摘要翻译：
本文利用具有共同系数和异质系数以及横截面异方差的动态线性模型，构造了一组企业或家庭的个体比密度预测。本文所考虑的面板具有截面维数N大而时间序列T短的特点，由于T短，传统的方法难以从冲击中分离出非均质参数，从而影响了对非均质参数的估计。为了解决这个问题，我假设存在一个异质参数的潜在分布，对这种分布进行非参数建模，允许异质参数与初始条件以及个体特定回归之间的相关性，然后通过组合来自整个面板的信息来估计这种分布。从理论上证明了在横截面同态情况下，估计的公共参数和估计的异质参数分布均达到后验一致性，并且密度预测渐近收敛于oracle预测。在方法上，我开发了一个基于仿真的后验抽样算法，专门解决未观测到的异构参数的非参数密度估计。蒙特卡洛模拟和对年轻企业动态的实证应用表明，相对于替代方法，密度预测有了改进。
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英文标题：
《Density Forecasts in Panel Data Models: A Semiparametric Bayesian
  Perspective》
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作者：
Laura Liu
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients as well as cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, model this distribution nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors, and then estimate this distribution by combining information from the whole panel. Theoretically, I prove that in cross-sectional homoskedastic cases, both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Methodologically, I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. Monte Carlo simulations and an empirical application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches.
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PDF链接：
https://arxiv.org/pdf/1805.04178