A heteroskedasticity robust Breusch–Pagan test for Contemporaneous correlation in dynamic panel data models
动态面板同期相关的异方差稳健 Breusch–Pagan 检验AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:方法直接针对动态面板的截面相关,且应用于 OECD 国家增长面板,因此同时对应面板数据与宏观面板。
动态面板同期相关的异方差稳健 Breusch–Pagan 检验AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
This paper proposes a heteroskedasticity-robust Breusch–Pagan test of the null hypothesis of zero cross-section (or contemporaneous) correlation in linear panel data models, without necessarily assuming independence of the cross-sections. The procedure allows for either fixed, strictly exogenous and/or lagged dependent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution. The asymptotic validity of the test procedure is predicated on the number of time series observations, T, being large relative to the number of cross-section units, N, in that: either (i) N is fixed as T→∞; or, (ii) N2/T→0 as both T and N diverge, jointly, to infinity. Given this, it is not expected that asymptotic theory would necessarily provide an adequate guide to finite sample performance when T/N is “small”. Because of this we also propose, and establish asymptotic validity of, a number of wild bootstrap schemes designed to provide improved inference when T/N is small. Across a variety of experimental designs, a Monte Carlo study suggests that the predictions from asymptotic theory do, in fact, provide a good guide to the finite sample behaviour of the test when T is large relative to N. However, when T and N are of similar orders of magnitude, discrepancies between the nominal and empirical significance levels occur as predicted by the first-order asymptotic analysis. On the other hand, for all the experimental designs, the proposed wild bootstrap approximations do improve agreement between nominal and empirical significance levels, when T/N is small, with a recursive-design wild bootstrap scheme performing best, in general, and providing quite close agreement between the nominal and empirical significance levels of the test even when T and N are of similar size. Moreover, in comparison with the wild bootstrap “version” of the original Breusch–Pagan test (Godfrey and Yamagata, 2011) our experiments indicate that the corresponding version of the heteroskedasticity-robust Breusch–Pagan test appears reliable. As an illustration, the proposed tests are applied to a dynamic growth model for a panel of 20 OECD countries.
面板数据分位数回归的GMM方法
This paper develops a smoothed generalized method of moments (GMM) framework for panel quantile models with individual-specific fixed-effects and endogenous regressors. By smoothing the indicator in the quantile moment conditions and profiling out the individual effects through smoothed individual quantile restrictions, the proposed estimator can be implemented using standard GMM algorithms. We establish consistency, asymptotic normality, and feasible inference under weak…
含个体、时间及交互效应的短期动态面板模型
Summary This paper proposes a transformed quasi‐maximum likelihood (TQML) estimator for short dynamic fixed effects panel data models allowing for interactive effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, while at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. The order condition for identification…
交互固定效应面板模型的设定检验
In this paper, we propose a consistent nonparametric test for linearity in a large dimensional panel data model with interactive fixed effects. Both lagged dependent variables and conditional heteroskedasticity of unknown form are allowed in the model. We estimate the model under the null hypothesis of linearity to obtain the restricted residuals which are then used to construct the test statistic. We show that after being appropriately centered and standardized, the test…
针对非线性备择的线性动态面板模型检验
The most popular econometric models in the panel data literature are the class of linear panel data models with unobserved individual- and/or time-specific effects. The consistency of parameter estimators and the validity of their economic interpretations as marginal effects depend crucially on the correct functional form specification of the linear panel data model. In this paper, a new class of residual-based tests is proposed for checking the validity of dynamic panel…
交互固定效应面板模型中的同质性检验
This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The…
具有个体与时间随机效应的广义动态面板模型
An exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-Gaussian dynamic panel data model with unobserved random individual-specific and time-varying effects. We propose an estimation procedure based on the importance sampling technique. In particular, a sequence of conditional importance densities is derived which integrates out all random effects from the joint distribution of endogenous variables. We disentangle the integration…