs sampler is discussed. The paper contains three worked illustrative examples, two of which feature actual insurance claims data.     

沥青混合料生产质量稳定性分析

本文主要依托高速公路铣刨修复专项养护工程,通过对专项养护工程施工中沥青混合料质量检测,总结出沥青混合料生产质量通病并提出改进建议。     

Price Behavior in a Dynamic Oligopsony: Washington Processing Potatoes

Punishment strategies are necessary to sustain a collusive oligopsony in a repeated game context when demand is uncertain and only market variables are observable. This paper proposes a test for tacit collusion among potato processors in Washington state using a dynamic regime-switching model estimated with a finite mixture method. The results support the existence of punishment and collusive regimes and show the welfare losses due to anti-competitive behavior on the part of processors to be significant. Processors' oligopsony power is enhanced by higher domestic production, imports, and existing stocks, but it is ameliorated by high capacity utilization rates and exports.     

BAYESIAN INFERENCE METHODS FOR UNIVARIATE AND MULTIVARIATE GARCH MODELS: A SURVEY

This survey reviews the existing literature on the most relevant Bayesian inference methods for univariate and multivariate GARCH models. The advantages and drawbacks of each procedure are outlined as well as the advantages of the Bayesian approach versus classical procedures. The paper makes emphasis on recent Bayesian non‐parametric approaches for GARCH models that avoid imposing arbitrary parametric distributional assumptions. These novel approaches implicitly assume infinite mixture of Gaussian distributions on the standardized returns which have been shown to be more flexible and describe better the uncertainty about future volatilities. Finally, the survey presents an illustration using real data to show the flexibility and usefulness of the non‐parametric approach.     

Oil price and FX-rates dependency

Oil prices and exchange rates against the dollar have both experienced long swings over the recent decade. Regardless of the great amount of research, some issues are still open to debate. In this vein, this paper focuses on the evolution of the relationship between oil prices and dollar exchange rates of 12 oil exporting and oil importing countries based on a dynamic copula approach. We use daily data for two 5-year periods between 2003 and 2013, taking the collapse of Lehman Brothers as the dividing point. Our results have four main implications: first, the intensity of relationship between oil prices and FX-rates has increased over time even if the peak of the financial crisis is included. Second, the increased tail dependency shows that extreme events are likelier to occur simultaneously for both series. Third, the dependency has become more dynamic after the financial crisis and is therefore better characterized by time-varying copulas. Finally, currencies of oil importers and oil exporters display a different dependency structure against the US dollar in the case of rising oil prices with the latter appreciating and the former depreciating.     

Industry and time specific deviations from fundamental values in a random coefficient model

The paper analyzes the relationship between stock prices and fundamentals for a large sample of US stocks in the last 10 years using a random coefficient model. Heterogeneity and omitted variable bias are properly taken into account with model coefficients being allowed to vary across time and industries. The random coefficient model allows to track waves of reliance on analysts’ forecasts and nonfundamental stock price components across time and clearly identifies the growth of the nonfundamental component in the long 1991–2000 swing.