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平稳时间序列自相关的稳健置信区间AI 解读

This paper develops a simple and new approach for testing linear hypotheses about autocorrelations for time series with general stationary serial correlation structures. A practically important special case is the computation of robust confidence intervals for individual autocorrelations that do not require resampling methods. Inference is heteroskedasticity and autocorrelation robust (HAR) and allows innovations to be uncorrelated but not necessarily independent and…

长时间跨度高频回归的若干 fixed-b 结果AI 解读

This paper develops fixed-b asymptotic results for heteroskedasticity autocorrelation robust (HAR) Wald tests for high frequency data using the continuous time framework of Chang et al. (2023) (CLP). It is shown that the fixed-b limit of HAR Wald tests for high frequency stationary regressions is the same as the standard fixed-b limit in Kiefer and Vogelsang (2005). For the case of cointegrating regression the form of the fixed-b limits are different from the stationary case…

双向聚类面板模型的固定 b 渐近理论AI 解读

This paper studies a cluster robust variance estimator proposed by Chiang, Hansen and Sasaki (2024) for linear panels. First, we show algebraically that this variance estimator (CHS estimator, hereafter) is a linear combination of three common variance estimators: the one-way unit cluster estimator, the “HAC of averages” estimator, and the “average of HACs” estimator. Based on this finding, we obtain a fixed-b asymptotic result for the CHS estimator and corresponding test…

利用平滑聚类标准误进行时间序列模型推断AI 解读

This paper proposes a long run variance estimator for conducting inference in time series regression models that combines the nonparametric approach with a cluster approach. The basic idea is to divide the time periods into non-overlapping clusters. The long run variance estimator is constructed by first aggregating within clusters and then kernel smoothing across clusters or applying the nonparametric series method to the clusters with Type II discrete cosine transform. We…

缺失数据下时间序列回归的异方差与自相关稳健推断AI 解读

In this article, we investigate the properties of heteroskedasticity and autocorrelation robust (HAR) test statistics in time series regression settings when observations are missing. We primarily focus on the nonrandom missing process case where we treat the missing locations to be fixed as T → ∞ by mapping the missing and observed cutoff dates into points on [0,1] based on the proportion of time periods in the sample that occur up to those cutoff dates. We consider two…

对《HAR 推断：实践建议》的评论AI 解读

Inference robust to variance and covariance features of the data has become the standard practice in empirical economics. Robust inference in the presence of heteroscedasticity is straightforward a...

空间依赖稳健非参数协方差矩阵估计的 fixed-b 渐近理论AI 解读

This paper develops a method for performing inference using spatially dependent data. We consider test statistics formed using nonparametric covariance matrix estimators that account for heteroskedasticity and spatial correlation (spatial HAC). We provide distributions of commonly used test statistics under “fixed- b ” asymptotics, in which HAC smoothing parameters are proportional to the sample size. Under this sequence, spatial HAC estimators are not consistent but…

非平稳面板的非参数秩检验AI 解读

We develop a set of nonparametric rank tests for non-stationary panels based on multivariate variance ratios which use untruncated kernels. As such, the tests do not require the choice of tuning parameters associated with bandwidth or lag length and also do not require choices with respect to numbers of common factors. The tests allow for unrestricted cross-sectional dependence and dynamic heterogeneity among the units of the panel, provided simply that a joint functional…

评述AI 解读

Inference in time series settings is complicated by the possibility of strong autocorrelation in the data. In general, some aspect of a time series model is assumed to satisfy stationarity and weak...

