Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

Confronting the crisis of food safety and revitalizing companies' social responsibility in the People's Republic of China

In recent years, China has experienced many crises related to food safety which have challenged the legitimacy of food production companies and damaged the reputation of relevant state law enforcement agencies, as well as influenced consumer confidence. Based on recent interviews with 20 food and drink production companies in the Shanghai region, this paper aims to address a key research question: what are the institutional and organizational factors that influence the production systems in general and the quality and safety of products in particular? The outcome of this research indicates that the pressure to minimize costs driven by price competition, changing consumer expectations, as well as a lack of enforcement of state regulation, cause companies to struggle to stay afloat and impede their commitment to social responsibility.     

Modeling Conditional Skewness in Stock Returns

Abstract

In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the monthly postwar US stock returns. Small positive news is also found to have a smaller impact on conditional variance than no news at all. Moreover, the symmetric GARCH-M model not allowing for conditional skewness is found to systematically overpredict conditional variance and average excess returns.     

Estimation of Asymmetric Stochastic Volatility Models: Application to Daily Average Prices of Energy Products

Energy supply and demand, and as a consequence energy prices, are likely to represent one of the biggest challenges of the 21st century. Commodity markets exhibit increased volatility when there is little or no underutilized supply capability to meet natural fluctuations in demand. In the case of energy markets, the large capital requirements and significant lead times associated with energy production and delivery make them more susceptible to the imbalances in supply capability and demand. Energy price volatility has destructive impact on market agents, and this impact is intensified when the prices exhibit asymmetric volatility. This article pursues two aspects of the issue. First we consider general aspects, especially the asymmetric pattern of volatility of daily returns of different types of energy products. Then, we analyze the behaviour of daily returns by using traditional models of volatility that include AGARCH, TGARCH, EGARCH, and ARSV strategies, as well as a threshold asymmetric autoregressive stochastic volatility (TA-ARSV) model that we propose. The energy products considered in this analysis are probably the most relevant energy products for the economic activity of the nations and the economic relations between countries: Crude Oil (OPEC reference basket and London Brent index), Gasoline, Natural Gas, Butane, and Propane. We use spot prices and the time reference ranges from 1986–1993 to 2009 depending on the product.     

家庭收入预期与货币政策有效性

通过对2001年第一季度至2006年第一季度我国货币政策与家庭收入预期的相互影响进行实证分析发现：我国货币政策与家庭收入预期的相互影响具有持久性，并且呈反方向变动；钉住通货膨胀率的货币政策具有反周期性质；我国居民消费／储蓄（或消费贷款）受到跨期最优的约束，我们不能忽视前瞻性变量对居民行为的影响。     

On Jumps and ARCH Effects in Natural Resource Prices: An Application to Pacific Northwest Stumpage Prices

Continuous-time models of natural resource prices usually preclude the possibility of large changes (jumps) resulting from unexpected events. To test for the presence of jumps and/or ARCH effects, we combine bounds and the Monte Carlo test technique to obtain finite-sample, level-exact p -values. We apply this methodology to stumpage prices from the Pacific Northwest and find evidence of jumps and ARCH effects. To assess the impact of neglecting jumps on the decision to harvest old-growth timber, we develop an autonomous, infinite-horizon stopping model for which we provide a new method of resolution. Our numerical results show the importance of modeling jumps explicitly.     

U.S. and Latin American stock market linkages

This paper examines to what extent the Latin American equity markets of Argentina, Brazil, Chile and Mexico have become more integrated with the US equity market. We empirically measure integration by finding the dynamic conditional correlation (DCC) between each market and that in the U.S. using a DCC multivariate GARCH model. We then track how these correlations evolve over time using a smooth transition model which not only shows when greater integration first occurred but also how long it took these correlations to transition to their new levels. Our sample period stretches from December 30th, 1988 to March 26th, 2004. Results show an increase in the degree of co-movement between these countries’ equity returns and those in the U.S. although the magnitude and speed of these increases greatly varies across these four countries.     

论传统成本理论的期望差距及其理论态势

通过对传统成本理论及其期望差距的界定和回顾，深入评析了传统成本理论期望差距的历史必然性．及其在现代经济环境中的演进态势，提出了消除成本理论期望差距，实现成本系统优化是现代成本理论发展必由之路的基本结论。     

Stochastic Conditional Intensity Processes

In this article, we introduce the so-called stochastic conditionalintensity (SCI) model by extending Russell’s (1999) autoregressiveconditional intensity (ACI) model by a latent common dynamicfactor that jointly drives the individual intensity components.We show by simulations that the proposed model allows for awide range of (cross-)autocorrelation structures in multivariatepoint processes. The model is estimated by simulated maximumlikelihood (SML) using the efficient importance sampling (EIS)technique. By modeling price intensities based on NYSE trading,we provide significant evidence for a joint latent factor andshow that its inclusion allows for an improved and more parsimoniousspecification of the multivariate intensity process.     

关于后验矩的近似计算

文章介绍了数理统计中实数或向量参数的正函数的后验平均、方差和预测密度的近似计算。