A Microeconomic Approach to Diffusion Models For Stock Prices

This paper studies a class of diffusion models for stock prices derived by a microeconomic approach. We consider discrete-time processes resulting from a market equilibrium and then apply an invariance principle to obtain a continuous-time model. the resulting process is an Ornstein-Uhlenbeck process in a random environment, and we analyze its qualitative behavior. In particular, we provide simple criteria for the stability or instability of the corresponding stock price model, and we give explicit formulae for the invariant distributions in the recurrent case.     

The Limit Distribution of the Bootstrap for the Unit Root Test Statistic when the Residuals are Dependent

In this article, the unit root test for the AR(1) model with dependent residuals is considered. We adopt a bootstrap procedure to bootstrap the residuals with bootstrap sample size m less than the size n of the original sample. Under the assumptions that m → ∞ and m/n → 0, the convergence in probability of the bootstrap distribution function is established. Research supported by National Natural Science Foundation of China (No. 10471126)     

The Gender Wealth Gap in Europe: Application of Machine Learning to Predict Individual-level Wealth

This article provides comparative estimates of the gender wealth gaps for 22 European countries, employing data from the Household Finance and Consumption Survey. The data on wealth are collected at the household level, while individual-level data are needed for the estimates of gender wealth gaps. We propose a novel approach using machine learning and model averaging methods to predict individual-level wealth data for multi-person households. Our results suggest that random forest performs best as the predicting tool for this exercise, outperforming elastic net and Bayesian model averaging. The estimated gender wealth gaps tend to be in favor of men, especially at the top of the wealth distribution. Men have 24 percent more wealth than women on average. We also find that a high home ownership rate is associated with a smaller country-level gender wealth gap. Our estimates suggest that the individual-level wealth inequality is on average 3 pp higher than the household-level wealth inequality in multi-member households.     

Seasonal Adjustment in a Market for Female Agricultural Workers

This article explores seasonal adjustment in the market for temporary agricultural labor. We estimate a model of participation allowing for unobserved heterogeneity and endogeneity/selection bias using daily observations from Chilean panel data, and a model of daily earnings. Results indicate that seasonal wage variation is an important aspect of labor-market adjustment, contributing to a large change in labor force participation. The labor force participation rate of women is significantly more elastic to changes in the expected wage than is that for men. Nonetheless, we find evidence of substantial open unemployment during the slack season, especially for females, probably due to frictional and efficiency wage effects.     

双线性有限元Monte Carlo方法

本文构造了矩形有限元的概率算法,应用这种方法可以直接求出有限元解在一个或少数几个网格点的近似值。     

Comparing risk attitudes of organic and non-organic farmers with a Bayesian random coefficient model

Organic farming is usually considered to be more risky thanconventional farming, but the risk aversion of organic farmerscompared with that of conventional farmers has not been studied.Using a non-structural approach to risk estimation, a Bayesianrandom coefficient model is used to obtain individual Arrow–Prattcoefficients of absolute risk aversion for a sample of Dutchorganic and non-organic arable farmers. The model is estimatedusing Gibbs sampling. The results indicate that organic farmersare significantly less risk averse than their non-organic colleagues.     

An Adjustment Procedure for Predicting Betas When Thin Trading is Present: Canadian Evidence

We have incorporated effects of the process that generates true betas for TSE stocks, as well as thin trading effects, into the beta adjustment model. We note the Blume and Dimson and Marsh beta adjustment techniques aim at eliminating beta forecast error through regression tendency bias. Effects of other sources of forecast error have been ignored. We show the process generating security betas affects both cross-sectional correlation coefficient and order bias, while thin trading affects only cross-sectional correlation coefficient. We demonstrate that when OLS beta estimates are used to forecast their future risk levels, order bias accounts for 86% of forecast error, while thin trading effects account for 14% of forecast error. A beta regression tendency model which properly accounts for effects of cross-sectional correlation (which is a function of thin trading) and order bias completely abates forecast error. Our results have implications for the use of correlation coefficient to measure stability of betas across time, for beta adjustment models proposed in the literature, and for event study methodologies that rely on prediction errors.     

THE TERM STRUCTURE OF INTEREST RATES AS A GAUSSIAN RANDOM FIELD

A simple model of the term structure of interest rates is introduced in which the family of instantaneous forward rates evolves as a continuous Gaussian random field. A necessary and sufficient condition for the associated family of discounted zero-coupon bond prices to be martingales is given, permitting the consistent pricing of interest rate contingent claims. Examples of the pricing of interest-rate caps and the situation when the Gaussian random field may be viewed as a deterministic time change of the standard Brownian sheet are discussed.     

The Problem of Time Dependent Explanatory Variables at the Context-Level in Discrete Time Multilevel Event History Analysis: A Comparison of Models Considering Mobility Between Local Labour Markets as an Example

The paper deals with the question of how to include time dependent explanatory variables at the context-level in multilevel event history models. In general, context-level explanatory variables in multilevel models are assumed to be time constant. Only time constant context-level explanatory variables perform the task of reducing context-level error variance. Thus, it will be suggested that the analysis should be extended to a three-level model. In this model, time periods of persons constitute level 1 units, time periods of contexts constitute level 2 units and the contexts themselves constitute level 3 units – in which in turn level 2 units are clustered. Considering mobility between local labour markets as an example, four different ways of modelling time varying context-level variables are compared. The result is that the proposed three-level model leads to the most conservative results.     

机器类设备基于不同时延需求分组的随机接入方法

针对大规模机器类通信中拥塞导致的时延敏感设备时延高和接入成功率低的问题,提出将小区中设备按时延要求分组,对不同组设备引入不同的退避模型,分析时延敏感设备的时延和吞吐量,按照不同组中设备的时延需求动态分配前导数目,同时通过调整接入类限制因子实现吞吐量的优化。仿真结果表明,在给定时延敏感设备的时延限制条件时,与统一退避的机制对比,所提分组机制的时延敏感设备能够满足时延要求,并且提高了接入效率。