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信息技术外包与企业生产率：消除因变量选择性缺失带来的偏差AI 解读

Summary Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic…

重叠有限时有限总体处理效应的推断AI 解读

Summary This paper studies inference on finite-population average and local average treatment effects under limited overlap, meaning that some strata have a small proportion of treated or untreated units. We model limited overlap in an asymptotic framework, sending the propensity score to zero (or one) with the sample size. We derive the asymptotic distribution of analogue estimators of the treatment effects under two common randomization schemes: conditionally independent…

动态面板模型的初始条件：组内方程与组间方程AI 解读

Summary This paper investigates the quasi-maximum likelihood estimation of short dynamic panel data models. We consider their estimation on both fixed effects and random effects specifications and propose a Hausman test when exogenous variables are present. For a dynamic panel model, initial conditions play important roles in model structure and estimation, and they give rise to a between equation under the random effects framework. With the between equation properly…

重新审视面板数据分位数回归的简便方法AI 解读

Summary This note discusses two errors in the approach proposed in Canay (2011) for constructing a computationally simple two-step estimator in a quantile regression model with quantile-independent fixed effects. Firstly, we show that Canay’s assumption about n/Ts → 0 for some s > 1 is not strong enough and can entail severe bias or even the non-existence of the limiting distribution for the estimator of the vector of coefficients. The condition n/T → 0 appears to be closer…

面板二元响应模型识别的脆弱性AI 解读

Summary The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various assumptions that are used to establish conditions for identification. The paper also shows that even for this simple model, it is always possible—including in the logistic case—to find a distribution of the random effects given the exogenous variables, such that the…

时变系数面板模型中的潜在分组结构估计AI 解读

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients…

基于分位数的平滑转换风险价值估计AI 解读

Summary Value at risk models are concerned with the estimation of conditional quantiles of a time series. Formally, these quantities are a function of conditional volatility and the respective quantile of the innovation distribution. The former is often subject to asymmetric dynamic behaviour, e.g., with respect to past shocks. In this paper, we propose a model in which conditional quantiles follow a generalised autoregressive process governed by two parameter regimes with…

引导式非参数拟合优度检验及其收入分布应用AI 解读

Summary We have developed a customizable goodness-of-fit test of a parametric density based on its distance to a consistently estimated density. This consistent estimate is obtained via a nonparametric density estimator with a parametric start, wherein the start is set to be the hypothesized parametric density. To cope with the influence of nonparametric estimation bias, nonparametric goodness-of-fit tests have resorted to remedies such as undersmoothing or convolution of…

面板数据模型中不同个体效应的分离AI 解读

Summary In this paper we consider a panel data model with individual effects that are arbitrarily correlated with the explanatory variables. The effects are composed as the sum of two different interpretable components, such as inefficiency versus heterogeneity in a production frontier setting, or ability versus socioeconomic background in an earnings function, or genetics versus environment in an epidemiological analysis. We wish to predict the two components separately.…

一种新不平等指标的统计推断AI 解读

Summary In recent decades, substantial changes have been observed in the left and right tails of income distributions in countries like the USA, Germany, the UK, and France. These changes are a major concern for policy makers. Here, we derive inferential results for a new inequality index that is specifically designed for capturing such significant shifts. We propose two empirical estimators for the index and show that they are asymptotically equivalent. Afterward, we adopt…

向量时间序列共线性检验AI 解读

Summary We investigate the collinearity of vector time series in the frequency domain, by examining the rank of the spectral density matrix at a given frequency of interest. Rank reduction corresponds to collinearity at the given frequency. When the time series is nonstationary and has been differenced to stationarity, collinearity corresponds to co-integration at a particular frequency. We examine rank through the Schur complements of the spectral density matrix, testing…

协变量在合成控制法中的作用AI 解读

Summary Abadie et al. (2010) derive bounds on the bias of the synthetic control estimator under a perfect balance assumption on both observed covariates and pre-treatment outcomes. In the absence of a perfect balance on covariates, we show that it is still possible to derive such bounds, albeit at the expense of relying on stronger assumptions about the effects of observed and unobserved covariates and of generating looser bounds. We also show that a perfect balance on…

基于折叠凹惩罚回归的高维宏观预测与变量选择AI 解读

Summary This study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically, we show the oracle inequality with ultra-high-dimensional time-dependent…

