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强横截面相关下大规模动态面板的推断与时间断点检验AI 解读

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…

含固定效应与横截面依赖的分数阶单整面板估计AI 解读

We consider a large N,T heterogeneous panel data model with fixed effects, common factors allowing for cross-section dependence, and persistent data and errors, which are assumed fractionally integrated. We propose individual and common-correlation estimates for the slope parameters while error memory parameters are estimated from regression residuals. The individual parameter estimates are all T consistent, asymptotically normal and mutually uncorrelated, irrespective of…

具有交互固定效应的动态线性面板回归AI 解读

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…

Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. The discrete instrumental variable model has testable implications for the law of the observed data. However, current assessments of instrumental validity are typically based solely on subject-matter arguments rather than these testable implications, partly due to a lack of formal statistical tests with known properties. In this paper, we develop simple procedures…

因子模型的Bootstrap预测区间AI 解读

We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the innovations. The bootstrap…

收入动态中的未观测异质性：经验贝叶斯视角AI 解读

Empirical Bayes methods for Gaussian compound decision problems involving longitudinal data are considered. The new convex optimization formulation of the nonparametric (Kiefer–Wolfowitz) maximum likelihood estimator for mixture models is employed to construct nonparametric Bayes rules for compound decisions. The methods are first illustrated with some simulation examples and then with an application to models of income dynamics. Using panel data, we estimate a simple…

Summary We investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Specifically, we may suppose that the observed individuals can be grouped into a number of classes whose members all share the same regression function. We develop a statistical procedure to estimate the unknown group structure from the data.…

含固定效应的变系数面板模型：理论与美国商业银行应用AI 解读

In this paper, we propose a semiparametric varying-coefficient categorical panel data model in which covariates (variables affecting the coefficients) are purely categorical. This model has two features: first, fixed effects are included to allow for correlation between individual unobserved heterogeneity and the regressors; second, it allows for cross-sectional dependence through a general spatial error dependence structure. We derive a semiparametric estimator for our…

跨国债务执行、企业投资与风险承担AI 解读

We argue that the prospect of an imperfect enforcement of debt contracts in default reduces shareholder–debtholder conflicts and induces leveraged firms to invest more and take on less risk as they approach financial distress. To test these predictions, we use a large panel of firms in 41 countries with heterogeneous debt enforcement characteristics. Consistent with our model, we find that the relation between debt enforcement and firms’ investment and risk depends on the…

含噪数据下贫困转移矩阵的估计AI 解读

Summary This paper investigates measurement error biases in estimated poverty transition matrices. We compare transition matrices based on survey expenditure data to transition matrices based on measurement‐error‐free simulated expenditure. The simulation model uses estimates that correct for measurement error in expenditure. We find that time‐varying measurement error in expenditure data magnifies economic mobility. Roughly 45 % of households initially in poverty at time t…

具有度异质性的网络形成计量模型AI 解读

I introduce a model of undirected dyadic link formation which allows for assortative matching on observed agent characteristics (homophily) as well as unrestricted agent‐level heterogeneity in link surplus (degree heterogeneity). Like in fixed effects panel data analyses, the joint distribution of observed and unobserved agent‐level characteristics is left unrestricted. Two estimators for the (common) homophily parameter, β0, are developed and their properties studied under…

劳动收入与消费动态：非线性面板数据框架AI 解读

We develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption.Log-earnings are the sum of a general Markovian persistent component and a transitory innovation.The persistence of past shocks to earnings is allowed to vary according to the size and sign of the current shock.Consumption is modeled as an age-dependent nonlinear function of assets and the two earnings components.We establish the…

具有门槛效应与内生性的动态面板模型AI 解读

This paper addresses an important issue of modeling nonlinear asymmetric dynamics and unobserved individual heterogeneity in the threshold panel data framework, simultaneously. As a general approach, we develop the first-differenced GMM estimator, which allows both threshold variable and regressors to be endogenous. When the threshold variable becomes strictly exogenous, we propose a more efficient two-step least squares estimator. We provide asymptotic theory and develop…

