Concept‐Based Bayesian Model Averaging and Growth Empirics

In specifying a regression equation, we need to specify which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted least squares (HWALS) method to address these uncertainties. We examine the effects of different growth determinants taking explicit account of the measurement problem in the growth regressions. We find that estimates produced by HWALS provide intuitive and robust explanations. We also consider approximation techniques which are useful when the number of variables is large or when computing time is limited.     

Panel Data模型设定的新思路--固定效应与随机效应的统一

经典PanelData模型研究中一直存在着固定效应与随机效应的判断与争论问题,这种模型设定形式的不准确常常导致模型参数估计的无效性以及一维（one-way）与二维（two-way）误差成分模型的混淆。在此,本文提出建立一种新的同时囊括随机与固定两种效应的误差成分一般模型,其中一维（one-way）情况的模型仅为二维（two-way）模型的特例模型,单一的随机效应或固定效应模型亦为其特殊情况的一种。在这种一般误差成分模型的基础上,我们力图将有关PanelData模型的讨论纳入到一个更加一般和统一的分析构架中予以研究。     

不确定条件下缺货不补货的库存模型分析

对以前研究得很少的一种情况——连续检查库存、提前期固定、需求随机且缺货不补进行了分析,详细推导了其最佳订货点和订货批量,并探讨了简化计算的合理性,然后用EXCEL进行了实例计算,最后进一步推广到提前期也随机的情况。     

面板数据模型设定存在的问题及对策分析

利用面板数据建模具有许多纯时间序列或截面数据没有的优点，但其建模过程也更复杂。本文指出了面板数据模型设定过程中常出现的几个问题，同时探讨了解决这些问题的有效方法。     

The Mundlak Approach in the Spatial Durbin Panel Data Model

Abstract

This paper extends the Mundlak approach to the spatial Durbin panel data model (SDM) to help the applied researcher to determine the adequacy of the random effects specification in this setup. We propose a likelihood ratio (LR) test that assesses the significance of the correlation between regressors and individual effects. By contrast to the Hausman test, the Mundlak approach identifies (to some extent) the regressors correlated with individual effects. The second advantage is that once the correlation with individual effects has been modelled through an auxiliary regression, the random effects specification provides consistent estimators and the effect of time-constant variables can be estimated. Some Monte Carlo simulations study the properties of this proposed LR test in small samples and show that in some cases, it has a better behaviour than the Hausman test. We finally illustrate the usefulness of the extended Mundlak approach by estimating a house price model where some of the price determinants are time-constant. We show that ignoring the endogeneity of regressors with respect to individual effects leads to unreliable estimated parameters while results obtained using the Mundlak approach and the fixed effects specification are similar (concerning time-varying variables), implying that correlation between regressors and individual effects is well captured.

RÉSUMÉ la présente communication applique l'approche de Mundlak au modèle de données spatiales de Durbin pour aider le chercheur appliqué à déterminer dans quelle mesure la spécification des effets aléatoires est adéquate dans cette configuration. Nous proposons un test de ratio de vraisemblance évaluant l'importance de la corrélation entre régresseurs et effets individuels. Contrairement au test de Hausman, l'approche de Mundlak identifie (dans une certaine mesure) les régresseurs corrélés à des effets individuels. Le deuxième avantage est que lorsque la corrélation avec les effets individuels a été modélisée via une régression auxiliaire, la spécification des effets aléatoires fournit des estimateurs convergents, et il est alors possible d’évaluer l'effet de variables constantes dans le temps. Des simulations Monte Carlo étudient les propriétés de ce test de ratio de vraisemblance proposé dans des échantillons de taille finie, et indiquent que, dans certains cas, il présente un meilleur comportement que le test de Hausman. Nous illustrons enfin l'utilité de l'approche étendue de Mundlak en évaluant un modèle de prix des maisons, dans lequel certains déterminants des prix sont constants dans le temps. Nous montrons que si on ne prend pas en compte l'endogénéité des régresseurs par rapport aux effets individuels, on obtient des paramétres estimés non fiables, alors que les résultats obtenus avec l'approche de Mundlak et la spécification des effets fixes sont similaires (sur le plan des variables variant dans le temps), ce qui implique que la corrélation entre régresseurs et effets individuels est bien captée.