This paper is concerned with parameter estimation and inference in a cointegrating regression, where as usual endogenous regressors as well as serially correlated errors are considered. We propose a simple, new estimation method based on an augmented partial sum (integration) transformation of the regression model. The new estimator is labeled integrated modified ordinary least squares (IM-OLS). IM-OLS is similar in spirit to the fully modified OLS approach of Phillips and…

Phillips–Perron 单位根检验的 fixed-b 视角AI 解读

In this paper we extend fixed- b asymptotic theory to the nonparametric Phillips–Perron (PP) unit root tests. We show that the fixed- b limits depend on nuisance parameters in a complicated way. These nonpivotal limits provide an alternative theoretical explanation for the well-known finite-sample problems of the PP tests. We also show that the fixed- b limits depend on whether deterministic trends are removed using one-step or two-step detrending approaches. This is in…

固定效应线性面板中对异方差、自相关与空间相关稳健的推断AI 解读

This paper develops an asymptotic theory for test statistics in linear panel models that are robust to heteroskedasticity, autocorrelation and/or spatial correlation. Two classes of standard errors are analyzed. Both are based on nonparametric heteroskedasticity autocorrelation (HAC) covariance matrix estimators. The first class is based on averages of HAC estimators across individuals in the cross-section, i.e. “averages of HACs”. This class includes the well known cluster…

未知时点趋势变动检验：OLS 稳健检验的固定 b 分析AI 解读

This paper analyzes tests for a shift in the trend function of a time series at an unknown date based on ordinary least squares (OLS) estimates of the trend function. Inference about the trend parameters depends on the serial correlation structure of the data through the long-run variance (zero frequency spectral density) of the errors. Asymptotically pivotal tests can be obtained by the use of serial correlation robust standard errors that require an estimate of the…

区块自助法 HAC 稳健检验：朴素自助法的精细性质AI 解读

This paper studies the properties of naive block bootstrap tests that are scaled by zero frequency spectral density estimators (long-run variance estimators). The naive bootstrap is a bootstrap where the formula used in the bootstrap world to compute standard errors is the same as the formula used on the original data. Simulation evidence shows that the naive bootstrap can be much more accurate than the standard normal approximation. The larger the HAC bandwidth, the greater…

异方差与自相关稳健检验的新渐近理论AI 解读

A new first-order asymptotic theory for heteroskedasticity-autocorrelation (HAC) robust tests based on nonparametric covariance matrix estimators is developed. The bandwidth of the covariance matrix estimator is modeled as a fixed proportion of the sample size. This leads to a distribution theory for HAC robust tests that explicitly captures the choice of bandwidth and kernel. This contrasts with the traditional asymptotics (where the bandwidth increases more slowly than the…

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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).

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动态时间序列均值突变检验中非单调功效的来源AI 解读

Sources of nonmonotonic power are uncovered for a wide variety of tests for a shift in the mean of a dynamic time series. Two main sources of nonmonotonic power are found. The first source is the behavior of the estimate of error variance under the alternative hypothesis of a shift in mean. In particular if the error variance is estimated under the null hypothesis, nonmonotonic power can result. The second source is the presence of a lagged dependent variable in the…

The interpretation of seasonality in terms of economic behavior depends on the form of the econometric time-series model that allows for a description of seasonality. Popular models often assume either approximate deterministic seasonality (cf. Miron (1996)) or stochastic trend seasonality (cf. Hylleberg (1994)). Inference from an inappropriate model can be shown to be invalid. Since much graphical evidence clearly suggests that seasonal fluctuations are not constant over…

存在序列相关时的趋势函数假设检验AI 解读

Test statistics are proposed for testing hypotheses about the parameters of the deterministic trend function of a univariate time series. The tests are valid for general forms of serial correlation in the errors and do not require estimates (parametric or nonparametric) of serial correlation parameters. The tests are valid for stationary and unit root errors. Allowable trend functions include linear polynomials of time that may have structural change. Asymptotic results are…

无需估计序列相关参数的均值突变检验AI 解读

Tests for detecting a shift in the mean of univariate time series that do not require estimation of serial-correlation parameters are proposed. The statistics are valid whether the errors are stationary or have a unit root. The date of the shift may be known or unknown. The statics are based on a simple transformation of the data and are functions of partial sums of the data. These so-called partial sum statistics are shown to be asymptotically invariant to…