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非平衡时间序列数据的非参数推断AI 解读

This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data are unbalanced or incomplete. In this case, one can work with only the common sample, to which a standard HAC/bootstrap theory applies, but at the expense of throwing away data and perhaps losing efficiency. An alternative is to use some sort of imputation method, but this requires additional modeling assumptions, which we would rather avoid. We show how…

通过自动化回归进行 HAC 估计AI 解读

A simple regression approach to HAC and LRV estimation is suggested. The method exploits the fact that the quantities of interest relate to only one point of the spectrum (the origin). The new estimator is simply the explained sum of squares in a linear regression whose regressors are a set of trend basis functions. Positive definiteness in the estimate is therefore automatically enforced, and the technique can be implemented with standard regression packages. No kernel…

伪回归中具有极限分布的 t 统计量AI 解读

This paper investigates the asymptotic properties of the t-statistic in spurious regressions when the bandwidth in the estimation of the heteroskedasticity and autocorrelation consistent (HAC) standard error is set proportional to the sample size. Using autocovariances of large lags, the so-defined HAC estimator is capable of capturing the high persistence of the regressor and regression residuals. It is shown that the resulting t-statistic converges to a nondegenerate…

纵向数据半参数建模的新估计与模型选择程序AI 解读

Semiparametric regression models are very useful for longitudinal data analysis. The complexity of semiparametric models and the structure of longitudinal data pose new challenges to parametric inferences and model selection that frequently arise from longitudinal data analysis. In this article, two new approaches are proposed for estimating the regression coefficients in a semiparametric model. The asymptotic normality of the resulting estimators is established. An…

遗漏结构不稳定时协方差估计与过度识别检验的极限行为AI 解读

We consider the limiting behavior of the overidentifying restrictions test in the presence of neglected structural instability at a single “break point.” It is shown that the test need not be consistent against this type of misspecification. If it is consistent then it emerges that the limiting behavior of this test statistic depends on the covariance matrix estimator employed. In this paper we consider the case in which a heteroskedasticity autocorrelation covariance (HAC)…

最近邻方差估计（NNVE）AI 解读

Robust covariance estimation commonly proceeds by downweighting outliers. In this article we measure the “outlyingness” of a data point by the standardized distance between the point and its Kth nearest neighbor. The appropriate weights for robust estimation are found by a model-based mixture modeling approach that follows from considering the data cloud as a realization of a high-dimensional point process. To correct a potential bias when there are no outliers, we introduce…

使用与样本量相等带宽的异方差自相关稳健检验AI 解读

Asymptotic theory for heteroskedasticity autocorrelation consistent (HAC) covariance matrix estimators requires the truncation lag, or bandwidth, to increase more slowly than the sample size. This paper considers an alternative approach covering the case with the asymptotic covariance matrix estimated by kernel methods with truncation lag equal to sample size. Although such estimators are inconsistent, valid tests (asymptotically pivotal) for regression parameters can be…

使用不截断Bartlett核的异方差与自相关稳健标准误AI 解读

In this paper we analyze heteroskedasticity-autocorrelation (HAC) robust tests constructed using the Bartlett kernel without truncation. We show that while such an HAC estimator is not consistent, asymptotically valid testing is still possible. We show that tests using the Bartlett kernel without truncation are exactly equivalent to recent HAC robust tests proposed by Kiefer, Vogelsang and Bunzel (2000, Econometrica, 68, pp 695-714).

病态估计问题的风险下界与置信集合：谱密度、持续性、单位根及长记忆参数的应用AI 解读

Important estimation problems in econometrics like estimating the value of a spectral density at frequency zero, which appears in the econometrics literature in the guises of heteroskedasticity and autocorrelation consistent variance estimation and long run variance estimation, are shown to be “ill-posed” estimation problems. A prototypical result obtained in the paper is that the minimax risk for estimating the value of the spectral density at frequency zero is infinite…

聚类数据的半参数回归AI 解读

We consider estimation in a semiparametric partially generalised linear model for clustered data using estimating equations. A marginal model is assumed where the mean of the outcome variable depends on some covariates parametrically and a cluster‐level covariate nonparametrically. A profile‐kernel method allowing for working correlation matrices is developed. We show that the nonparametric part of the model can be estimated using standard nonparametric methods, including…

夹心协方差矩阵估计效率的一则札记AI 解读

The sandwich estimator, also known as robust covariance matrix estimator, heteroscedasticity-consistent covariance matrix estimate, or empirical covariance matrix estimator, has achieved increasing use in the econometric literature as well as with the growing popularity of generalized estimating equations. Its virtue is that it provides consistent estimates of the covariance matrix for parameter estimates even when the fitted parametric model fails to hold or is not even…

