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短时间维度异质性面板中的平均效应估计AI 解读

The commonly used two-way fixed effects estimator is biased under correlated heterogeneity and can lead to misleading inference. The mean group estimator proposed by \cite{PesaranSmith1995} is robust to correlated heterogeneity but requires the individual estimators to have second-order moments that could fail if the number of estimated coefficients ($k$) is close to the time dimension ($T$) of the panel. This paper focuses on panels where $T-k$ is mall (including $T=k$),…

面板数据预测：估计不确定性与参数异质性的权衡AI 解读

We provide a comprehensive examination of the predictive performance of panel forecasting methods based on individual, pooling, fixed effects, and empirical Bayes estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter…

潜在因子模型中的网络依赖检测：偏误修正 CD 检验AI 解读

In a recent paper Juodis and Reese (2022) (JR) show that the application of the CD test proposed by Pesaran (2004) to residuals from panels with latent factors results in over-rejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This paper considers the same problem but from a different perspective, and shows that the standard CD test remains valid if the latent factors are weak in the sense the…

面板数据模型中多重长期关系的分析AI 解读

The literature on panel cointegration is extensive but does not cover data sets where the cross section dimension, $n$, is larger than the time series dimension $T$. This paper proposes a novel methodology that filters out the short run dynamics using sub-sample time averages as deviations from their full-sample counterpart, and estimates the number of long-run relations and their coefficients using eigenvalues and eigenvectors of the pooled covariance matrix of these…

强、半强及潜在因子下线性资产定价模型的 alpha 识别与利用AI 解读

The risk premia of traded factors are the sum of factor means and a parameter vector we denote by φ which is identified from the cross section regression of alpha of individual securities on the vector of factor loadings. If phi is non-zero one can construct "phi-portfolios" which exploit the systematic components of non-zero alpha. We show that for known values of betas and when phi is non-zero there exist phi-portfolios that dominate mean-variance portfolios. The paper…

参数不稳定下的高维线性回归变量选择AI 解读

This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating…

短时间维度面板模型中的异质自回归AI 解读

This paper considers a first-order autoregressive panel data model with individual-specific effects and heterogeneous autoregressive coefficients defined on the interval (-1,1], 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 autoregressive (AR) coefficients, assuming a random coefficient model for the autoregressive coefficients without imposing any restrictions on the…

利用已知变量与未知潜在因素开展高维预测AI 解读

Forecasts play a central role in decision making under uncertainty. After a brief review of the general issues, this paper considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and OCMT, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches. We demonstrate the various issues involved in variable selection in a…

动态异质面板长期关系的合并 Bewley 估计量AI 解读

Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small sample performance of PB compared to the existing estimators in the…

美国各州与部分国家新冠基本再生数的结构计量估计AI 解读

This paper proposes a structural econometric approach to estimating the basic reproduction number ($\mathcal{R}_{0}$) of Covid-19. This approach identifies $\mathcal{R}_{0}$ in a panel regression model by filtering out the effects of mitigating factors on disease diffusion and is easy to implement. We apply the method to data from 48 contiguous U.S. states and a diverse set of countries. Our results reveal a notable concentration of $\mathcal{R}_{0}$ estimates with an…

类别型随机系数模型的识别与估计AI 解读

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of Moments estimation procedure is proposed also employed by Peter Schmidt and his coauthors to address heterogeneity in time effects in panel data models. Using Monte Carlo simulations, we…

用随机网络 SIR 模型连接新冠疫情理论与证据AI 解读

This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under-reporting of…