High wage workers and low wage firms: negative assortative matching or limited mobility bias?

Summary.  In the empirical literature on assortative matching using linked employer–employee data, unobserved worker quality appears to be negatively correlated with unobserved firm quality. We show that this can be caused by standard estimation error. We develop formulae that show that the estimated correlation is biased downwards if there is true positive assortative matching and when any conditioning covariates are uncorrelated with the firm and worker fixed effects. We show that this bias is bigger the fewer movers there are in the data, which is 'limited mobility bias'. This result applies to any two-way (or higher) error components model that is estimated by fixed effects methods. We apply these bias corrections to a large German linked employer–employee data set. We find that, although the biases can be considerable, they are not sufficiently large to remove the negative correlation entirely.     

货币政策冲击对股票市场价格泡沫影响的时变分析

一直以来,资产价格波动与货币政策的关系都是经济金融领域关注的重点问题。本文以1997-2014年我国月度数据为样本,运用新近发展的时变参数的因子扩展向量自回归模型（TVP-FAVAR）分析了我国货币政策冲击对股票市场价格泡沫影响的时变特征。研究结果表明,我国货币政策的变动对股票市场价格的影响有着显著的时变性特点。当股票市场存在较大程度的资产价格泡沫时,股票市场的价格可能会随着利率提高而上升,采用加息的紧缩货币政策未必能够起到相应的调控资产价格波动的作用。这一结果和理性资产价格泡沫理论一致,意味着有必要重新审视传统观念中货币政策与资产价格波动之间是逆周期关系的认知。因此,政策制定者在采用“逆风而动”的货币政策时需要更加审慎,应密切关注货币政策对资产价格影响的时变特征,针对金融市场波动状况相机抉择。     

货币政策中介目标M2的修正和新生

M2是传统的货币政策中介目标,然而,随着经济和金融环境的变化,它的有效性备受质疑。在没有更好的替代指标出现以前,探讨修正的M2是学术界的热点。迪维西亚法和现金等价法都是基于流动性总量的测度来修正简单加总法测度下的M2。本文通过更为简洁的模型推导,首次在构建原理上论证,在测度M2的流动性总量时,现金等价法的精确性明显优于迪维西亚法。本文还基于货币需求函数的稳定性和选择货币政策中介目标的可控性、相关性原则,对简单加总法、迪维西亚法和现金等价法测度下的M2的合理性进行实证比较,结果表明,现金等价法测度下的M2在货币需求函数的稳定性以及作为货币政策中介目标的可控性、相关性上都明显优于简单加总法和迪维西亚法测度下的M2。因此,本文认为,应该用现金等价法来修正传统M2,使其能够准确反映流动性总量的变化,提升其作为货币政策中介目标的效力。     

Correction of Bias in Imputing Missing Values of Categorical Variables

Markov Chain Monte Carlo (MCMC) is the most common method used in multiple imputation. However, it is not unbiased when it is applied to imputations of categorical variables. The literature has considered the problem for binary variables with only two levels. In this article, we consider more general situations. We not only evaluate the bias associated with the imputation of categorical variables using the MCMC method, but also introduce a method to correct the bias. A simulation study is conducted and an application is provided to demonstrate the advantages of using the correction factors proposed in this article.     

Bias From Censored Regressors

We study the bias that arises from using censored regressors in estimation of linear models. We present results on bias in ordinary least aquares (OLS) regression estimators with exogenous censoring and in instrumental variable (IV) estimators when the censored regressor is endogenous. Bound censoring such as top-coding results in expansion bias, or effects that are too large. Independent censoring results in bias that varies with the estimation method—attenuation bias in OLS estimators and expansion bias in IV estimators. Severe biases can result when there are several regressors and when a 0–1 variable is used in place of a continuous regressor.     