非线性模型中的简便稳健假设检验AI 解读

We develop test statistics to test hypotheses in nonlinear weighted regression models with serial correlation or conditional heteroscedasticity of unknown form. The novel aspect is that these tests are simple and do not require the use of heteroscedasticity autocorrelationconsistent (HAC) covariance matrix estimators. This new class of tests uses stochastic transformations to eliminate nuisance parameters as a substitute for consistently estimating the nuisance parameters.…

谱密度估计与学生化样本均值的 Edgeworth 展开AI 解读

We establish valid Edgeworth expansions for the distribution of smoothed nonparametric spectral estimates, and of studentized versions of linear statistics such as the sample mean, where the studentization employs such a nonparametric spectral estimate. Particular attention is paid to the spectral estimate at zero frequency and, correspondingly, the studentized sample mean, to reflect econometric interest in autocorrelation-consistent or long-run variance estimation. Our…

改进的异方差一致协方差矩阵估计量AI 解读

Este documento tem por objetivo orientar os usuários do Repositório Institucional da Universidade Federal da Bahia (RI/UFBA) quanto à conversão de arquivos para o formato PDF/A (Portable Document Format/Archive).Recomenda-se que sua produção e arquivamento sejam realizados no formato PDF/A visando assegurar o acesso e a preservação de documentos digitais a longo prazo.

异方差与自相关一致协方差矩阵估计量的强一致性证明AI 解读

A strong consistency result for heteroskedasticity and autocorrelation consistent covariance matrix estimators is proven in this paper. In addition, an error in a weak consistency proof for such estimators in the econometrics literature and a correction of that result is provided.

存在非参数自相关时的无平滑推断AI 解读

The author gives conditions under which nonparametric autocorrelation-consistent variance estimation is possible without smoothing. The conditions are relevant to inference on slope parameters in models with an intercept and strictly exogenous regressors, and allow regressors and disturbances to collectively have considerable stationary long memory and to satisfy only mild, in some cases minimal, moment conditions. His estimate dominates smoothed ones in the sense that it…

广义半线性模型的有效估计AI 解读

The generalized semilinear model (GSLIM) extends the generalized linear model by allowing the addition of an unknown smooth function to the predictor parameter, producing a semiparametric model. Some previously proposed score-based estimators for the parametric component in this model are not √n consistent. We propose efficient estimators for both the parametric and nonparametric components of the GSLIM. The estimator for the parametric component is the solution to an…

广义半线性模型的有效估计量AI 解读

The generalized semilinear model (GSLIM) extends the generalized linear model by allowing the addition of an unknown smooth function to the predictor parameter, producing a semiparametric model. Some previously proposed score-based estimators for the parametric component in this model are not √n consistent. We propose efficient estimators for both the parametric and nonparametric components of the GSLIM. The estimator for the parametric component is the solution to an…

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异方差协整AI 解读

This paper develops an asymptotic theory of estimation and inference in ‘cointegrated’ regression models with errors displaying nonstationary variances. Least squares estimates are shown to be consistent at a T12 rate. Hypothesis testing requires the use of a robust covariance matrix estimate, in contrast to earlier work on cointegrated regressions. The inference theory is not nuisance-free, but preliminary investigations indicate that approximation by the normal…

广义矩估计竞争模型的非嵌套检验AI 解读

Non-nested tests are proposed for competing models estimated by generalized method of moments. Results are presented for non-nested linear regression models with het- eroskedasticity and serial correlation of unknown form and differing instrument validity assumptions. Regression forms of the statistics are also presented. THIS PAPER IS CONCERNED with providing a procedure whereby non-nested models estimated by generalized method of moments (GMM) (Hansen (1982)) may be com-…

改进的异方差与自相关一致协方差矩阵估计量AI 解读

This paper considers a new class of heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators. The estimators considered are prewhitened kernel estimators with vector autoregressions employed in the prewhitening stage. The paper establishes consistency, rate of convergence, and asymptotic truncated mean squared error (MSE) results for the estimators when a fixed or automatic bandwidth procedure is employed. Conditions are obtained under which…

异方差与自相关一致的协方差矩阵估计AI 解读

This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms. Currently available estimators that are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme. No results are available regarding the choice of lag truncation parameter for a fixed sample size, regarding data-dependent automatic lag truncation parameters, or regarding the choice of…