A Panel Data Analysis of Corporate Attributes and Stock Prices for Indian Manufacturing Sector

Stock prices have always been considered as unpredictable phenomena due to their dynamic patterns. Identifying the forces that contribute to variations of stock prices is probably one of the most researched areas in finance. This study relates stock prices to the stock volatility （measured by beta） and to corporate attributes, i.e., size, liquidity, profits, leverage, and returns. The study is based on manufacturing sector in India, and it is based on a sample of 3,027 manufacturing companies during the periods from 1991-1992 to 2006-2007 collected from the Centre for Monitoring Indian Economy （CMIE） database. The regressions were performed with the dummies for time effect and firm effect separately and then for both effects together. Panel data models have been used to estimate the stock prices equation. The model finds out fixed and random effects between independent and explanatory variables and analyzes them through Hausman test. The paper also studies multicollineairity that may exist amongst the selected variables. The study shows that volatility （represented by Beta）, profit （represented by earnings per share （EPS））, and size （represented by market capitalization （MCAP）） significantly influence the stock prices （at the level of 5%）. Panel data analysis using Hausman test supports the fixed effect model.     

Semiparametric Regression Analysis of Panel Count Data: A Practical Review

Panel count data arise in many applications when the event history of a recurrent event process is only examined at a sequence of discrete time points. In spite of the recent methodological developments, the availability of their software implementations has been rather limited. Focusing on a practical setting where the effects of some time‐independent covariates on the recurrent events are of primary interest, we review semiparametric regression modelling approaches for panel count data that have been implemented in R package spef . The methods are grouped into two categories depending on whether the examination times are associated with the recurrent event process after conditioning on covariates. The reviewed methods are illustrated with a subset of the data from a skin cancer clinical trial.     

Testing the Political Replacement Effect: A Panel Data Analysis*

This article tests for the existence of the political replacement effect, as suggested by Acemoglu and Robinson: [American Political Science Review, Vol. 100, pp. 115–131]. They argue that the implementation of market‐oriented reform is crucially driven by the political calculus of incumbent governments: they implement economic policy change if such a choice is not expected to reduce their chances to retain power. This implies a non‐monotonic relationship between the level of political competition and the extent of economic reform. We test this hypothesis using data for 102 countries over the period 1980 to 2005. Our results strongly support the theory.     

Decomposition of Technological Change and Factor Bias in Indian Power Sector: An Unbalanced Panel Data Approach

Technological change and factor bias in the Indian power sector are analyzed using a translog cost function. Various components of technological progress and factor bias are identified and estimated, using a 21 year unbalanced panel data of Indian states and union territories. Heterogeneity across states is incorporated in the model using a variance component model. Appropriate corrections are made for unbalanced panel data. Empirical results show that the annual average rate of technological progress has been 2.4% for the country as a whole. Accumulation of knowledge and increasing scale are found to be the major factors contributing to technological progress. In contrast, the effects of factor price changes and fixed capital accumulation on technological progress have been unfavorable. Pure factor bias measure indicate saving in the use of fuel and labor, and increased use of materials. Tests are performed to check the curvature properties of the underlying technology.     

交通基础设施投资对经济增长推动作用研究——基于中国省级面板数据分析

本文依据生产函数模型和我国1994-2007年度30个省级地区(重庆市包含在四川省内)面板数据分析交通基础设施投资对我国各省份经济增长推动作用,得出在规模报酬不变条件下,我国各省份交通基础设施投资产出系数,进而得出经济越发达地区,交通基础设施投资对于经济增长贡献越大的结论。     

农村物流固定资产投资对农业产出的支持分析——基于30个省份面板数据的实证研究

利用2001-2010年间30个省份的面板数据,分析了农村物流固定资产投资对各省份农业发展增长力度和差异影响。研究表明:农村物流固定资产投资对农业产出影响明显;中西部农村物流固定资产投资对农业产出影响更大,呈现区域差异化。加大中西部农村物流固定资产投资和东部地区农村物流资源整合是提高农业产出的重要途径。     

农村物流固定资产投资对农业产出的支持分析——基于30个省份面板数据的实证研究

利用2001-2010年间30个省份的面板数据,分析了农村物流固定资产投资对各省份农业发展增长力度和差异影响.研究表明:农村物流固定资产投资对农业产出影响明显;中西部农村物流固定资产投资对农业产出影响更大,呈现区域差异化.加大中西部农村物流固定资产投资和东部地区农村物流资源整合是提高农业产出的重要途径.     