Connection between uniformity and orthogonality for symmetrical factorial designs

In this paper, we study the issue of uniformity in symmetrical fractional factorial designs. The discrete discrepancy (Biometrika 89 (2002) 893; Metrika 58 (2003) 279; Metrika 60 (2004) 59) is employed as a measure of uniformity. Although there are some emerging literature for connecting uniformity with orthogonality, less attention has been given to this issue for more than three-level fractional factorials and asymmetric fractional factorials. This paper discusses this issue for general symmetric fractional factorials. We derive results connecting uniformity and orthogonality and show that these criteria agree quite well, which provide further justifiable interpretation for some criteria of orthogonality by the consideration of uniformity. In addition, we also point that two measures of orthogonality in the literature (Fang, Hickernell, Niederreiter (Eds.), Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Springer, Berlin, 2002; J. Complexity 19 (2003) 692) are equivalent and derive now a lower bound of the discrete discrepancy.     

Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey

Questions about monetary variables (such as income, wealth or savings) are key components of questionnaires on household finances. However, missing information on such sensitive topics is a well-known phenomenon which can seriously bias any inference based only on complete-case analysis. Many imputation techniques have been developed and implemented in several surveys. Using the German SAVE data, a new estimation technique is necessary to overcome the upward bias of monetary variables caused by the initially implemented imputation procedure. The upward bias is the result of adding random draws to the implausible negative values predicted by OLS regressions until all values are positive. To overcome this problem the logarithm of the dependent variable is taken and the predicted values are retransformed to the original scale by Duan’s smearing estimate. This paper evaluates the two different techniques for the imputation of monetary variables implementing a simulation study, where a random pattern of missingness is imposed on the observed values of the variables of interest. A Monte-Carlo simulation based on the observed data shows the superiority of the newly implemented smearing estimate to construct the missing data structure. All waves are consistently imputed using the new method.     

金融发展差异与货币政策区域效应——以华东六省一市为例

选取华东六省一市作为研究对象,运用因子分析方法研究2012年该7个省市金融发展情况及其差异,并对其金融发展相似性进行聚类分析。在此基础上,采用1990—2012年数据,对聚类后的三类地区通过基于VAR模型的协整关系检验、Granger因果关系检验等建立变量间长期均衡模型,实证研究货币政策在华东地区不同省市效应的差异性。研究结果表明:三类地区经济增长和货币政策对于区域金融发展均具有明显的区域效应,经济发展和广义货币供应量与金融发展均呈正相关关系。鉴此,提出如下政策建议:正视区域经济发展差异,实施差别化货币调控政策;优化各区域金融结构差异,改善货币政策传导效应等。     

Exploring the economic and social determinants of psychological well-being and perceived social support in England

Summary.  A fundamental focus of Government concern is to enhance well-being. Recently, policy makers in the UK and elsewhere have recognized the importance of the community and society to the well-being of the nation as a whole. We explore the extent to which economic and social factors influence the psychological well-being of individuals and their perceptions of the social support that they receive, using Health Survey for England data. We employ a random-effects ordered probit modelling approach and find that unobserved intrahousehold characteristics help to explain the variation in our dependent variables, particularly for co-resident females. Our results indicate that individuals with acute and chronic physical illness, who are female, unemployed or inactive in the labour market and who live in poor households or areas of multiple deprivation report lower levels of psychological well-being. Reduced perceptions of social support are associated with being male, single or post marriage, from an ethnic minority, having low educational attainment and living in a poor household, but are not statistically related to area deprivation measures. These findings may help to inform the contemporary policy debate surrounding the promotion of individual well-being and community, through the alleviation of social exclusion.     

Selection bias in working with the top genes in supervised classification of tissue samples

Currently there is much interest in using microarray gene-expression data to form prediction rules for the diagnosis of patient outcomes. A process of gene selection is usually carried out first to find those genes that are most useful according to some criterion for distinguishing between the given classes of tissue samples. However, there is a bias (selection bias) introduced in the estimate of the final version of a prediction rule that has been formed from a smaller subset of the genes that have been selected according to some optimality criterion. In this paper, we focus on the bias that arises when a full data set is not available in the first instance and the prediction rule is formed subsequently by working with the top-ranked genes from the full set. We demonstrate how large the subset of top genes must be before this selection bias is not of practical consequence.     

