论文检索

同方差线性工具变量回归中高效且水平正确的子集推断AI 解读

We show that Moreira’s (2003) conditional critical value function for likelihood ratio (LR) tests on the structural parameter in homoskedastic linear instrumental variables (IV) regression provides a bounding critical value function for subset LR tests on one structural parameter of several for general homoskedastic linear IV regression. The resulting subset LR test is size correct under weak identification and efficient under strong identification. A power study shows that…

工具变量与局部投影工具变量模型中偏误和检验尺寸扭曲的置信区间AI 解读

In this article, we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models with heteroscedasticity and serial correlation. Importantly our framework covers the local projections—instrumental variable model as well. Unlike tests for weak instruments, whose distributions are nonstandard and depend on nuisance parameters that cannot be…

主分层框架下的鼓励、处理与溢出效应：青少年博物馆参观实验AI 解读

This article revisits results from a field experiment, conducted in Florence, Italy, to study the effects of incentives provided to high school teens to motivate them to visit art museums. In the experiment, different classes of students were randomized to three types of encouragement and were offered a free visit to a main museum in the city. Using the principal stratification framework, the article explores causal pathways that may lead students to increase future visits,…

半参数模型的弱识别理论AI 解读

We provide general formulation of weak identification in semiparametric models and an efficiency concept. Weak identification occurs when a parameter is weakly regular, that is, when it is locally homogeneous of degree zero. When this happens, consistent or equivariant estimation is shown to be impossible. We then show that there exists an underlying regular parameter that fully characterizes the weakly regular parameter. While this parameter is not unique, concepts of…

GMM 得分检验：为何使用隐含概率？AI 解读

While simple to implement and thus attractive in practice, the GMM score test of Newey and West (1987) often displays upward size distortion under common scenarios involving skewed moment vectors or models with weak identification. Inference based on the Generalized Empirical Likelihood (GEL) is seen as a general solution to this problem. Kleibergen (2005) and, more generally, Guggenberger and Smith (2005) devised an elegant theory for the GEL score tests. However, strictly…

外生性检验、不完全模型、弱识别与非高斯分布：不变性和有限样本分布理论AI 解读

We study the distribution of Durbin–Wu–Hausman (DWH) and Revankar–Hartley (RH) tests for exogeneity from a finite-sample viewpoint, under the null and alternative hypotheses. We consider linear structural models with possibly non-Gaussian errors, where structural parameters may not be identified and where reduced forms can be incompletely specified (or nonparametric). On level control, we characterize the null distributions of all the test statistics. Through conditioning…

识别强度检验AI 解读

We consider models defined by a set of moment restrictions that may be subject to weak identification. We propose a testing procedure to assess whether instruments are “too weak” for standard (Gaussian) asymptotic theory to be reliable. Since the validity of standard asymptotics for GMM rests upon a Taylor expansion of the first order conditions, we distinguish two cases: (i) models that are either linear or separable in the parameters of interest; (ii) general models that…

二阶识别模型中的推断AI 解读

We explore the local power properties of different test statistics for conducting inference in moment condition models that only identify the parameters locally to second order. We consider the conventional Wald and LM statistics, and also the Generalized Anderson–Rubin (GAR) statistic (Anderson and Rubin, 1949; Dufour, 1997; Staiger and Stock, 1997; Stock and Wright, 2000), KLM statistic (Kleibergen, 2002; Kleibergen, 2005) and the GMM extension of Moreira (2003) (GMM-M)…

可能弱识别下的渐近 F 检验AI 解读

This paper develops asymptotic F tests robust to weak identification and temporal dependence. The test statistics we focus on are modified versions of the S statistic of Stock and Wright (2000) and the K statistic of Kleibergen (2005). In the former case, the modification involves only a multiplicative degree-of-freedom adjustment, and the modified S statistic is asymptotically F distributed under fixed-smoothing asymptotics regardless of the strength of the model…

重新审视杠杆效应之谜：离散时间中的识别AI 解读

The term “leverage effect,” as coined by Black (1976), refers to the tendency of an asset’s volatility to be negatively correlated with the asset’s return. Aït-Sahalia et al. (2013) refer to the “leverage effect puzzle” as the fact that, in spite of a broad agreement that the effect should be present, it is hard to identify empirically. For this purpose, we propose an extension with leverage effect of the discrete time stochastic volatility model of Darolles et al. (2006).…

弱可识别性下的主成分方向检验AI 解读

We consider the problem of testing, on the basis of a $p$-variate Gaussian random sample, the null hypothesis $\mathcal{H}_{0}:\boldsymbol{\theta}_{1}=\boldsymbol{\theta}_{1}^{0}$ against the alternative $\mathcal{H}_{1}:\boldsymbol{\theta}_{1}\neq \boldsymbol{\theta}_{1}^{0}$, where $\boldsymbol{\theta}_{1}$ is the “first” eigenvector of the underlying covariance matrix and $\boldsymbol{\theta}_{1}^{0}$ is a fixed unit $p$-vector. In the classical setup where eigenvalues $\lambda_{1}>\lambda_{2}\geq \cdots \geq \lambda_{p}$…