非线性面板固定效应估计的 Bootstrap 与 k 步 Bootstrap 偏差修正AI 解读

Because of the incidental parameters problem, the fixed effects maximum likelihood estimator in a nonlinear panel data model is in general inconsistent when the time series length T is short and fixed. Even if T approaches infinity but at a rate not faster than the cross sectional sample size n , the fixed effects estimator is still asymptotically biased. This paper proposes using the standard bootstrap and k -step bootstrap to correct the bias. We establish the asymptotic…

未观测异质性的 Neyman C(α) 检验AI 解读

A unified framework is proposed for tests of unobserved heterogeneity in parametric statistic models based on Neyman’s C( α ) approach. Such tests are irregular in the sense that the first order derivative of the log likelihood with respect to the heterogeneity parameter is identically zero, and consequently the conventional Fisher information about the parameter is zero. Nevertheless, local asymptotic optimality of the C( α ) tests can be established via LeCam’s…

高维相关随机效应面板模型中的预言不等式、变量选择与一致有效推断AI 解读

In this paper we study high-dimensional correlated random effects panel data models. Our setting is useful as it allows including time invariant covariates as under random effects yet allows for correlation between covariates and unobserved heterogeneity as under fixed effects. We use the Mundlak–Chamberlain device to model this correlation. Allowing for a flexible correlation structure naturally leads to a high-dimensional model in which least squares estimation easily…

无处理无混杂性条件下有序处理模型的平均处理效应识别AI 解读

We show identification of the Average Treatment Effect (ATE) when treatment is specified by ordered choice in cross section or panel models. Treatment is determined by location of a latent variable (containing a continuous instrument) relative to two or more thresholds. We place no functional form restrictions on latent errors and potential outcomes. Unconfoundedness of treatment does not hold and identification at infinity for the treated is not possible. Yet we still show…

泡沫与危机：房价和信贷的作用AI 解读

Summary This paper utilizes quarterly panel data for 20 OECD countries over the period 1975:Q1–2014:Q2 to explore the importance of house prices and credit in affecting the likelihood of a financial crisis. Estimating a set of multivariate logit models, we find that booms in credit to both households and non‐financial enterprises are important to account for when evaluating the stability of the financial system. In addition, we find that global housing market developments…

欧元区与美国繁荣衰退的联动：贝叶斯面板马尔可夫转换VAR模型AI 解读

Summary The proposed panel Markov‐switching VAR model accommodates changes in low and high data frequencies and incorporates endogenous time‐varying transition matrices of country‐specific Markov chains, allowing for interconnections. An efficient multi‐move sampling algorithm draws time‐varying Markov‐switching chains. Using industrial production growth and credit spread data, several important data features are obtained. Three regimes appear, with slow growth becoming…

欧拉方程估计：子女与信贷约束AI 解读

Conventional estimators based on the consumption Euler equation, intensively used in studies of intertemporal consumption behavior, produce biased estimates of the effect of children on the marginal utility of consumption if consumers face credit constraints. As a more constructive contribution, I propose a tractable approach to obtaining bounds on the effect of children on the marginal utility of consumption. I estimate these bounds using the Panel Study of Income Dynamics…

利用高频面板评估因子定价模型AI 解读

This paper develops a new framework and statistical tools to analyze stock returns using high-frequency data. We consider a continuous-time multifactor model via a continuous-time multivariate regression model incorporating realistic empirical features, such as persistent stochastic volatilities with leverage effects. We find that the conventional regression approach often leads to misleading and inconsistent test results when applied to high-frequency data. We overcome this…

通过具有局部依赖的贝叶斯混合模型多重插补分类与连续缺失值AI 解读

We present a nonparametric Bayesian joint model for multivariate continuous and categorical variables, with the intention of developing a flexible engine for multiple imputation of missing values. The model fuses Dirichlet process mixtures of multinomial distributions for categorical variables with Dirichlet process mixtures of multivariate normal distributions for continuous variables. We incorporate dependence between the continuous and categorical variables by (1)…

含交互固定效应与多重结构突变的面板数据模型AI 解读

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the…

图式记录链接与去重的贝叶斯方法AI 解读

We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the…