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SIMPLE, ROBUST, AND POWERFUL TESTS OF THE BREAKING TREND HYPOTHESIS

趋势断裂假说的简便、稳健且有力检验

In this paper we develop a simple procedure that delivers tests for the presence of a broken trend in a univariate time series that do not require knowledge of the form of serial correlation in the data and are robust as to whether the shocks are generated by an I (0) or an I (1) process. Two trend break models are considered: the first holds the level fixed while allowing the trend to break, while the latter allows for a simultaneous break in level and trend. For the known…

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EXACT DISTRIBUTION THEORY IN STRUCTURAL ESTIMATION WITH AN IDENTITY

含恒等式结构模型估计的精确分布理论

Some exact distribution theory is developed for structural equation models with and without identities. The theory includes LIML, IV, and OLS. We relate the new results to earlier studies in the literature, including the pioneering work of Bergstrom (1962). General IV exact distribution formulas for a structural equation model without an identity are shown to apply also to models with an identity by specializing along a certain asymptotic parameter sequence. Some of the new…

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ON DISCRETE SAMPLING OF TIME-VARYING CONTINUOUS-TIME SYSTEMS

时变连续时间系统的离散采样

We consider a multivariate continuous-time process, generated by a system of linear stochastic differential equations, driven by white noise, and involving coefficients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points (though equal spacing significantly simplifies matters). Such settings represent partial extensions of ones studied extensively by A.R. Bergstrom. A model for the observed time series is…

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EXACT PROPERTIES OF THE CONDITIONAL LIKELIHOOD RATIO TEST IN AN IV REGRESSION MODEL

工具变量回归中条件似然比检验的精确性质

For a simplified structural equation/IV regression model with one right-side endogenous variable, we derive the exact conditional distribution function of Moreira's (2003) conditional likelihood ratio (CLR) test statistic. This is used to obtain the critical value function needed to implement the CLR test, and reasonably comprehensive graphical versions of this function are provided for practical use. The analogous functions are also obtained for the case of testing more…

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Testing for cointegration using the Johansen approach: are we using the correct critical values?

使用 Johansen 方法检验协整:临界值是否选对了?

Abstract This paper presents Monte Carlo simulations for the Johansen cointegration test which indicate that the critical values applied in a number of econometrics software packages are inappropriate. This is due to confusion in the specification of the deterministic terms included in the vector error correction model (VECM). The result is a tendency to reject the null of no cointegration too often. Copyright © 2009 John Wiley & Sons, Ltd.

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The optimal choice of moments in dynamic panel data models

动态面板数据模型中矩条件的最优选择

This paper derives an approximation of the mean square error (MSE) of the GMM estimator in dynamic panel data models. The approximation is based on higher-order asymptotic theory under double asymptotics. While first-order theory under double asymptotics provides information about the bias, it does not provide enough information about the variance of the estimator. Higher-order theory enables us to obtain information about the variance. From this result, a procedure for…

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Estimating deterministic trends with an integrated or stationary noise component

含单整或平稳噪声成分时确定性趋势的估计

We propose a test for the slope of a trend function when it is a priori unknown whether the series is trend-stationary or contains an autoregressive unit root. The procedure is based on a Feasible Quasi Generalized Least Squares method from an AR(1) specification with parameter [alpha], the sum of the autoregressive coefficients. The estimate of [alpha] is the OLS estimate obtained from an autoregression applied to detrended data and is truncated to take a value 1 whenever…

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Minimax regret treatment choice with finite samples

有限样本下的最小最大遗憾处理选择

This paper applies the minimax regret criterion to choice between two treatments conditional on observation of a finite sample. The analysis is based on exact small sample regret and does not use asymptotic approximations or finite-sample bounds. Core results are: (i) Minimax regret treatment rules are well approximated by empirical success rules in many cases, but differ from them significantly-both in terms of how the rules look and in terms of maximal regret incurred-for…

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Local structural quantile effects in a model with a nonseparable control variable

具有非可分控制变量模型中的局部结构分位数效应

I consider a semiparametric version of the nonseparable triangular model of Chesher [Chesher, A., 2003. Identification in nonseparable models. Econometrica 71, 1405-1441]. The proposed model is linear in coefficients, where the coefficients are unknown functions of unobserved latent variables. Using a control variable idea and quantile regression methods, I propose a simple two-step estimator for the coefficients evaluated at particular values of the latent variables. Under…

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A specification test for the propensity score using its distribution conditional on participation

利用参与条件分布检验倾向得分设定

Propensity score matching has become a popular method for the estimation of average treatment effects. In empirical applications, researchers almost always impose a parametric model for the propensity score. This practice raises the possibility that the model for the propensity score is misspecified and therefore the propensity score matching estimator of the average treatment effect may be inconsistent. We show that the common practice of calculating estimates of the…

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Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects

This paper empirically assesses the wage effects of the Job Corps program, one of the largest federally funded job training programs in the U.S. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied microeconometric research. Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program. This paper…

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Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve

GMM 中的弱工具变量稳健检验与新凯恩斯菲利普斯曲线

We discuss weak instrument robust statistics in GMM for testing hypotheses on the full parameter vector or on subsets of the parameters. We use these test procedures to reexamine the evidence on the new Keynesian Phillips curve model. We find that U.S. postwar data are consistent with the view that inflation dynamics are predominantly forward-looking, but we cannot rule out the presence of considerable backward-looking dynamics. Moreover, the Phillips curve has become…

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Comment

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Comment on "Weak instrument robust tests in GMM and the new Keynesian Phillips curve", Frank Kleibergen, Sophocles Mavroeidis. Journal of Business and Economic Statistics. July 1, 2009, 27(3): 293-311. doi:10.1198/jbes.2009.08280.

