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Wild Bootstrap Inference for Wildly Different Cluster Sizes

聚类规模悬殊时的野生自助法推断

Summary The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is…

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Measuring segregation on small units: A partial identification analysis

小单位中的群体隔离测度:部分识别分析

We consider the issue of measuring segregation in a population of small units, considering establishments in our application. Each establishment may have a different probability of hiring an individual from the minority group. We define segregation indices as inequality indices on these unobserved, random probabilities. Because these probabilities are measured with error by proportions, standard estimators are inconsistent. We model this problem as a nonparametric binomial…

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Ascending auctions with bidder asymmetries

竞买人不对称的升价拍卖

We present a partial identification approach for ascending auctions with bidder asymmetries, where bidders' asymmetric types may be unobservable to the econometrician. Our approach yields sharp bounds and builds on and generalizes other recent bounds approaches for correlated private values ascending auctions. When bidder identities are observable, our approach yields tighter bounds than previous approaches that ignore asymmetry, demonstrating that bidder asymmetries can…

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Testing for fundamental vector moving average representations

向量移动平均表示的基本性检验

We propose a test for invertibility or fundamentalness of structural vector autoregressive moving average models generated by non-Gaussian independent and identically distributed structural shocks. We prove that in these models and under some regularity conditions the Wold innovations are a martingale difference sequence (mds) if and only if the structural shocks are fundamental. This simple but powerful characterization suggests an empirical strategy to assess…

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Inference for subvectors and other functions of partially identified parameters in moment inequality models

矩不等式模型中部分识别参数的子向量及其他函数推断

This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. These functions are restricted to be linear for two-sided testing problems, but may be nonlinear for one-sided testing problems. In the most common case, this function selects a subvector of the parameter, such as a single component. The new inference method we propose controls asymptotic size uniformly over a large class of data distributions and…

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Modified marginal expected shortfall under asymptotic dependence

渐近依赖下的修正边际期望损失

We propose an estimator of the marginal expected shortfall by considering a log transformation of a variable which has an infinite expectation.We establish the asymptotic normality of our estimator under general assumptions.A simulation study suggests that the estimation procedure is robust with respect to the choice of tuning parameters.Our estimator has lower bias and mean squared error than the empirical estimator when the latter is applicable.We illustrate our…

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Extreme eigenvalues of large-dimensional spiked Fisher matrices with application

高维尖峰 Fisher 矩阵的极端特征值及其应用

Consider two $p$-variate populations, not necessarily Gaussian, with covariance matrices $\Sigma_{1}$ and $\Sigma_{2}$, respectively. Let $S_{1}$ and $S_{2}$ be the corresponding sample covariance matrices with degrees of freedom $m$ and $n$. When the difference $\Delta$ between $\Sigma_{1}$ and $\Sigma_{2}$ is of small rank compared to $p,m$ and $n$, the Fisher matrix $S:=S_{2}^{-1}S_{1}$ is called a spiked Fisher matrix. When $p,m$ and $n$ grow to infinity proportionally,…

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Flexible results for quadratic forms with applications to variance components estimation

二次型的灵活理论结果及其在方差分量估计中的应用

We derive convenient uniform concentration bounds and finite sample multivariate normal approximation results for quadratic forms, then describe some applications involving variance components estimation in linear random-effects models. Random-effects models and variance components estimation are classical topics in statistics, with a corresponding well-established asymptotic theory. However, our finite sample results for quadratic forms provide additional flexibility for…

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Normal approximation and concentration of spectral projectors of sample covariance

样本协方差谱投影的正态近似与集中性

Let $X,X_{1},\dots,X_{n}$ be i.i.d. Gaussian random variables in a separable Hilbert space $\mathbb{H}$ with zero mean and covariance operator $\Sigma=\mathbb{E}(X\otimes X)$, and let $\hat{\Sigma}:=n^{-1}\sum_{j=1}^{n}(X_{j}\otimes X_{j})$ be the sample (empirical) covariance operator based on $(X_{1},\dots,X_{n})$. Denote by $P_{r}$ the spectral projector of $\Sigma$ corresponding to its $r$th eigenvalue $\mu_{r}$ and by $\hat{P}_{r}$ the empirical counterpart of $P_{r}$.…

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Tensor decompositions and sparse log-linear models

张量分解与稀疏对数线性模型

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results…

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A general theory of hypothesis tests and confidence regions for sparse high dimensional models

稀疏高维模型中假设检验与置信域的一般理论

We consider the problem of uncertainty assessment for low dimensional components in high dimensional models. Specifically, we propose a novel decorrelated score function to handle the impact of high dimensional nuisance parameters. We consider both hypothesis tests and confidence regions for generic penalized M-estimators. Unlike most existing inferential methods which are tailored for individual models, our method provides a general framework for high dimensional inference…

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Positive semidefinite integrated covariance estimation, factorizations and asynchronicity

半正定积分协方差估计:矩阵分解与异步交易

An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization of the covariance matrix in order to exploit the heterogeneity in trading intensities to estimate the different parameters sequentially with as many observations as possible. The estimator is positive semidefinite by construction. We derive asymptotic results and confirm their good finite sample properties by means of a Monte…

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Testing for central dominance: Method and application

