论文检索

固定 T 异质性面板模型的双向均值组估计AI 解读

Summary We consider a correlated random coefficient panel data model with two-way fixed effects and interactive fixed effects in a fixed T framework. The model allows slope coefficients to be arbitrarily correlated with the regressors, accommodating flexible forms of heterogeneity. We propose a two-way mean group estimator for the expected value of the slope coefficient and propose a leave-one-out jackknife method for valid inference. We apply our new methods to examine the…

具有全局与局部因子的三维因子模型AI 解读

This article considers a three-dimensional latent factor model in the presence of one set of global factors and two sets of local factors. We show that the numbers of global and local factors can be estimated uniformly and consistently. Given the number of global and local factors, we propose a two-step estimation procedure based on principal component analysis (PCA) and establish the asymptotic properties of the PCA estimators. Monte Carlo simulations demonstrate that they…

状态变化因子模型的筛估计AI 解读

In this paper, we propose a sieve approach to estimate state-varying factor models, where the factor loadings vary over specific state variables. Our methodology consists of a two-step estimation procedure for the parameters of interest. In the first step, we achieve preliminary consistent estimates of the factors and factor loadings via nuclear norm regularization (NNR). In the second step, we perform post-NNR iterative least squares estimations for the factors and factor…

具有时变交互固定效应的时变面板数据模型AI 解读

This paper introduces a time-varying (TV) panel data model with interactive fixed effects where both the coefficients and factor loadings are allowed to change smoothly over time. We propose a local version of the least squares and principal component method to estimate the TV coefficients, TV factor loadings, and common factors simultaneously. We provide a bias-corrected local least squares estimator for the TV coefficients and establish the limiting distributions and…

具有多层交互固定效应的三维异质面板数据模型AI 解读

We consider a three-dimensional (3D) panel data model with heterogeneous slope coefficients and multi-level interactive fixed effects consisting of latent global factors and two types of local factors. Our model nests many commonly used 3D panel data models. We propose an iterative estimation procedure that relies on initial consistent estimators obtained through a novel defactored approach. We study the asymptotic properties of our estimators and show that our iterative…

含交互固定效应的异质动态面板模型：Oracle 有效估计AI 解读

We propose a two-step procedure to estimate a heterogeneous dynamic panel data model with interactive fixed effects. We establish the asymptotic properties of the estimators and show that the final estimator is oracle efficient. We also propose a specification test for the null hypothesis of homogeneous slopes and study the asymptotic properties of the test statistic under both local and global alternatives. Simulations demonstrate the fine performance of the estimator and…

含局部二次趋势的单位根新检验AI 解读

Summary This paper proposes a new test for unit root processes with a partial quadratic trend on an unknown break date, denoted as the URQ process herein. Such a process is extremely similar to the explosive bubble process, and both can capture the sharp rise in prices. We develop the asymptotic distributions under the local-to-unity hypothesis, which covers the URQ null and explosive root alternatives. Simulations show that the test has good finite sample performances and…

稳健的预测优越性检验及专家预测组合评估AI 解读

Summary We develop forecast superiority tests that are robust to the choice of loss function by following Jin, Corradi and Swanson (JCS: 2017), and relying on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not uniformly valid and are correctly sized only under the least favorable case, our tests are uniformly asymptotically valid and non‐conservative. To show this, we establish uniform convergence…

随机缺失下的因子模型：EM 估计、推断与交叉验证AI 解读

We consider the estimation and inference in approximate factor models with random missing values. We show that with the low rank structure of the common component, we can estimate the factors and factor loadings consistently with the missing values replaced by zeros. We establish the asymptotic distributions of the resulting estimators and those based on the EM algorithm. We also propose a cross-validation-based method to determine the number of factors in factor models with…

具有潜在分组结构与截面依赖的非平稳面板模型AI 解读

This paper proposes a novel Lasso-based approach to handle unobserved parameter heterogeneity and cross-section dependence in nonstationary panel models. In particular, a penalized principal component (PPC) method is developed to estimate group-specific long-run relationships and unobserved common factors and jointly to identify the unknown group membership. The PPC estimators are shown to be consistent under weakly dependent innovation processes. But they suffer an…

识别协整面板中的潜在分组模式AI 解读

We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips (2016, Econometrica 84(6), 2215–2264) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the stationary regressors to diverge with the sample size. We…

具有潜在分组结构的时变面板模型的筛估计AI 解读

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure…

稳健预测比较AI 解读

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. In order to address this issue, a novel criterion for forecast evaluation that utilizes the entire distribution of forecast errors is introduced. In particular, we introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL)…

条件异方差下的自适应非参数回归AI 解读

In this paper, we study adaptive nonparametric regression estimation in the presence of conditional heteroskedastic error terms. We demonstrate that both the conditional mean and conditional variance functions in a nonparametric regression model can be estimated adaptively based on the local profile likelihood principle. Both the one-step Newton–Raphson estimator and the local profile likelihood estimator are investigated. We show that the proposed estimators are…

交互固定效应面板模型的设定检验AI 解读

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…

鞅假设检验AI 解读

We propose new tests of the martingale hypothesis based on generalized versions of the Kolmogorov–Smirnov and Cramér–von Mises tests. The tests are distribution-free and allow for a weak drift in the null model. The methods do not require either smoothing parameters or bootstrap resampling for their implementation and so are well suited to practical work. The article develops limit theory for the tests under the null and shows that the tests are consistent against a wide…

存在横截面依赖的面板数据模型筛估计AI 解读

In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006)…

幂核异方差与自相关稳健检验的检验力最大化和尺寸控制AI 解读

Using the power kernels of Phillips, Sun, and Jin (2006, 2007), we examine the large sample asymptotic properties of the t -test for different choices of power parameter ( ρ ). We show that the nonstandard fixed- ρ limit distributions of the t -statistic provide more accurate approximations to the finite sample distributions than the conventional large- ρ limit distribution. We prove that the second-order corrected critical value based on an asymptotic expansion of the…

部分线性空间自回归模型的剖面准最大似然估计AI 解读

We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are partially linear. The rate of convergence of the spatial parameter estimator depends on some general features of the spatial weight matrix of the model. The estimators of other finite-dimensional parameters in the model have the regular -rate of convergence and the estimator of the nonparametric component is consistent but with different restrictions on the choice of bandwidth…

非平稳面板离散选择建模及其汇率制度选择应用AI 解读

This paper develops a regression limit theory for discrete choice nonstationary panels with large cross section (N) and time series (T) dimensions. Some results emerging from this theory are directly applicable in the wider context of M-estimation. This includes an extension of work by Wooldridge [Wooldridge, J.M., 1994. Estimation and Inference for Dependent Processes. In: Engle, R.F., McFadden, D.L. (Eds.). Handbook of Econometrics, vol. 4, North-Holland, Amsterdam] on the…

异方差与自相关稳健检验中的最优带宽选择AI 解读

In time series regressions with nonparametrically autocorrelated errors, it is now standard empirical practice to use kernel-based robust standard errors that involve some smoothing function over the sample autocorrelations. The underlying smoothing parameter b, which can be defined as the ratio of the bandwidth (or truncation lag) to the sample size, is a tuning parameter that plays a key role in determining the asymptotic properties of the standard errors and associated…