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含测量误差内生变量的非可分模型估计AI 解读

We study the identification and estimation of covariate-conditioned average marginal effects of endogenous regressors in nonseparable structural systems when the regressors are mismeasured. We control for the endogeneity by making use of covariates as control variables; this ensures conditional independence between the endogenous causes of interest and other unobservable drivers of the dependent variable. Moreover, we recover distributions of the underlying true causes from…

识别表达数量性状位点的贝叶斯划分模型AI 解读

Expression quantitative trait loci (eQTLs) are genomic locations associated with changes of expression levels of certain genes. By assaying gene expressions and genetic variations simultaneously on a genome-wide scale, scientists wish to discover genomic loci responsible for expression variations of a set of genes. The task can be viewed as a multivariate regression problem with variable selection on both responses (gene expression) and covariates (genetic variations),…

Dynamic networks are used in a variety of fields to represent the structure and evolution of the relationships between entities. We present a model which embeds longitudinal network data as trajectories in a latent Euclidean space. We propose Markov chain Monte Carlo (MCMC) algorithm to estimate the model parameters and latent positions of the actors in the network. The model yields meaningful visualization of dynamic networks, giving the researcher insight into the…

重复测量结果的函数单指标广义部分线性模型：核与样条融合平滑AI 解读

We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single…

利用分位数进行面板非参数识别AI 解读

This paper considers identification and estimation of ceteris paribus effects of continuous regressors in nonseparable panel models with time homogeneity. The effects of interest are derivatives of the average and quantile structural functions of the model. We find that these derivatives are identified with two time periods for “stayers”, i.e. for individuals with the same regressor values in two time periods. We show that the identification results carry over to models that…

具有固定效应的异质面板半参数模型AI 解读

This paper develops methodology for semiparametric panel data models in a setting where both the time series and the cross section are large. Such settings are common in finance and other areas of economics. Our model allows for heterogeneous nonparametric covariate effects as well as unobserved time and individual specific effects that may depend on the covariates in an arbitrary way. To model the covariate effects parsimoniously, we impose a dimensionality reducing common…

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处理效应估计：中国2008年经济刺激计划案例AI 解读

Researchers often face the challenge of estimating the counterfactuals to evaluate the treatment effects. Hsiao et al. (2012) propose a method that offers more flexibilities by allowing the influence of the unobservable latent factors to vary cross-section. This paper relaxes the linear conditional mean assumption in their method by extending it to a semi-parametric setting. The asymptotic distribution properties of the average treatment effect estimator is derived and…

存在附带参数时的模型选择AI 解读

This paper considers model selection in panels where incidental parameters are present. Primary interest centers on selecting a model that best approximates the underlying structure involving parameters that are common within the panel. It is well known that conventional model selection procedures are often inconsistent in panel models and this can be so even without nuisance parameters. Modifications are then needed to achieve consistency. New model selection information…

含弱外生解释变量的异质动态面板共同相关效应估计AI 解读

This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variables and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satisfied to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section…

固定效应面板部分指定Tobit回归的估计AI 解读

This paper presents an estimation of a partly specified Tobit model with fixed effects by extending the symmetric trimming procedure proposed in Honoré (1992). Under some sufficient conditions, we present conditions to identify both the parametric and the nonparametric component of the unknown parameter. Under some sufficient conditions, we establish that the proposed estimates are consistent and the estimates of the parametric component are asymptotically normally…

短面板固定效应删失分位数回归模型的集合识别AI 解读

This paper studies identification and estimation of a censored quantile regression model for short panel data with fixed effects. Using the redistribution-of-mass idea, we obtain bounds on the conditional distribution of differences of the model across periods, under conditional quantile restrictions together with a weak conditional independence assumption along the lines of Rosen (2012). The inversion of the distribution bounds characterizes the sharp identified set via a…

固定效应非参数面板模型中的误差序列相关检验AI 解读

In this paper we consider the problem of testing serial correlation in fixed effects panel data model in a nonparametric framework. Using asymptotic results developed in Su and Lu (2013), we show that our test statistic has a standard normal distribution under the null hypothesis of zero serial correlation. The test statistic diverges to infinity at the rate of N under the alternative hypothesis that error is serially correlated, where N is the cross sectional sample size.…