EXTRACTO Este estudio extiende el planteamiento Mundlak al modelo espacial de datos de panel (SDM) Durbin para ayudar al investigador aplicado a determinar la idoneidad de la especificación de efectos aleatorios dentro de esta configuración. Proponemos una prueba de relación de la probabilidad (LR) que evalúa la significancia de la correlación entre regresores y efectos individuales. En contraste con la prueba Hausman, el planteamiento Mundlak identifica (hasta cierto punto) los regresores correlacionados con efectos individuales. La segunda ventaja es que, una vez modelada la correlación con efectos individuales a través de una regresión auxiliar, la especificación de efectos aleatorios proporciona estimadores consistentes y puede estimarse el efecto de las variables constantes en el tiempo. Algunas simulaciones de Monte Carlo estudian las propiedades de esta prueba LR propuesta en muestras pequeñas y demuestran que, en algunos casos, se comporta mejor que la prueba Hausman. Finalmente, ilustramos la utilidad del planteamiento Mundlak ampliado estimando el precio de una vivienda donde varios determinantes del precio son constantes en el tiempo. Mostramos que ignorar la endogeneidad de los regresores con respecto a efectos individuales conduce a parámetros estimados no fiables, mientras que los resultados obtenidos mediante el planteamiento Mundlak y la especificación de efectos fijos son similares (en lo concerniente a variables que varan en el tiempo), sugiriendo que la correlación entre regresores y efectos individuales se ha capturado satisfactoriamente

     

非线性动态面板模型的条件GMM估计

基于时间序列的实证分析已经证实,很多经济变量的动态调整过程都存在非线性的平滑转换机制.本文将传统的线性动态面板模型扩展为平滑转换的非线性动态面板模型,并基于对非线性参数的格点搜索,提出了一种简便易行的非线性动态面板模型估计程序--条件GMM估计,其估计量具有一致性.仿真实验结果显示,条件GMM估计量在有限样本下具有良好表现.同时,非线性动态面板模型的条件GMM估计还为在非线性框架下检验面板单位根创造了条件.     

时变系数的空间误差合成模型

本文对时变系数的空间误差合成模型进行了研究,该模型的特征是利用扰动项中的空间个体成分将不同时期的方程联系起来;同时,允许自变量系数和误差项中的空间自回归系数随时间而变化,但每一个时期的自变量系数和误差项中的空间自回归系数是固定不变的。本文使用基于FGLS和GM方法的多阶段估计策略对模型参数进行了估计,证明了估计量的渐进异质性,并利用Monte Carlo方法模拟了其小样本性质。模拟结果表明,估计量的渐近性质随着样本容量的增加而改善。对中国省际知识生产及其空间溢出的实证案例也体现了该模型的应用价值。     

非等间隔动态面板数据模型：估计方法与应用实例

非等间隔动态面板数据模型由于相邻两期观测之间的时间长度不尽相同使得传统动态面板数据模型的估计方法失效,本文提出使用非线性最小二乘、最短距离以及它们的一步估计量对该模型进行估计,证明了这四个估计量的一致性和渐进正态性,同时借助蒙特卡洛模拟的方法验证了它们在有限样本中的估计精度,并且进一步使用所提出的估计量讨论了以往文献由于缺乏相应的估计方法而没有被研究或者充分讨论的问题,得到了一些新的结论。     

Estimating and Forecasting with a Dynamic Spatial Panel Data Model*

This study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006) , a dynamic spatial generalized method of moments (GMM) estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the spatial autoregressive (SAR) error model. The main idea is to mix non‐spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non‐spatial estimators and illustrate our approach with an application to new economic geography.     

Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion

This paper provides an econometric examination of technological knowledge spillovers among countries by focusing on the issue of error cross‐sectional dependence, particularly on the different ways—weak and strong—that this dependence may affect model specification and estimation. A preliminary analysis based on estimation of the exponent of cross‐sectional dependence provides a clear result in favor of strong cross‐sectional dependence. This result has relevant implications in terms of econometric modeling and suggests that a factor structure is preferable to a spatial error model. The common correlated effects approach is then used because it remains valid in a variety of situations that are likely to occur, such as the presence of both forms of dependence or the existence of nonstationary factors. According to the estimation results, richer countries benefit more from domestic R&D and geographic spillovers than poorer countries, while smaller countries benefit more from spillovers originating from trade. The results also suggest that when the problem of (possibly many) correlated unobserved factors is addressed the quantity of education no longer has a significant effect. Finally, a comparison of the results with those obtained from a spatial model provides interesting insights into the bias that may arise when we allow only for weak dependence, despite the presence of strong dependence in the data. Copyright © 2016 John Wiley & Sons, Ltd.     