Methods for exploring treatment effect heterogeneity in subgroup analysis: an application to global clinical trials

Multi‐country randomised clinical trials (MRCTs) are common in the medical literature, and their interpretation has been the subject of extensive recent discussion. In many MRCTs, an evaluation of treatment effect homogeneity across countries or regions is conducted. Subgroup analysis principles require a significant test of interaction in order to claim heterogeneity of treatment effect across subgroups, such as countries in an MRCT. As clinical trials are typically underpowered for tests of interaction, overly optimistic expectations of treatment effect homogeneity can lead researchers, regulators and other stakeholders to over‐interpret apparent differences between subgroups even when heterogeneity tests are insignificant. In this paper, we consider some exploratory analysis tools to address this issue. We present three measures derived using the theory of order statistics, which can be used to understand the magnitude and the nature of the variation in treatment effects that can arise merely as an artefact of chance. These measures are not intended to replace a formal test of interaction but instead provide non‐inferential visual aids, which allow comparison of the observed and expected differences between regions or other subgroups and are a useful supplement to a formal test of interaction. We discuss how our methodology differs from recently published methods addressing the same issue. A case study of our approach is presented using data from the Study of Platelet Inhibition and Patient Outcomes (PLATO), which was a large cardiovascular MRCT that has been the subject of controversy in the literature. An R package is available that implements the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.     

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes

In randomized clinical trials, methods of pairwise comparisons such as the ‘Net Benefit’ or the ‘win ratio’ have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan–Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.     

Bayesian predictive inference of a finite population proportion under selection bias

We show how to infer about a finite population proportion using data from a possibly biased sample. In the absence of any selection bias or survey weights, a simple ignorable selection model, which assumes that the binary responses are independent and identically distributed Bernoulli random variables, is not unreasonable. However, this ignorable selection model is inappropriate when there is a selection bias in the sample. We assume that the survey weights (or their reciprocals which we call ‘selection’ probabilities) are available, but there is no simple relation between the binary responses and the selection probabilities. To capture the selection bias, we assume that there is some correlation between the binary responses and the selection probabilities (e.g., there may be a somewhat higher/lower proportion of positive responses among the sampled units than among the nonsampled units). We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We use Markov chain Monte Carlo methods to fit the nonignorable selection model. We illustrate our method using numerical examples obtained from NHIS 1995 data.     

Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

ABSTRACT

In logistic regression with nonignorable missing responses, Ibrahim and Lipsitz proposed a method for estimating regression parameters. It is known that the regression estimates obtained by using this method are biased when the sample size is small. Also, another complexity arises when the iterative estimation process encounters separation in estimating regression coefficients. In this article, we propose a method to improve the estimation of regression coefficients. In our likelihood-based method, we penalize the likelihood by multiplying it by a noninformative Jeffreys prior as a penalty term. The proposed method reduces bias and is able to handle the issue of separation. Simulation results show substantial bias reduction for the proposed method as compared to the existing method. Analyses using real world data also support the simulation findings. An R package called brlrmr is developed implementing the proposed method and the Ibrahim and Lipsitz method.     

Biases in bias elicitation

Abstract

We consider the biases that can arise in bias elicitation when expert assessors make random errors. After presenting a general framework of the phenomenon, we illustrate it for two examples: the case of omitting variables bias and that of the bias arising in adjusting relative risks. Results show that, even when assessors’ elicitations of bias have desirable properties, the nonlinear nature of biases can lead to elicitations of bias that are, themselves, biased. We show the corrections which can be made to remove this bias and discuss the implications for the applied literature which employs these methods.     