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Testing for Shifts in Trend With an Integrated or Stationary Noise Component

含单整或平稳噪声的趋势变化检验

We consider testing for structural changes in the trend function of a time series without any prior knowledge of whether the noise component is stationary or integrated. Following Perron and Yabu (2009), we consider a quasi-feasible generalized least squares procedure that uses a super-efficient estimate of the sum of the autoregressive parameters αwhen α=1. This allows tests of basically the same size with stationary or integrated noise regardless of whether the break is…

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Robust Nonparametric Quantile Estimation of Efficiency and Productivity Change in U.S. Commercial Banking, 1985–2004

美国商业银行效率与生产率变化的稳健非参数分位数估计:1985—2004年

This article uses a new nonparametric, unconditional, hyperbolic order-αquantile estimator to construct a hyperbolic version of the Malmquist index. Unlike traditional nonparametric efficiency estimators, the new estimator is both robust to data outliers and has a root-n convergence rate. We use this estimator to examine changes in the efficiency and productivity of U.S. banks between 1985 and 2004. We find that larger banks experienced larger efficiency and productivity…

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On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies

倾向得分模型的设定:贸易政策分析中的应用

The use of propensity score methods for program evaluation with nonexperimental data typically requires that the propensity score be estimated, often with a model whose specification is unknown. Although theoretical results suggest that estimators using more flexible propensity score specifications perform better, this has not filtered into applied research. Here we provide Monte Carlo evidence indicating benefits of overspecifying the propensity score that are robust across…

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A note on non-parametric estimation with predicted variables

含预测变量的非参数估计札记

This article gives the asymptotic properties of non-parametric kernel-based density and regression estimators when one of the variables is predicted. Such variables, also known as "constructed variables" or "generated predictors", occur quite frequently in econometric and applied economic analysis. The impact of using predicted rather than observed values on the properties of estimators has been extensively studied in the fully parametric context. The results derived here…

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Panel unit root tests in the presence of cross-sectional dependence: finite sample performance and an application

横截面相关下的面板单位根检验:有限样本表现与应用

This paper examines the finite sample properties of three testing regimes for the null hypothesis of a panel unit root against stationary alternatives in the presence of cross-sectional correlation. The regimes of Bai and Ng (2004), Moon and Perron (2004) and Pesaran (2007) are assessed in the presence of multiple factors and also other non-standard situations. The behaviour of some information criteria used to determine the number of factors in a panel is examined and new…

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Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models

非线性动态矩条件模型的分块广义经验似然推断

This paper shows how the blockwise generalized empirical likelihood method can be used to obtain valid asymptotic inference in non-linear dynamic moment conditions models for possibly non-stationary weakly dependent stochastic processes. The results of this paper can be used to construct test statistics for overidentifying moment restrictions, for additional moments, and for parametric restrictions expressed in mixed implicit and constraint form. Monte Carlo simulations seem…

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Non-parametric regression with a latent time series

含潜在时间序列的非参数回归

In this paper we investigate a class of semi-parametric models for panel data sets where the cross-section and time dimensions are large. Our model contains a latent time series that is to be estimated and perhaps forecasted along with a non-parametric covariate effect. Our model is motivated by the need to be flexible with regard to the functional form of covariate effects but also the need to be practical with regard to forecasting of time series effects. We propose…

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Saddlepoint approximation for mixture models

混合模型的鞍点近似

Two-component mixture distributions with one component a point mass and the other a continuous density may be used as priors for Bayesian inference when sparse representation of an underlying signal is required. We show how saddlepoint approximation in such models can yield highly accurate quantiles for posterior distributions, and illustrate this numerically, using wavelet regression with point mass/Laplace and point mass/normal prior distributions.

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Hierarchically penalized Cox regression with grouped variables

具有分组变量的层级惩罚 Cox 回归

In many biological and other scientific applications, predictors are often naturally grouped. For example, in biological applications, assayed genes or proteins are grouped by biological roles or biological pathways. When studying the dependence of survival outcome on these grouped predictors, it is desirable to select variables at both the group level and the within-group level. In this article, we develop a new method to address the group variable selection problem in the…

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Nonparametric additive regression for repeatedly measured data

重复测量数据的非参数加性回归

We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for…

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Double block bootstrap confidence intervals for dependent data

依赖数据的双重分块自助法置信区间

The block bootstrap confidence interval for dependent data can outperform the conventional normal approximation only with nontrivial studentization which, in the case of complicated statistics, calls for specialist treatment and often results in unstable endpoints. We propose two double block bootstrap approaches for improving the accuracy of the block bootstrap confidence interval under very general conditions. The first approach calibrates the nominal coverage level and…

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