联立方程模型的工具变量方法质疑与商榷

本文质疑联立方程模型前定变量的工具变量性质：前定变量并不保证与当期行为解释变量的相关性,由其构建的工作回归元所完成的分阶段最小二乘估计因而并非两阶段最小二乘估计。建议按照简约式方程构建工作回归元,其具有模型数理逻辑支持下的可替代意义。工作回归元的不同导致结构式方程分阶段最小二乘估计的不同结果,之于恰好识别方程则揭示了业内关于间接最小二乘估计方法的一个误区。     

我国宏观经济运行动态模拟与预测

一、研究的意义。监测我国宏观经济运行状况是否良好。经济是否能持续、快速、稳定、健康地发展。一直是我国学和政府时刻关注的焦点。而这一评价过程又是通过运用诸多宏观经济指标及指标间的联系来衡量的。在以往大多数研究中，是通过逐个建立单个指标对其他一个或多个指标相应的单一方程模型，对未来有关宏观经济指标进行预测。但是单方程模型有一个隐含的假定，就是被解释变量与解释变量之间如果有因果关系。则这种关系是单向的，即解释变量是原因，被解释变量是结果。单方程模型只是从一个方向解释了变量间的因果关系，因变量与解释变量之间没有反馈式的关系。但是在国民经济运行中，根据这种单向因果关系建立模型进行预测几乎是没有意义的。因为解释变量与被解释变量之间有着双向或联立关系，这时就需要采用联立方程模型．它可以使我们考虑多个变量之间的相互关系．通常这类模型由一组回归方程构成。这样，在估计一个方程的参数时就可以运用方程组中其他方程所提供的信息。我们可以运用一些统计软件来对这些方程进行估计。本采用计量经济学软件Eviews 3．1来对联立方程进行估计。在单方程模型里。普通最小二乘法是最适合这种模型的．但是两个或多个内生变量的同时存在需要我们使用另外的建模和估计方法。因为方程的联立性会使普通最小二乘法得到的参数估计量不一致。因为在联立方程模型中，一个方程中的内生变量往往又影响另一个方程中的其他变量，因此误差项与内生变量相关，所以普通最小二乘估计量也将是有偏和不一致的。     

趋势成分引起的虚假回归问题解决方法研究

本文研究了由序列中趋势成分引起的虚假回归问题的解决方法。发现在模型设定式中加入趋势变量,并考虑趋势存在结构突变的情况,再根据残差是否存在自相关进行可行广义最小二乘（FGLS）或普通最小二乘（OLS）估计,可以有效解决趋势成分引起的虚假回归问题。通过理论分析表明,采用本文中的估计方法,所得检验两序列是否为虚假相关的t统计量渐近服从标准正态分布或与标准正态非常接近的分布。Monte Carlo模拟证实了该方法的有效性。最后以Yule(1926)中两高度虚假相关的时间序列为例,佐证文中结论。     

基于偏最小二乘的泰国菠萝出口影响因素实证研究

本文采用偏最小二乘回归模型(PLS),以泰国菠萝贸易为例,通过变量投影重要性准则筛选自变量,由交叉有效性提取主成分,进而建立偏最小二乘回归模型。深入分析了各指标对泰国菠萝出口贸易的影响。研究表明泰国菠萝出口与原料价格及工厂生产加工速度密切相关,并且偏最小二乘回归的拟合效果优于普通最小二乘回归。     

样本数据缺失情形下泊松过程的强度估计和检验

文章讨论了样本数据缺失情形下泊松过程的强度估计和检验问题。用极大似然估计、矩估计法和最小二乘估计法对强度进行估计,分别得出了极大似然估计强度的迭代方法,矩估计值及最小二乘估计值。证明了矩估计值和最小二乘估计值的无偏性和相合性,导出了其统计量的极限分布。最后,对两个Poisson过程的差异进行了假设检验同时给出渐近置信区间。     

电力系统抗差状态估计研究

状态估计能高效地估计出电力系统最佳的运行状态。测试并比较了基于原对偶内点法的加权最小绝对值估计与基于牛顿法求解的二次-线性准则、加权最小二乘估计的估计精度与计算效率,由测试结果可知,加权最小二乘估计虽有较高的计算效率,但估计精度偏差,而加权最小绝对值估计有着较高的估计精度,但较低的计算效率也限制了其工程应用。     

财务变量与预期β系数关系的实证分析

本文在国内外关于β系数的研究成果基础上分析了影响β系数的相关因素,以沪深两市所有A股上市公司为研究样本,运用最小二乘回归分析方法计算样本公司2005年的β系数,在此基础上分析了β系数与32个相关财务变量的关系。研究结果显示:β系数只与其中的9个财务变量显著相关。     

财务变量与预期β系数关系的实证分析

本文在国内外关于p系数的研究成果基础上分析了影响β系数的相关因素,以沪深两市所有A股上市公司为研究样本,运用最小二乘回归分析方法计算样本公司2005年的β系数,在此基础上分析了β系数与32个相关财务变量的关系.研究结果显示:β系数只与其中的9个财务变量显著相关.     

