RANDOM COEFFICIENT MODELS: THEORY AND APPLICATIONS

Abstract. This paper provides an overview of the rationale behind, and the implementation, and uses of, the random coefficient approach to econometric modelling. A simple random coefficient model is presented, and methods for estimating, testing, and validating such a model are described. A more general model is then presented. The general model is shown to include several fixed-coefficient models as special cases and can be estimated incorporating a variety of judgements concerning simplification. Finally, the paper reviews recent applications of random coefficient estimation.     

DIC in variable selection

Model comparison is discussed from an information theoretic point of view. In particular the posterior predictive entropy is related to the target yielding DIC and modifications thereof. The adequacy of criteria for posterior predictive model comparison is also investigated depending on the comparison to be made. In particular variable selection as a special problem of model choice is formalized in different ways according to whether the comparison is a comparison across models or within an encompassing model and whether a joint or conditional sampling scheme is applied. DIC has been devised for comparisons across models. Its use in variable selection and that of other criteria is illustrated for a simulated data set.     

论人本管理的本质与实现

文章指出人本管理作为一种不同于其他管理理论之处在于它对人的理解,对于“人”与“本”的内在逻辑的合理把握,在我国特有的管理语境中,对人本管理的本质与实现的理解有着自己的独特性。     

省级政府在农村公共品供给中的地位分析——以山东省为例

在当前“自上而下”的农村公共品供给体制中,文章提出了省级政府的“双角色主体”假说,即省级政府一方面要发挥“接力者”的角色,负责将中央政府有关农村公共品供给的决策与资金安排及时准确地传递给其他地方政府;另一方面,省级政府还要承担“供给者”的责任,不仅要对中央政府的公共品决策进行本地化的修正,还要及时应对本地居民有关农村公共品需求的变化调整。运用山东省的数据资料证明了“双角色主体”可以确保农村公共品充足供给以及在当前“接力者”要优于“供给者”,但未来后者会日渐重要的两个命题。     

Adaptive market efficiency of agricultural commodity futures contracts

In this paper we investigate the adaptive market efficiency of the agricultural commodity futures market, using a sample of eight futures contracts. Using a battery of nonlinear tests, we uncover the nonlinear serial dependence in the returns series. We run the Hinich portmanteau bicorrelation test to uncover the moments in which the nonlinear serial dependence, and therefore adaptive market efficiency, occurs for our sample.     

Inference with Dependent Data in Accounting and Finance Applications

We review developments in conducting inference for model parameters in the presence of intertemporal and cross‐sectional dependence with an emphasis on panel data applications. We review the use of heteroskedasticity and autocorrelation consistent (HAC) standard error estimators, which include the standard clustered and multiway clustered estimators, and discuss alternative sample‐splitting inference procedures, such as the Fama–Macbeth procedure, within this context. We outline pros and cons of the different procedures. We then illustrate the properties of the discussed procedures within a simulation experiment designed to mimic the type of firm‐level panel data that might be encountered in accounting and finance applications. Our conclusion, based on theoretical properties and simulation performance, is that sample‐splitting procedures with suitably chosen splits are the most likely to deliver robust inferential statements with approximately correct coverage properties in the types of large, heterogeneous panels many researchers are likely to face.     

振动应力筛选

本文介绍了振动应力筛选技术,讨论了建立振动应力筛选的益处,应考虑的主要问题以及应牢记的关键因素。     

ARCH and GARCH models vs. martingale volatility of finance market returns

ARCH and GARCH models assume either i.i.d. or ‘white noise’ as is usual in regression analysis, while also assuming memory in a conditional mean square fluctuation with stationary increments. We will show that ARCH/GARCH is inconsistent with uncorrelated increments, violating the i.i.d. and ‘white’ assumptions, and violating finance data and the efficient market hypothesis as well.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

就业质量促进家庭消费升级的理论与实证分析

依据2010—2020年共6期中国家庭追踪调查（CFPS）面板数据,构建个体层面就业质量指标体系,考量就业质量对家庭消费升级的影响。结果显示：就业质量显著促进家庭消费升级;与农村和中西部地区相比,就业质量提升对城镇和东部家庭消费升级的促进效应更显著;机制分析表明,金融素养水平、家庭经济条件、年龄段均存在不同程度的调节效应。