含空间自回归误差项的空间动态面板模型的检验与模拟

研究目标：探究含空间自回归误差项的空间动态面板数据（SDPD）模型选择和假设检验结果的水平扭曲、检验功效。研究方法：构建含空间自回归误差项的SDPD模型的空间Hausman、LM和LR检验统计量,选择蒙特卡洛模拟进行分析。研究发现：含空间自回归误差项的SDPD模型的各类检验统计量的大样本性质良好;时空滞后项对空间Hausman检验结果的影响比空间滞后项显著;条件LMλ|α、LRλ|α检验是含空间自回归误差项随机效应SDPD模型的最优检验统计量,时空滞后项对LM、LR检验结果的影响比自回归误差项更显著。研究创新：解析了SDPD模型中各空间关联项系数的波动对模型检验精度的影响。研究价值：探究含空间自回归误差项SDPD模型的选择问题,为实证提供理论依据。     

交互效应面板模型的EM算法和MCMC算法

交互效应面板模型是目前计量经济学前沿研究的热点,有着广阔的应用空间。但是对很多应用者而言,模型内的参数估计是一个非常棘手的问题。通常的Newton-Raphson算法在优化似然函数的过程中,常常会出现优化失败的情况。本文依据EM算法和MCMC算法理论,为应用研究者提供了一套获得参数估计值的流程。计算机上的试验证实两种估计方法都非常稳健可靠,并在很多情况下,差异不是很大。     

Testing Exogeneity in the Bivariate Probit Model: A Monte Carlo Study*

We conduct an extensive Monte Carlo experiment to examine the finite sample properties of maximum‐likelihood‐based inference in the bivariate probit model with an endogenous dummy. We analyse the relative performance of alternative exogeneity tests, the impact of distributional misspecification and the role of exclusion restrictions to achieve parameter identification in practice. The results allow us to infer important guidelines for applied econometric practice.     

Information Acquisition and Choice Under Uncertainty

This paper presents a model where individuals have imperfect information and there is an opportunity cost of learning. It shows that the endogenous decision to collect costly information before taking an action has a systematic effect on choices. More precisely, consider two alternatives with ex ante identical expected payoff but different variances. The model predicts that, after the learning process is stopped, a majority of individuals will select the alternative with largest payoff-variance. The result persists when agents have multiple sources of information. Applications to entrepreneurial investments, composition of advisory committees, and judicial decision-making are discussed.     

Inflation Uncertainty, Output Growth Uncertainty and Macroeconomic Performance

We use a bivariate generalized autoregressive conditionally heteroskedastic (GARCH) model of inflation and output growth to examine the causality relationship among nominal uncertainty, real uncertainty and macroeconomic performance measured by the inflation and output growth rates. The application of the constant conditional correlation GARCH(1,1) model leads to a number of interesting conclusions. First, inflation does cause negative welfare effects, both directly and indirectly, i.e. via the inflation uncertainty channel. Secondly, in some countries, more inflation uncertainty provides an incentive to Central Banks to surprise the public by raising inflation unexpectedly. Thirdly, in contrast to the assumptions of some macroeconomic models, business cycle variability and the rate of economic growth are related. More variability in the business cycle leads to more output growth.     

Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor

Abstract

This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965–1994. The spatial autocorrelation due to neighbouring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states’ demand for liquor for 1 year and 5 years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring spatial correlation, fixed effects with spatial correlation, random-effects GLS estimator ignoring spatial correlation and random-effects estimator accounting for the spatial correlation. Based on RMSE forecast performance, estimators that take into account spatial correlation and heterogeneity across the states perform the best for forecasts 1 year ahead. However, for forecasts 2–5 years ahead, estimators that take into account the heterogeneity across the states yield the best forecasts.     

中国城镇居民消费函数的变系数Panel Data模型

本文研究了Panel Data模型的正确设定与识别问题，并利用变系数和变截距Panel Data模型建立了我国城镇居民消费函数，根据模型分析了收入差异对城镇居民消费结构变化的影响。     

基于面板数据模型的林业投入产出效率影响因素研究

基于我国2003-2017年31个省域有关林业经济的面板数据,采用固定效应模型对全国及湖北省林业投入因素和产出效率进行分析。结果显示:相对于全国而言,湖北省的林业资源投资效率是属于中等偏上的,其中投资完成额这一指标做出了巨大贡献,造林面积和人员平均工资起到了辅助作用,而林业就业人数抑制了林业经济发展。     

外商直接投资对我国就业的区域差异与动态效应——基于动态面板数据模型的分析

中国作为最大的发展中国家以及人口最多的国家,就业率高低一直是政府十分关注的宏观经济指标,并且利用外商直接投资增加就业是我国政府外资政策的直接目标之一。本文以我国各个省市区作为横截面单元,利用各横截面单元1988~2005年的样本组成面板数据,进而建立动态面板数据模型来考察FDI对我国不同地区就业的影响。结果表明,FDI对我国不同地区的影响有显著差异。对东部地区而言,FDI对就业有显著的替代效应,而对中、西部地区则效果不明显。