长期限制结构向量自回归中的稳健推断AI 解读

Long-run restrictions are a very popular method for identifying structural vector autoregressions, but they suffer from weak identification when the data is very persistent, i.e., when the highest autoregressive roots are near unity. Near unit roots introduce additional nuisance parameters and make standard weak-instrument-robust methods of inference inapplicable. We develop a method of inference that is robust to both weak identification and strong persistence. The method…

弱识别下三角联立方程模型的非参数估计AI 解读

This paper analyzes the problem of weak instruments on identification, estimation, and inference in a simple nonparametric model of a triangular system. The paper derives a necessary and sufficient rank condition for identification, based on which weak identification is established. Then nonparametric weak instruments are defined as a sequence of reduced‐form functions where the associated rank shrinks to zero. The problem of weak instruments is characterized as concurvity ,…

不连续与折点激励分配机制下加权平均处理效应的识别及小波估计AI 解读

This paper studies identification, estimation, and inference of a weighted average treatment effect (W-ATE) parameter in a class of switching regime models, where the agent’s selection of treatment is affected by either a discontinuous or kink incentive assignment mechanism and some unobservable characteristic. For each assignment mechanism, we (i) establish identification and propose a local wavelet estimator of the W-ATE; (ii) establish asymptotic properties of the local…

用野生自助法改善聚类工具变量模型推断的两个应用AI 解读

Summary Microeconomic data often have within‐cluster dependence, which affects standard error estimation and inference. When the number of clusters is small, asymptotic tests can be severely oversized. In the instrumental variables (IV) model, the potential presence of weak instruments further complicates hypothesis testing. We use wild bootstrap methods to improve inference in two empirical applications with these characteristics. Building from estimating equations and…

线性工具变量回归中更有功效的子向量 Anderson–Rubin 检验AI 解读

We study subvector inference in the linear instrumental variables model assuming homoskedasticity but allowing for weak instruments. The subvector Anderson and Rubin (1949) test that uses chi square critical values with degrees of freedom reduced by the number of parameters not under test, proposed by Guggenberger, Kleibergen, Mavroeidis, and Chen (2012), controls size but is generally conservative. We propose a conditional subvector Anderson and Rubin test that uses…

条件同方差下子集Anderson–Rubin检验的Bootstrap不一致与Bonferroni尺寸校正AI 解读

We focus on the linear instrumental variable model with two endogenous regressors under conditional homoskedasticity, and study the subset Anderson and Rubin (1949, AR) test when the nuisance structural parameter, the unrestricted slope coefficient of endogenous regressor, may be weakly identified. Weak identification leads to nonstandard null limiting distributions, and alternative to the usual chi-squared critical value is needed. We first investigate the bootstrap…

In models with potentially weak identification, researchers often decide whether to report a robust confidence set based on an initial assessment of model identification. Two-step procedures of this sort can generate large coverage distortions for reported confidence sets, and existing procedures for controlling these distortions are quite limited. This paper introduces a generally applicable approach to detecting weak identification and constructing two-step confidence sets…

两样本工具变量回归中的弱工具稳健推断AI 解读

Summary Instrumental variable (IV) methods for regression are well established. More recently, methods have been developed for statistical inference when the instruments are weakly correlated with the endogenous regressor, so that estimators are biased and no longer asymptotically normally distributed. This paper extends such inference to the case where two separate samples are used to implement instrumental variables estimation. We also relax the restrictive assumptions of…

准贝叶斯模型选择AI 解读

In this paper, we establish the consistency of the model selection criterion based on the quasi‐marginal likelihood (QML) obtained from Laplace‐type estimators. We consider cases in which parameters are strongly identified, weakly identified and partially identified. Our Monte Carlo results confirm our consistency results. Our proposed procedure is applied to select among New Keynesian macroeconomic models using US data.

多工具变量下由约化式进行结构推断AI 解读

This paper develops exact finite sample and asymptotic distributions for structural equation tests based on partially restricted reduced form estimates. Particular attention is given to models with large numbers of instruments, wherein the use of partially restricted reduced form estimates is shown to be especially advantageous in statistical testing even in cases of uniformly weak instruments. Comparisons are made with methods based on unrestricted reduced forms, and…

已知第一阶段符号时的无偏工具变量估计AI 解读

We derive mean-unbiased estimators for the structural parameter in instrumental variables models with a single endogenous regressor where the sign of one or more first-stage coefficients is known. In the case with a single instrument, there is a unique nonrandomized unbiased estimator based on the reduced-form and first-stage regression estimates. For cases with multiple instruments we propose a class of unbiased estimators and show that an estimator within this class is…

DSGE 模型的脉冲响应匹配估计AI 解读

The existing asymptotic theory for VAR-based impulse response matching estimators of the structural parameters of DSGE models does not cover situations in which the number of impulse responses exceeds the number of VAR model parameters. We establish the consistency of the estimator in this situation, we derive its asymptotic distribution, and we show how this distribution can be approximated by bootstrap methods. We also demonstrate that under our assumptions special care is…

若干弱可识别有限混合模型的参数估计收敛速率AI 解读

We establish minimax lower bounds and maximum likelihood convergence rates of parameter estimation for mean-covariance multivariate Gaussian mixtures, shape-rate Gamma mixtures and some variants of finite mixture models, including the setting where the number of mixing components is bounded but unknown. These models belong to what we call “weakly identifiable” classes, which exhibit specific interactions among mixing parameters driven by the algebraic structures of the class…