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具有不可观测分组异质性的面板自回归模型统一推断AI 解读

This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underlying PAR process is…

具有一般因子与解释变量的交互效应面板模型AI 解读

This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough,…

短时间维度面板中的异质自回归AI 解读

Summary This paper considers a first‐order autoregressive (AR) panel data model with individual‐specific effects and heterogeneous AR coefficients defined on the interval , thus allowing for some of the individual processes to have unit roots. It proposes estimators for the moments of the cross‐sectional distribution of the AR coefficients, assuming a random coefficient model for the AR coefficients without imposing any restrictions on the fixed effects. It is shown that the…

预测分位数回归中的统计推断AI 解读

This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t -statistic in terms of functionals of Ornstein–Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a…

交互固定效应面板模型中处理效应的置信区间AI 解读

We augment the factor-based estimation of treatment effects proposed by Bai and Ng (2021) with easy-to-implement and nonparametric confidence intervals of treatment effects on every treated unit at every post-treatment time. The construction of confidence intervals entails a residual-based bootstrap resampling procedure. This method does not rely on any parametric assumption on the distribution of idiosyncratic errors and it is robust to weak cross-sectional and time-series…

含个体、时间及交互效应的短期动态面板模型AI 解读

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…

通过 Classifier-Lasso 检测有效价格中的未观测异质性AI 解读

This article proposes a new measure of efficient price as a weighted average of bid and ask prices, where the weights are constructed from the bid-ask long-run relationships in a panel error-correction model (ECM). To allow for heterogeneity in the long-run relationships, we consider a panel ECM with latent group structures so that all the stocks within a group share the same long-run relationship and do not otherwise. We extend the Classifier-Lasso method to the ECM to…

重印：异质面板中的单位根检验AI 解读

This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal…

含平稳协变量的分位数协整非参数推断AI 解读

This paper considers the inference problems in nonlinear quantile regressions with both stationary and nonstationary covariates. The nonparametric local constant quantile estimator is proposed to estimate the unknown quantile regression function, whose asymptotic properties are established under quite general conditions. Specification testing of the quantile regression function is further considered through a statistic constructed based on the integrated squared distance…

动态面板数据模型的稳健似然估计AI 解读

We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias-corrected conditional score estimators, random effects maximum likelihood in levels and first differences, and estimators that impose mean stationarity are considered for general autoregressive models with individual effects. We investigate identification under unit roots, and show that random effects estimation in levels may…

运行变量存在组别特定测量误差时的断点回归校正AI 解读

When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is heterogeneous across different groups of observations. We develop a novel measurement error correction procedure capable of addressing heterogeneous mismeasurement structures by…

近单位根交互效应面板中的因子分析方法AI 解读

In a recent study, Bai (2013) proposes a new factor analytical (FA) method for estimation of stationary dynamic panel data models with fixed effects. Our interest in this method originates with the fact it does not require explicit demeaning of the data, a practice that is known to cause problems of bias and low power in near unit root panels. The purpose is to study the properties of FA when applied to such panels when the common component admits to a interactive effects…

大规模面板中异质性与协整的推断理论AI 解读

This paper provides an estimation and testing framework to assess the presence and the extent of slope heterogeneity and cointegration when the units are a mixture of spurious and/or cointegrating regressions. We propose two moment estimators for the degree of heterogeneity (measured by the dispersion of the slope coefficients around their average) and for the fraction of spurious regressions, which are found to be consistent in the whole parameter space. Based on these…

协整与对照：利用时间序列数据评估事件影响AI 解读

Summary Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that fitting a multivariate time series model offers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as difference in differences and synthetic control, suggesting ways to test these assumptions. Gains from simple…

二阶识别模型中的推断AI 解读

We explore the local power properties of different test statistics for conducting inference in moment condition models that only identify the parameters locally to second order. We consider the conventional Wald and LM statistics, and also the Generalized Anderson–Rubin (GAR) statistic (Anderson and Rubin, 1949; Dufour, 1997; Staiger and Stock, 1997; Stock and Wright, 2000), KLM statistic (Kleibergen, 2002; Kleibergen, 2005) and the GMM extension of Moreira (2003) (GMM-M)…

收入动态中经典永久—暂时模型的半参数估计AI 解读

This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components…

具有趋势的面板数据推断AI 解读

Semiparametric panel data modelling and statistical inference with fractional stochastic trends, nonparametrically time-trending individual effects, and general cross-sectional correlation and heteroscedasticity in innovations are developed. The fractional stochastic trends allow for a wide range of nonstationarity, indexed by a memory parameter, nesting the familiar I(1) case and allowing for parametric short-memory. The individual effects can nonparametrically vary…

内生随机单位根模型中的 IV 与 GMM 推断AI 解读

Lieberman and Phillips (2017; LP) introduced a multivariate stochastic unit root (STUR) model, which allows for random, time varying local departures from a unit root (UR) model, where nonlinear least squares (NLLS) may be used for estimation and inference on the STUR coefficient. In a structural version of this model where the driver variables of the STUR coefficient are endogenous, the NLLS estimate of the STUR parameter is inconsistent, as are the corresponding estimates…

一般趋势与横截面相关的固定 T 面板单位根推断AI 解读

This article proposes a new panel unit root test based on the generalized method of moments approach for panels with a possibly small number of time periods, T, and a large number of cross-sectional units, N. In the model that we consider the deterministic trend function is essentially unrestricted and the errors obey a multifactor structure that allows for rich forms of unobserved heterogeneity. In spite of these allowances, the GMM estimator considered is shown to be…

持久性面板过程中的非参数异方差：收入动态应用AI 解读

This paper considers a dynamic panel model where a latent state variable follows a unit root process with nonparametric heteroskedasticity. We develop constructive nonparametric identification and estimation of the skedastic function. Applying this method to the Panel Survey of Income Dynamics (PSID) in the framework of earnings dynamics, we found that workers with lower pre-recession permanent earnings had higher earnings risk during the three most recent recessions.

近单位根动态面板中的 Anderson–Hsiao 估计AI 解读

Limit theory is developed for the dynamic panel IV estimator in the presence of an autoregressive root near unity. In the unit root case, Anderson–Hsiao lagged variable instruments satisfy orthogonality conditions but are well known to be irrelevant. For a fixed time series sample size ( T ) IV is inconsistent and approaches a shifted Cauchy-distributed random variate as the cross-section sample size n → ∞. But when T → ∞, either for fixed n or as n → ∞, IV is $\sqrt T$ consi…

通过网络连接产生的长记忆AI 解读

Many time series exhibit “long memory”: Their autocorrelation function decays slowly with lag. This behavior has traditionally been modeled via unit roots or fractional Brownian motion and explained via aggregation of heterogeneous processes, nonlinearity, learning dynamics, regime switching, or structural breaks. This paper identifies a different and complementary mechanism for long‐memory generation by showing that it can naturally arise when a large number of simple…

一般预测变量下的面板可预测性检验AI 解读

Summary The difficulty of predicting returns has recently motivated researchers to start looking for tests that are either more powerful or robust to more features of the data. Unfortunately, the way that these tests work typically involves trading robustness for power or vice versa. The current paper takes this as its starting point to develop a new panel‐based approach to predictability that is both robust and powerful. Specifically, while the panel route to increased…

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…