中心优势的检验:方法与应用

Central dominance (CD) introduced in Gollier (1995, Journal of Economic Theory) is a risk concept that differs from stochastic dominance (SD) in an important way. In particular, CD implies a deterministic comparative static of a change in decision when risk changes, but SD does not have such an implication. In this paper, we propose the first test of central dominance, which amounts to checking a functional inequality. We derive the asymptotic distribution of a lower bound…

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Unequal spacing in dynamic panel data: Identification and estimation

不等时间间隔动态面板数据的识别与估计

We propose conditions under which parameters of fixed-effect dynamic models are identified with unequally spaced panel data. Under predeterminedness, weak stationarity, and empirically testable rank conditions, AR(1) parameters are identified given the availability of “two pairs of two consecutive time gaps”, which generalizes “two pairs of two consecutive time periods”. This result extends to models with multiple covariates, higher-order autoregressions, and partial…

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Fractional order statistic approximation for nonparametric conditional quantile inference

非参数条件分位数推断的分数阶次序统计量近似

Using and extending fractional order statistic theory, we characterize the O(n−1) coverage probability error of the previously proposed (Hutson, 1999) confidence intervals for population quantiles using L-statistics as endpoints. We derive an analytic expression for the n−1 term, which may be used to calibrate the nominal coverage level to get O(n−3/2[log(n)]3) coverage error. Asymptotic power is shown to be optimal. Using kernel smoothing, we propose a related method for…

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Tests for conditional ellipticity in multivariate GARCH models

多元 GARCH 模型的条件椭圆性检验

Tests are proposed for the assumption that the conditional distribution of a multivariate GARCH process is elliptic. These tests are of Kolmogorov–Smirnov and Cramér–von Mises-type and make use of the common geometry underlying the characteristic function of any spherically symmetric distribution. The asymptotic null distribution of the test statistics as well as the consistency of the tests is investigated under general conditions. It is shown that both the finite sample…

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Inference and testing breaks in large dynamic panels with strong cross sectional dependence

强横截面相关下大规模动态面板的推断与时间断点检验

In this paper we provide a new Central Limit Theorem for estimators of the slope papers in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. We proceed by providing two related tests for breaks/homogeneity in the time dimension. The first test is based on the CUSUM principle; the second test is based on a Hausman–Durbin–Wu approach. Some of the key features of the tests are that they…

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R-estimation in semiparametric dynamic location-scale models

半参数动态位置—尺度模型的秩估计

We propose rank-based estimation (R-estimators) as an alternative to Gaussian quasi-likelihood and standard semiparametric estimation in time series models, where conditional location and/or scale depend on a Euclidean parameter of interest, while the unspecified innovation density is a nuisance. We show how to construct R-estimators achieving semiparametric efficiency at some predetermined reference density while preserving root-n consistency and asymptotic normality…

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Inference based on many conditional moment inequalities

基于大量条件矩不等式的统计推断

We construct confidence sets for models defined by many conditional moment inequalities/equalities. The number of conditional moment restrictions can be up to infinitely many. To deal with the vast number of moment restrictions, we exploit the manageability (Pollard (1990)) of the class of moment functions. We verify this condition in five examples from the recent partial identification literature.

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The impact of firm prestige on executive compensation

企业声望对高管薪酬的影响

We show that chief executive officers (CEOs) of prestigious firms earn less. Total compensation is on average 8% lower for firms listed in Fortune’s ranking of America’s most admired companies. We suggest that CEOs are willing to trade off status and career benefits from working for a publicly admired company against additional monetary compensation. Our identification strategy is based on matched sample analyses, difference-in-differences regressions, and a regression…

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SMOOTHED ESTIMATING EQUATIONS FOR INSTRUMENTAL VARIABLES QUANTILE REGRESSION

工具变量分位数回归的平滑估计方程

The moment conditions or estimating equations for instrumental variables quantile regression involve the discontinuous indicator function. We instead use smoothed estimating equations (SEE), with bandwidth h . We show that the mean squared error (MSE) of the vector of the SEE is minimized for some h > 0, leading to smaller asymptotic MSE of the estimating equations and associated parameter estimators. The same MSE-optimal h also minimizes the higher-order type I error of a…

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ON THE POWER OF INVARIANT TESTS FOR HYPOTHESES ON A COVARIANCE MATRIX

协方差矩阵假设的不变检验功效

The behavior of the power function of autocorrelation tests such as the Durbin–Watson test in time series regressions or the Cliff-Ord test in spatial regression models has been intensively studied in the literature. When the correlation becomes strong, Krämer (1985, Journal of Econometrics 28, 363–370.) (for the Durbin–Watson test) and Krämer (2005, Journal of Statistical Planning and Inference , 128, 489–496) (for the Cliff-Ord test) have shown that power can be very low,…

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DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS

具有交互固定效应的动态线性面板回归

We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the…

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IDENTIFICATION AND INFERENCE ON REGRESSIONS WITH MISSING COVARIATE DATA

协变量缺失回归模型的识别与推断

This paper examines the problem of identification and inference on a conditional moment condition model with missing data, with special focus on the case when the conditioning covariates are missing. We impose no assumption on the distribution of the missing data and we confront the missing data problem by using a worst case scenario approach. We characterize the sharp identified set and argue that this set is usually too complex to compute or to use for inference. Given…

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