旅游产业发展影响因素的区域差异研究——基于中国省际面板数据的分析

近年来,随着社会经济的快速发展与国家宏观政策的引导,我国旅游业发展进入新的战略机遇期。随着旅游产业规模的持续扩张,空间非均衡性已成为我国旅游经济发展的典型特征。运用空间数据分析与面板数据分析方法 ,选取中国大陆31个省、市、区2000~2015年反映国内旅游业发展的指标及其影响因素,构建计量模型。实证研究发现影响我国旅游产业发展的诸多因素存在一定滞后效应。同时,滞后模型统计结果显示,影响我国东、中、西部地区旅游业发展的具体因素存在显著差异,表明各区域旅游产业发展有着不同的特征。基于统计分析结果,并结合国家全域旅游发展战略与旅游供给侧改革的要求,建议东部地区旅游产业的发展需要"增容提质",中部地区需要"减负促需",而西部地区则需要"互联互通"。     

影响电力消费的因素——基于省域面板数据分析

进行中国(不含港、澳、台)电力消费影响因素分析,对中国各个地区电力供求与经济协调发展意义重大。本文收集了1999-2012年以来的31个省区电力消费量的相关数据,对此建立多元回归模型,再对模型分析检验,求该模型,并最终得到模型。     

浅析LED平板灯生产制造中的问题

LED平板灯具是LED灯具型式的一种,由于产品刚进入大规模生产阶段,生产工艺技术等均未成熟,造成很多质量问题。为此本文由生产制造流程入手,从各组成部件质量要求方面查找LED平板灯质量问题的原因。     

A Principal Components Analysis of Common Stochastic Trends in Heterogeneous Panel Data: Some Monte Carlo Evidence

In this paper we propose a new approach based on principal components analysis to test for the number of common stochastic trends driving the non-stationary series in a panel data set. This test has the advantage that it is also consistent when there is a mixture of I (0) and I (1) series, making it unnecessary to pre-test the panel for unit root. Furthermore, the test solves the problem of dimensionality encountered in large panel data sets.     

外商直接投资、前后向关联与技术溢出——基于江苏制造业面板数据的经验研究

本文利用2000～2006年江苏28个制造业行业的面板数据,检验了江苏FDI通过前后向关联对内资企业产生的技术溢出效应。结果表明,整个外资制造行业以及外资高科技行业,通过前后向关联产生的溢出效应都非常显著。通过对全要素生产率的分解发现,整个外资制造行业正向溢出主要由于促进内资企业技术进步所导致,对于技术效率的溢出效应为负;外资高科技行业通过前后向关联对内资企业技术进步产生了积极的溢出,对内资制造业技术效率后向关联溢出效应为正,前向关联溢出效应为负。     

陕西省装备制造产业国际竞争力实证分析——基于2007-2010年进出口面板数据

装备制造业作为国民经济发展的基础性、战略性产业,体现了一个国家的科技实力和国际竞争力。本文从贸易视角出发,以进出口数据为基础建立了陕西省装备制造业国际竞争力的评价指标体系,采用单项显示性指标比较分析和主成分分析方法,运用统计软件SPSS18.0,对陕西省装备制造业2007—2010年的面板数据进行实证分析,得到在进出口数据基础上的陕西省装备制造业国际竞争力,并提出提升陕西省装备制造业国际竞争力的对策建议。     

我国东、中、西部外商直接投资(FDI)区位差异因素的Panel Data分析

目前我国已成为全球FDI最具吸引力的国家，但FDI(Foreign Direct Investment)在我国东部、中部和西部的分布极不均衡。本文采用Panel Data分析方法，选取1986～2003年全国各省份的历史统计数据，分别建立各时期东部、中部及西部地区的。Panel Data模型，对相应可能影响FDI区位差异的影响因素进行实证分析，旨在为我国决策各地区引进FDI提供参考。     

A Partially Heterogeneous Framework for Analyzing Panel Data

This article proposes a partially heterogeneous framework for the analysis of panel data with fixed T. In particular, the population of cross‐sectional units is grouped into clusters, such that slope parameter homogeneity is maintained only within clusters. Our method assumes no a priori information about the number of clusters and cluster membership and relies on the data instead. The unknown number of clusters and the corresponding partition are determined based on the concept of ‘partitional clustering’, using an information‐based criterion. It is shown that this is strongly consistent, that is, it selects the true number of clusters with probability one as N→∞. Simulation experiments show that the proposed criterion performs well even with moderate N and the resulting parameter estimates are close to the true values. We apply the method in a panel data set of commercial banks in the US and we find five clusters, with significant differences in the slope parameters across clusters.     

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.