如何测度央行行长的口头沟通信息——一种基于监督学习的文本分析方法

本文以2003年1月至2018年8月中国央行行长所有口头沟通内容为文本基础，生成央行行长沟通这一特定领域的专用词典，进而使用短语数量加权的方法分别构造货币政策沟通指数和经济形势沟通指数。其中，货币政策沟通指数与实际基准利率和存款准备金率的变动具有高度相关性，而经济形势沟通指数可以作为经济基本面的信号器。进一步，本文基于监督学习方法，通过训练子样本词典得到具有倾向的短语及其概率分布，利用文本分类器对新的沟通文本进行自动分类，最终对新样本进行指数计算。子样本的监督学习与全样本信息具有一致的结果，表明本文的央行行长口头沟通测度具有可复制性和可延展性。     

关于人民币升值对治理通货膨胀积极效应的实证分析

本文通过建立经济模型,以人民币汇率、外汇储备以及国际商品价格作为变量,对人民币升值与国内通货膨胀之间关系进行了协整分析,得出了从长期看人民币升值与国内通货膨胀保持着稳定的负相关关系,即人民币升值会导致国内通货膨胀回落的结论,并根据实证分析数据,分析了人民币升值与否是当前通货膨胀生成及回落的重要原因。同时,文章提出治理外汇推动型通货膨胀,应以调整对外经济政策为主,以紧缩性货币政策为辅的政策建议。     

Deprivation analysis based on Bayesian latent class models

This article seeks to measure deprivation among Portuguese households, taking into account four well-being dimensions – housing, durable goods, economic strain and social relationships – with survey data from the European Community Household Panel. We propose a multi-stage approach to a cross-sectional analysis, side-stepping the sparse nature of the contingency tables caused by the large number of variables considered and bringing together partial and overall analyses of deprivation that are based on Bayesian latent class models via Markov Chain Monte Carlo methods. The outcomes demonstrate that there was a substantial improvement on household overall well-being between 1995 and 2001. The dimensions that most contributed to the risk of household deprivation were found to be economic strain and social relationships.     

A Comparison of Nonparametric Tests of Weak Separability for Annual and Quarterly Data on Consumption,Leisure, and Money

Among the most fundamental assumptions made in economics are utility maximization and the separability of the arguments in the representative consumer's utility function. These assumptions are important for theoretical and empirical applications of economics. In this article, we present results from nonparametric tests of these assumptions of consumer behavior. We find that utility maximization generally obtains with either annual or quarterly per capita data on consumption goods, leisure, and relatively liquid monetary assets. Annual data on consumption goods, leisure, and all monetary assets are consistent with utility maximization. There is some evidence in support of using partial adjustment models when estimating quarterly data models of the demand for monetary assets. Further, annual data on consumption goods and leisure and on liquid monetary assets meet the necessary and sufficient conditions for weak separability. These results support the notion of a monetary aggregate more broadly based than currency plus demand deposits. Separability of monetary assets does not obtain for quarterly data.     

Controlling for confounding via propensity score methods can result in biased estimation of the conditional AUC: A simulation study

In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not random in observational studies, comparisons of outcomes between exposed and nonexposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of conditional odds ratio and hazard ratio. However, research is lacking on the performance of propensity score methods for covariate adjustment when estimating the area under the ROC curve (AUC). In this paper, AUC is proposed as measure of effect when outcomes are continuous. The AUC is interpreted as the probability that a randomly selected nonexposed subject has a better response than a randomly selected exposed subject. A series of simulations has been conducted to examine the performance of propensity score methods when association between exposure and outcomes is quantified by AUC; this includes determining the optimal choice of variables for the propensity score models. Additionally, the propensity score approach is compared with that of the conventional regression approach to adjust for covariates with the AUC. The choice of the best estimator depends on bias, relative bias, and root mean squared error. Finally, an example looking at the relationship of depression/anxiety and pain intensity in people with sickle cell disease is used to illustrate the estimation of the adjusted AUC using the proposed approaches.