Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

Religion,social milieu,and the contraceptive revolution

This study examines how the social environment of religious congregations affects the spread of contraceptive use in developing contexts, using Mozambique as a case study. Analysis of qualitative data collected in urban areas of that country in 1998-99 and of the data from the 1997 Mozambique Demographic and Health Survey suggests that, in urban areas, the environment of more socioculturally diverse and inclusive Roman Catholic and mission-based Protestant congregations is more propitious to the spread and legitimization of modern contraception than the milieu of smaller, relatively homogeneous, independent churches. In rural areas, however, sociocultural diversity within and across different religious denominations is minimal, and membership in any formal congregation offers an advantage in contraceptive learning.     

辽宁省综合交通运输体系内部分工合理性分析

综合交通运输体系分工合理性研究对促进各种运输方式合理分工、优势互补、协调发展起着重要作用。以平均里程系数为指标，运用实证分析法，得出目前辽宁省综合交通运输体系内部分工比较合理、各种运输方式之间互补性较强但竞争性不明显的结论，并指出应根据辽宁省经济发展需要在综合交通运输体系内部推行非平衡发展战略。     

COVARIANCE MODELS FOR LATTICE DATA

Given observations on an m × n lattice, approximate maximum likelihood estimates are derived for a family of models including direct covariance, spatial moving average, conditional autoregressive and simultaneous autoregressive models. The approach involves expressing the (approximate) covariance matrix of the observed variables in terms of a linear combination of neighbour relationship matrices, raised to a power. The structure is such that the eigenvectors of the covariance matrix are independent of the parameters of interest. This result leads to a simple Fisher scoring type algorithm for estimating the parameters. The ideas are illustrated by fitting models to some remotely sensed data.     

A Comparison of the Performance of Phase II Simple Linear Profile Control Charts when Parameters are Estimated

The standard deviation of the average run length (SDARL) is an important performance metric in studying the performance of control charts with estimated in-control parameters. Only a few studies in the literature, however, have considered this measure when evaluating control chart performance. The current study aims at comparing the in-control performance of three phase II simple linear profile monitoring approaches; namely, those of Kang and Albin (2000), Kim et al. (2003), and Mahmoud et al. (2010). The comparison is performed under the assumption of estimated parameters using the SDARL metric. In general, the simulation results of the current study show that the method of Kim et al. (2003) has better overall statistical performance than the competing methods in terms of SDARL values. Some of the recommended approaches based solely on the usual average run length properties can have poor SDARL performance.     

我国人口预期寿命的结构及影响因素研究—基于省级面板数据的分析

基于省级面板数据,本文对预期寿命结构及其影响因素问题进行研究。首先,文章分析了我国整体预期寿命及两性预期寿命的地理分布特性,随时间的变化特点,以及与经济发展水平的关系。然后,建立预期寿命的固定效应模型,分析不同影响因素与预期寿命的关系。研究发现经济发展水平、教育和卫生资源对男性和女性预期寿命会产生不同的影响,其中经济发展水平会显著影响女性预期寿命,而教育对男性预期寿命的影响更显著。由此,经济发展水平的提高会导致两性预期寿命差距的增加,而教育水平的提高和卫生资源投入的增加则可能会降低两性预期寿命的差距。     

Effect of systematic sampling on arima models

Given a general homogeneous non-stationary autoregressive integrated moving average process ARIMA(p,d,q), the corresponding model for the subseries obtained by a systematic sampling is derived. The article then shows that the sampled subseries approaches approximately to an integrated moving average process IMA(d,l), l≤(d-l), regardless of the autoregressive and moving average structures in the original series. In particular, the sampled subseries from an ARIMA (p,l,q) process approaches approximately to a simple random walk model.     

Book Reviews

Books reviewed:
David Griffiths, W. Douglas Stirling, and K. Laurence Weldon, Understanding Data: Principles and Practice of Statistics
Ingwer Borg and Patrick Groenen, Modern Multidimensional Scaling: Theory and Applications
Jeffrey H. Dorfman, Bayesian Economics Through Numerical Methods: A Guide to Econometrics and Decision-making with Prior Information
Marek Musiela and Marek Rutkowski, Martingale Methods in Financial Modelling: Theory and Applications
Aad W. van der Vaart and Jon A. Wellner, Weak Convergence and Empirical Processes     

Single sampling plans indexed by point op control and inflection point

Three tables for selection of single sampling plans with any one of the following combinations of entry parameters are givens.

1) the indifference quality level and the average outgoing quality limit.

2) the indifference quality level with relative slope of the OC curve at that quality leve land.

3) the quality level corresponding to the inflection point with relative slope of the OC curve at that quality level.

A table enabling the transition from one set of parameters to match the OC curve of other similar sets is also given.     

A Note on Superiority Comparisons of Homogeneous Linear Estimators

This note extends some results on homogeneous linear estimators to the general, even nonlinear case.A Sufficient condition for the difference of mean square error matrices of minimum conditional mean square error estimator and minimum average risk linear estimator to be postive definite is derived.