非观测效应和教育收益率

本文运用现代计量方法，在控制了非观测效应的基础上估计了我国1989、1991、1993、1997、2000年的教育收益率，发现：（1）随着改革的深化，教育和劳动市场机制日益发挥重要作用，从1989～2000年，我国的教育收益率迅速提高；（2）非观测效应，以及样本选择偏差和测量误差所引发的内生性问题对结果的影响很大，导致普通最小二乘法低估了我国的教育收益率。     

人力资本分布、能源资源约束与区域经济发展

以1990-2012年我国29个省际面板数据为对象,建立线性和非线性的经济增长计量模型,运用二阶段最小二乘估计回归方法,考察省际区域能源资源消耗量、人力资本平均存量、人力资本的分布对区域经济发展的影响,并提出可行性建议。     

Bias of OLS Estimators due to Exclusion of Relevant Variables and Inclusion of Irrelevant Variables

In this paper, I discuss three issues related to bias of OLS estimators in a general multivariate setting. First, I discuss the bias that arises from omitting relevant variables. I offer a geometric interpretation of such bias and derive sufficient conditions in terms of sign restrictions that allows us to determine the direction of bias. Second, I show that inclusion of some omitted variables will not necessarily reduce the magnitude of bias as long as some others remain omitted. Third, I show that inclusion of irrelevant variables in a model with omitted variables can also have an impact on the bias of OLS estimators. I use a running example of a simple wage regression to illustrate my arguments.     

Multiple capital inputs, Q, and investment spending

Despite their explicit treatment of dynamics and solid theoretical basis, investment models based on the Brainard-Tobin Q have recorded a generally disappointing empirical performance. When the Q model is expanded to recognize the possibility that the value of the firm depends on two or more capital inputs with differing adjustment cost technologies, the econometric equation following from optimizing behavior includes Q as well as a set of additional explanatory variables. The importance of these omitted variables is assessed, and the capital homogeneity assumption for equipment and structures implicit in Conventional Q models is rejected. The Multi-Capital Q model is then extended in two ways: (i) adding inventory, research and development, and labor as quasi-fixed factors and (ii) exploring the sensitivity of the instrumental variables estimates to normalization. We conclude that the Multi-Capital Q model is a useful extension that overcomes an important omitted variables problem in the Conventional Q framework.     

Omitted variables,variability of estimated parameters and the appearance of autocorrelated disturbances

This paper shows that in a standard regression model with omitted variables, the OLS formula for the estimated variance matrix of the regression coefficients is more likely to underestimate the appropriate criterion of estimator reliability which is the Mean Square Errors matrix. Using examples of two and three regressor models, we show that overestimation, though possible, occurs in rather special cases. Throughout, our analysis is contrasted with that of Chaudhuri (1977) and clarifies some ambiguities of that paper. Finally, we disagree with Chaudhuri who distinguishes between the corresponding coefficients in the correct and the misspecified models. This distinction is inappropriate and leads to a misplaced criticism of some GLS variants when errors are serially correlated. A first-order Markov process is an inexact representation of serial correlation which is due to omitted regressors.     

Import demand behavior in Africa: Some new evidence

Some African economies have experienced increases in the level of their foreign exchange reserves as well as increases in their import volume. Theory suggests that as the level of exchange reserves increases, it may affect the demand for imports since more funds will be available for imports. This paper examines import demand behavior in three African economies, namely Kenya, Nigeria and South Africa. An empirical analysis of import demand behavior is presented, based on the dynamic error-correction model, which allows an explicit parameterized division of effects into long-run influences, short-term adjustment and error-correction term. It uses econometric techniques organized around Johansen and Harris–Inder cointegration analyses; fully modified OLS, dynamic OLS and non-linear OLS to estimate long-run import demand functions.     

