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含结构变化的空间面板数据模型AI 解读

Spatial panel data models have gained widespread application in social sciences, particularly in economics, due to their ability to capture spatial dependencies. However, existing studies on spatial models typically rely on the assumption of parameter stability, which may be overly restrictive given the well-documented prevalence of structural changes in economic relationships. This paper addresses this limitation by proposing and analyzing spatial panel data models that…

门槛空间自回归模型AI 解读

In this paper, we consider the estimation and inferential issues of the threshold spatial autoregressive (TSAR) model, which is a hybrid of the threshold and spatial autoregressive models. We use the quasi maximum likelihood (QML) method to estimate the model. In addition, we prove the tightness and the Hájek–Rényi type inequality for a quadratic form and establish a full inferential theory of the QML estimator under the setup that threshold effect shrinks to zero as the…

实时宏观数据能否预测债券收益？AI 解读

We investigate the predictability of bond returns using real-time macro variables and consider the possibility of a nonlinear predictive relationship and the presence of weak factors. To address these issues, we propose a scaled sufficient forecasting (sSUFF) method and analyze its asymptotic properties. Using both the existing and the new method, we find empirically that real-time macro variables have significant forecasting power both in-sample and out-of-sample. Moreover,…

含不可观测异质性的空间面板分位数模型AI 解读

This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. For estimating model parameters, a new estimation procedure is proposed. When both the time and cross-sectional dimensions of the panel go to infinity, the uniform consistency and the asymptotic normality of the estimated parameters are established. In order…

含未观测异质性的大规模面板选择模型：贝叶斯与极大似然分析AI 解读

This paper considers the estimation and inference procedures for the case of a logistic panel regression model with interactive fixed effects, where multiple individual effects are allowed and the model is capable of capturing high-dimensional cross-section dependence. The proposed model also allows for heterogeneous regression coefficients. New Bayesian and non-Bayesian approaches are introduced to estimate the model parameters. We investigate the asymptotic behaviors of…

具有共同冲击的动态空间面板数据模型AI 解读

This paper studies dynamic spatial panel data models with common shocks to deal with both weak and strong cross-sectional correlations. Weak correlations are captured by a spatial structure and strong correlations are captured by a factor structure. The proposed quasi-maximum likelihood estimator (QMLE) is capable of handling both types of cross sectional dependence. We provide a rigorous analysis for the asymptotic theory of the QMLE, demonstrating its desirable properties.…

重新审视中国FDI区位：异质冲击面板方法AI 解读

Foreign Direct Investment (FDI) is viewed as a primary driving force in shaping the global economy and receives particular attention in empirical studies. In this paper, we argue that many of the existing studies ignore endogeneities that arise from shocks in source and destination countries. To address this endogeneity issue, we take the “controlling through estimating” idea from the econometric literature and propose using panel data models with heterogeneous shocks to…

具有交互固定效应的面板门槛模型AI 解读

This paper studies estimation and inference in a panel threshold model in the presence of interactive fixed effects. We study the asymptotic properties of the least squares estimators of the regression parameters in the shrinking-threshold-effect framework. We find that under some regularity conditions, the threshold parameter estimator possesses super-consistency in the sense that its estimation error has an asymptotically negligible effect on the asymptotic properties of…

共同冲击面板模型中异质系数的有效估计AI 解读

This paper investigates the estimation and inference issues of heterogeneous coefficients in panel data models with common shocks. We propose a novel two-step method to estimate the heterogeneous coefficients. We establish the asymptotic theory of our estimators, including consistency, asymptotic representation, and limiting distribution. Our two-step method can effectively address the limitations of the existing methods, such as the common correlated effects method proposed…

高维约束因子模型的准极大似然分析AI 解读

Factor models have been widely used in practice. However, an undesirable feature of a high dimensional factor model is that the model has too many parameters. An effective way to address this issue, proposed in a seminar work by Tsai and Tsay (2010) is to decompose the loadings matrix by a high-dimensional known matrix multiplying with a low-dimensional unknown matrix, which Tsai and Tsay (2010) name the constrained factor models. This paper investigates the estimation and…

固定效应动态空间面板模型与脉冲响应分析AI 解读

Real data often have complicated correlations over cross section and time. Such correlations are of particular interests in empirical studies. This paper considers using high order spatial lags and high order time lags to model complicated correlations over cross section and time. We propose to use the quasi maximum likelihood (QML) method to estimate the model. We establish the asymptotic theory of the quasi maximum likelihood estimator (QMLE), including the consistency and…

因子增广向量自回归模型的估计与推断AI 解读

The factor-augmented vector autoregressive (FAVAR) model is now widely used in macroeconomics and finance. In this model, observable and unobservable factors jointly follow a vector autoregressive process, which further drives the comovement of a large number of observable variables. We study the identification restrictions for FAVAR models, and propose a likelihood-based two-step method to estimate the model. The estimation explicitly accounts for factors being partially…

通过潜在共同因子建模多元波动率AI 解读

Volatility, represented in the form of conditional heteroscedasticity, plays an important role in controlling and forecasting risks in various financial operations including asset pricing, portfolio allocation, and hedging futures. However, modeling and forecasting multi-dimensional conditional heteroscedasticity are technically challenging. As the volatilities of many financial assets are often driven by a few common and latent factors, we propose in this article a…

高维近似因子模型的最大似然估计与推断AI 解读

An approximate factor model of high dimension has two key features. First, the idiosyncratic errors are correlated and heteroskedastic over both the cross-section and time dimensions; the correlations and heteroskedasticities are of unknown forms. Second, the number of variables is comparable or even greater than the sample size. Thus, a large number of parameters exist under a high-dimensional approximate factor model. Most widely used approaches to estimation are principal…

具有交互效应的面板数据模型：理论与方法AI 解读

This paper considers the maximum likelihood estimation of panel data models with interactive effects. Motivated by applications in economics and other social sciences, a notable feature of the model is that the explanatory variables are correlated with the unobserved effects. The usual within-group estimator is inconsistent. Existing methods for consistent estimation are either designed for panel data with short time periods or are less efficient. The maximum likelihood…

高维因子模型的统计分析AI 解读

This paper considers the maximum likelihood estimation of factor models of high dimension, where the number of variables (N) is comparable with or even greater than the number of observations (T). An inferential theory is developed. We establish not only consistency but also the rate of convergence and the limiting distributions. Five different sets of identification conditions are considered. We show that the distributions of the MLE estimators depend on the identification…