固定效应面板非参数回归AI 解读

Nonparametric regression is developed for data with both a temporal and a cross-sectional dimension. The model includes additive, unknown, individual-specific components and allows also for cross-sectional and temporal dependence and conditional heteroscedasticity. A simple nonparametric estimate is shown to be dominated by a GLS-type one. Asymptotically optimal bandwidth choices are justified for both estimates. Feasible optimal bandwidths, and feasible optimal regression…

二元响应相关随机系数面板模型AI 解读

In this paper, we consider binary response correlated random coefficient (CRC) panel data models which are frequently used in the analysis of treatment effects and demand of products. We focus on the nonparametric identification and estimation of panel data models under unobserved heterogeneity which is captured by random coefficients and when these random coefficients are correlated with regressors. Our identification conditions and estimation are based on the framework of…

美国跨国企业与优惠市场准入AI 解读

We combine firm-level panel data on U.S. foreign affiliate activity with detailed measures of U.S. trade policy to study the relationship between offshoring and preferential market access. Consistent with theory, we find that trade preferences and offshoring activity are positively and significantly correlated. Using instrumental variables, we estimate that a 10% increase in U.S. foreign affiliate exports to the United States is associated with a 4 percentage point increase…

N 固定时校正横截面依赖的新型面板协整检验AI 解读

In this paper, we propose new cointegration tests for single equations and panels. In both cases, the asymptotic distributions of the tests, which are derived with N fixed and T→∞, are shown to be standard normals. The effects of serial correlation and cross‐sectional dependence are mopped out via long‐run variances. An effective bias correction is derived, which is shown to work well in finite samples, particularly when N is smaller than T. Our panel tests are robust to…

稀疏异步纵向数据的回归分析AI 解读

Summary We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or…

存在截面依赖的半参数单指标面板模型AI 解读

In this paper, we consider a semiparametric single-index panel data model with cross-sectional dependence and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and the link function for the case where both cross-sectional dimension (N) and temporal dimension (T) go…

动态面板数据中的异质性与选择AI 解读

The data generating process (DGP) for generic dynamic panel data consists of a law of state dynamics g, a selection or attrition rule h, and an initial condition F. I study nonparametric identifiability of this complete DGP (g,h,F) using short unbalanced panel data, allowing for nonseparability between observed states and unobserved heterogeneity in each of g, h and F. For T⩾3, the DGP is identified by using a proxy variable. For T⩾6, the three additional periods construct a…

利用短面板估计异质自回归参数的截面分布AI 解读

This paper presents a maximum likelihood approach to estimation of cross sectional distributions of heterogeneous autoregressive (AR) parameters with short panel data. We construct a panel likelihood by integrating the unknown cross sectional density of heterogeneous AR parameters with respect to a known time-series data generating kernel. The solution to this extremal criterion recovers the unknown density of heterogeneous AR parameters. Applying our method to a model of…

理解面板单位根检验局部渐近功效的新工具AI 解读

Motivated by the previously documented discrepancy between actual and predicted power, the present paper provides new tools for analyzing the local asymptotic power of panel unit root tests. These tools are appropriate in general when considering panel data with a dominant autoregressive root of the form ρi=1+ciN−κT−τ, where i=1,…,N indexes the cross-sectional units, T is the number of time periods and ci is a random local-to-unity parameter. A limit theory for the sample…

横截面异方差下动态面板变换似然估计的稳健标准误AI 解读

This paper extends the transformed maximum likelihood approach for estimation of dynamic panel data models by Hsiao et al. (2002) to the case where the errors are cross-sectionally heteroskedastic. This extension is not trivial due to the incidental parameters problem and its implications for estimation and inference. We approach the problem by working with a mis-specified homoskedastic model, and then show that the transformed maximum likelihood estimator continues to be…

纵向数据部分线性单指标模型的半参数 GEE 分析AI 解读

In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on a combination of the local linear smoothing and generalized estimation equations (GEE) is introduced to estimate the two parameter vectors as well as the unknown link function. Under some mild conditions, we derive the asymptotic properties of the proposed…