Raising the bar (9)

ABSTRACT

This editorial summarizes the papers published in issue 13(4) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper develops an economic geography model with trade costs in all sectors and different shares of unskilled labour in all locations. The second paper translates an economic geography model into a dynamic spatial econometric model and then estimates the unknown parameters to test for congestion spillover effects among Chinese cities. The following paper also investigates spillover effects, but of sovereign and banking risks across countries. The fourth paper empirically examines if a higher market potential results in higher average productivity and lower productivity dispersion of Italian retailers. The fifth paper demonstrates that modelling more than one spatial lag in the independent variables, using different specifications of the spatial weight matrix, can be used as a tool to correct for an omitted variable bias. The final paper develops a test for the existence of non-parametric non-linearities in a linear spatial econometric model.     

The endogeneity bias in the relation between cost-of-debt capital and corporate disclosure policy

Abstract

The purpose of this paper is twofold. First, we provide a discussion of the problems associated with endogeneity in empirical accounting research. We emphasize problems arising when endogeneity is caused by (1) unobservable firm-specific factors and (2) omitted variables, and discuss the merits and drawbacks of using panel data techniques to address these causes. Second, we investigate the magnitude of endogeneity bias in Ordinary Least Squares (OLS) regressions of cost-of-debt capital on firm disclosure policy. We document how including a set of variables which theory suggests to be related with both cost-of-debt capital and disclosure and using fixed effects estimation in a panel data-set reduces the endogeneity bias and produces consistent results. This analysis reveals that the effect of disclosure policy on cost-of-debt capital is 200% higher than what is found in OLS estimation. Finally, we provide direct evidence that disclosure is impacted by unobservable firm-specific factors that are also correlated with cost of capital.     

The Productivity of Government Spending in Asia: 1983–2000

Leightner [The Changing Effectiveness of Key Policy Tools in Thailand. Institute of Southeast Asian Studies for East Asian Development Network, EAON Working Paper 19(2002)x0219-6417] develops a new analytical technique, named Reiterative Truncated Projected Least Squares (RTPLS), which produces 1/2 the error of OLS when omitted variables interact with the included independent variable. In this paper, RTPLS is applied to annual panel data on government spending and GDP from 1983 to 2000 for 23 developing Asian and Pacific countries. RTPLS produces estimates for the government spending multiplier for these countries and shows how omitted variables have affected these multipliers across countries and over time.JEL Classification: C13, E62, O23     

A new approach to estimating earnings forecasting models: Robust regression MM-estimation

This study focuses on the impact of model estimation methods on earnings forecast accuracy. Compared with an ordinary least squares (OLS) regression combined with winsorization, robust regression MM-estimation improves the earnings forecast accuracy of all the models examined, especially for those with more variables. My findings indicate that the impact of outliers on the OLS regression increases with the number of variables in the models, alerting researchers who use OLS regressions for forecasting. My findings explain the puzzling negative relationship between earnings forecast accuracy and the number of model variables in prior research. Moreover, I demonstrate the valuation implications of earnings forecasted using robust regression MM-estimation. This study contributes to earnings forecasting, valuation, and influential observation treatment in forecasting.     

Indirect inference in structural econometric models

This paper considers parametric inference in a wide range of structural econometric models. It illustrates how the indirect inference principle can be used in the inference of these models. Specifically, we show that an ordinary least squares (OLS) estimation can be used as an auxiliary model, which leads to a method that is similar in spirit to a two-stage least squares (2SLS) estimator. Monte Carlo studies and an empirical analysis of timber sale auctions held in Oregon illustrate the usefulness and feasibility of our approach.     

How does the benefit effect vary as unemployment spells lengthen?

This paper investigates how the effect of income while unemployed on the probability of an individual leaving unemployment varies with the length of time that the individual has been unemployed. We examine this question in the context of a variety of alternative econometric models. We extend the Proportional Hazards model with unrestricted baseline hazard to one in which there are unrestricted effects of a subset of the explanatory variables and also consider models that can be estimated as series of binary response models. The proportional hazard restrictions are rejected for the sample of British unemployed men analysed and in the binary sequence framework Logit and Probit models based on symmetric distributions dominate (in likelihood terms) the Extreme Value form model implied by extension of the Proportional Hazards formulation. Logit models with a flexible form for the duration dependence which also incorporate unobserved heterogeneity in a flexible way are estimated. The results for all formulations indicate a rapidly declining effect of unemployment income as a spell lengthens, with no significant effect for the long-term unemployed. The preferred specifications which allow for omitted heterogeneity indicate no significant effect after about 5 months, and this result is robust to the inclusion or exclusion of previous labour-market experience variables and to the choice of mixing distribution.