Analysis of a continuous-time proportional hazard model using discrete duration data

Many economic duration variables are often available only up to intervals, and not up to exact points. However, continuous time duration models are conceptually superior to discrete ones. Hence, in duration analyses, one faces a situation with discrete data and a continuous model. This paper discusses (i) the asymptotic bias of a conventional approximation procedure in which a discrete duration is treated as an exact observation; and (ii) the efficiency of a correct maximum likelihood estimator which appropriately accounts for the discrete nature of the data.     

Direct and Indirect Causal Effects via Potential Outcomes*

Abstract. The use of the concept of ‘direct’ versus ‘indirect’ causal effects is common, not only in statistics but also in many areas of social and economic sciences. The related terms of ‘biomarkers’ and ‘surrogates’ are common in pharmacological and biomedical sciences. Sometimes this concept is represented by graphical displays of various kinds. The view here is that there is a great deal of imprecise discussion surrounding this topic and, moreover, that the most straightforward way to clarify the situation is by using potential outcomes to define causal effects. In particular, I suggest that the use of principal stratification is key to understanding the meaning of direct and indirect causal effects. A current study of anthrax vaccine will be used to illustrate ideas.     

Adaptive estimation of the conditional cumulative distribution function from current status data

Consider a positive random variable of interest Y depending on a covariate X, and a random observation time T independent of Y given X. Assume that the only knowledge available about Y is its current status at time T  : δ=_I{Y≤T}$\delta ={\mathbb{I}}_{\{Y\le T\}}$ with I$\mathbb{I}$ the indicator function. This paper presents a procedure to estimate the conditional cumulative distribution function F of Y given X   from an independent identically distributed sample of (X,T,δ)$(X,T,\delta )$.     

变量误差模型下的代际收入弹性估计：一个文献综述

 代际收入流动性反映了社会的机会平等与经济公正程度,为收入分配研究提供了动态视角,主要由代际收入弹性来度量。对代际收入弹性的估计,主流方法都是建立在变量误差模型基础上的。随着新数据集的使用,研究者对收入动态方程假设进行了修正,从而推动了变量误差模型的发展,反过来变量误差模型的发展促进了代际收入弹性的一致性估计。     

各省区季度GDP数据质量评估

  国内外学者对我国GDP数据质量的质疑重点已从年度数据转到季度数据,从全国数据转向地方数据。本文通过设计一套较为系统且可操作性强的季度GDP评估指标体系,运用空间面板数据模型对各省区的季度GDP数据质量进行了实证检验。结果表明,整体来看,中国各省区季度GDP同各经济指标的匹配性较好,数据质量较高,并不存在明显的失真现象;从时间上来看,每年一、二季度的GDP存在一定程度的高估,而每年三、四季度的GDP则存在一定程度的低估,但是这种偏差在统计上不显著;分地区来看,尽管一半省区的季度GDP存在一定程度的高估,另一半省区存在一定程度的低估,但大部分省区高估或低估的程度在统计上不显著。文章进一步分析了其中的原因。     

Model averaging for M-estimation

M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors or outliers in the response. Under general conditions, we develop robust versions of the focused information criterion and a frequentist model average estimator for M-estimation, and we examine their theoretical properties. In addition, we carry out extensive simulation studies as well as two real examples to assess the performance of our new procedure, and find that the proposed method produces satisfactory results.     

Penalized Maximum Likelihood Principle for Choosing Ridge Parameter

We consider the problem of choosing the ridge parameter. Two penalized maximum likelihood (PML) criteria based on a distribution-free and a data-dependent penalty function are proposed. These PML criteria can be considered as “continuous” versions of AIC. A systematic simulation is conducted to compare the suggested criteria to several existing methods. The simulation results strongly support the use of our method. The method is also applied to two real data sets.     

Robust methods in econometrics

This survey of recent developments in robust estimation and inference is directed primarily toward econometricians. It is argued that many of the techniques in common use in econometrics are highly sensitive to unverified hypotheses. Recent progress in designing alternative robust procedures is described and some prospects for future developments are discussed.     

Comment

We propose a sequential test for predictive ability for recursively assessing whether some economic variables have explanatory content for another variable. In the forecasting literature it is common to assess predictive ability by using “one-shot” tests at each estimation period. We show that this practice leads to size distortions, selects overfitted models and provides spurious evidence of in-sample predictive ability, and may lower the forecast accuracy of the model selected by the test. The usefulness of the proposed test is shown in well-known empirical applications to the real-time predictive content of money for output and the selection between linear and nonlinear models.     

Analysis of a continuous-time proportional hazard model using discrete duration data

Many economic duration variables are often available only up to intervals, and not up to exact points. However, continuous time duration models are conceptually superior to discrete ones. Hence, in duration analyses, one faces a situation with discrete data and a continuous model. This paper discusses (i) the asymptotic bias of a conventional approximation procedure in which a discrete duration is treated as an exact observation; and (ii) the efficiency of a correct maximum likelihood estimator which appropriately accounts for the discrete nature of the data.