结构变化下滤波序列的相关系数恒定性检验AI 解读

Summary The paper proposes a test for constant correlations that allow for breaks at unknown times in the marginal means and variances. Theoretically and in an application to US and German stock returns, we find that not accounting for changes in the marginal moments has severe consequences. This is because incorrect standardization of the series transfers to the sample correlations onto which the tests are built. Correcting for variance breaks at unknown time will have an…

作为最小距离估计的两阶段最小二乘AI 解读

SummaryThe two-stage least-squares (2SLS) instrumental-variables (IV) estimator for the parameters in linear models with a single endogenous variable is shown to be identical to an optimal minimum-distance (MD) estimator based on the individual instrument-specific IV estimators. The 2SLS estimator is a linear combination of the individual estimators, with the weights determined by their variances and covariances under conditional homoskedasticity. It is further shown that…

中度爆炸性的检验AI 解读

Summary This paper considers a moderately explosive AR(1) process where the autoregressive root approaches unity from the right at a certain rate. We first develop a test for the null of moderate explosiveness under independent and identically distributed errors. We show that the t statistic is asymptotically standard normal regardless of whether the true process is dominated by the stochastic moderately explosive trend or the deterministic nonlinear drift trend. This result…

引入协变量的最优面板单位根检验AI 解读

Summary This paper provides asymptotic optimality results for panel unit root tests with covariates by deriving the Gaussian power envelope. The main conclusion is that the use of covariates holds considerable promise in the panel data context, much more so than in the time series context. In fact, the use of the covariates not only leads to increased power, but can actually have an order effect on the shrinking neighbourhoods around unity for which power is non-negligible.

使用 L1,2 范数估计图模型AI 解读

Gaussian graphical models are recently used in economics to obtain networks of dependence among agents. A widely used estimator is the graphical least absolute shrinkage and selection operator (GLASSO), which amounts to a maximum likelihood estimation regularized using the L1,1 matrix norm on the precision matrix Ω. The L1,1 norm is a LASSO penalty that controls for sparsity, or the number of zeros in Ω. We propose a new estimator called structured GLASSO (SGLASSO) that uses…

确定性季节性与季节均值变化的稳健检验AI 解读

We develop tests for the presence of deterministic seasonal behaviour and seasonal mean shifts in a seasonally observed univariate time series. These tests are designed to be asymptotically robust to the order of integration of the series at both the zero and seasonal frequencies. Motivated by the approach of Hylleberg, Engle, Granger and Yoo, we base our approach on linear filters of the data that remove any potential unit roots at the frequencies not associated with the…

超越“近似外生”：工具变量排除限制的敏感性分析AI 解读

We synthesize two recent advances in the literature on instrumental variable (IV) estimation that test and relax the exclusion restriction. Our approach first estimates the direct effect of the IV on the outcome in a subsample for which the IV does not affect the treatment variable. Subsequently, this estimate for the direct effect is used as input for the plausibly exogenous method developed by Conley, Hansen and Rossi. This two‐step procedure provides a novel and informed…

随机试验存在选择性参与时处理效应的识别AI 解读

Randomized trials (RTs) are used to learn about treatment effects. This paper studies identification of average treatment response (ATR) and average treatment effect (ATE) from RT data under various assumptions. The focus is the problem of external validity of the RT. RT data need not point identify the ATR or ATE because of selective participation in the RT. The paper reports partial‐identification and point‐identification results for the ATR and ATE based on RT data under…

重复博弈策略的非参数贝叶斯推断AI 解读

Inferring underlying cooperative and competitive strategies from human behaviour in repeated games is important for accurately characterizing human behaviour and understanding how people reason strategically. Finite automata, a bounded model of computation, have been extensively used to compactly represent strategies for these games and are a standard tool in game theoretic analyses. However, inference over these strategies in repeated games is challenging since the number…

含内生虚拟解释变量的异方差二元选择模型：识别与估计AI 解读

In this paper, we consider the semiparametric identification and estimation of a heteroscedastic binary choice model with endogenous dummy regressors and no parametric restriction on the distribution of the error term. Our approach addresses various drawbacks associated with previous estimators proposed for this model. It allows for: general multiplicative heteroscedasticity in both selection and outcome equations; a nonparametric selection mechanism; and multiple discrete…

波动率变化检验AI 解读

In this paper, we propose a consistent U‐statistic test with good sampling properties to detect changes in volatility. We show that the test has a limiting standard normal distribution under the null hypothesis, and that it is powerful compared with various alternatives. A Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural changes in volatility. An empirical example is examined to demonstrate the…