Cointegration Vector Estimation by Panel DOLS and Long‐run Money Demand*

We study the panel dynamic ordinary least square (DOLS) estimator of a homogeneous cointegration vector for a balanced panel of N individuals observed over T time periods. Allowable heterogeneity across individuals include individual‐specific time trends, individual‐specific fixed effects and time‐specific effects. The estimator is fully parametric, computationally convenient, and more precise than the single equation estimator. For fixed N as T→∞, the estimator converges to a function of Brownian motions and the Wald statistic for testing a set of s linear constraints has a limiting χ²(s) distribution. The estimator also has a Gaussian sequential limit distribution that is obtained first by letting T→∞ and then letting N→∞. In a series of Monte‐Carlo experiments, we find that the asymptotic distribution theory provides a reasonably close approximation to the exact finite sample distribution. We use panel DOLS to estimate coefficients of the long‐run money demand function from a panel of 19 countries with annual observations that span from 1957 to 1996. The estimated income elasticity is 1.08 (asymptotic s.e. = 0.26) and the estimated interest rate semi‐elasticity is ?0.02 (asymptotic s.e. = 0.01).     

Excess Volatility of Realized Excess Profit from Currency Speculation in a Two-Country General Equilibrium Model

This paper examines whether the Lucas model can explain stylized facts in foreign exchange markets, by employing Monte Carlo studies. It is assumed that changes in the logarithms of endowments and of money supplies follow a multivariate Markov switching process. From the results of the Monte Carlo studies, with plausible values of the preference parameters, the excess volatility of the realized excess profit from currency speculation, the strong autocorrelation of the forward premium in the sample can be found in the model for four exchange rates. However, the implied covariance between the forward premium and depreciation rates is positive.     

Bias Reduction in Dynamic Panel Data Models by Common Recursive Mean Adjustment*

The within‐group estimator (same as the least squares dummy variable estimator) of the dominant root in dynamic panel regression is known to be biased downwards. This article studies recursive mean adjustment (RMA) as a strategy to reduce this bias for AR(p) processes that may exhibit cross‐sectional dependence. Asymptotic properties for N,T→∞ jointly are developed. When ( log ²T)(N/T)→ζ, where ζ is a non‐zero constant, the estimator exhibits nearly negligible inconsistency. Simulation experiments demonstrate that the RMA estimator performs well in terms of reducing bias, variance and mean square error both when error terms are cross‐sectionally independent and when they are not. RMA dominates comparable estimators when T is small and/or when the underlying process is persistent.     

Dynamic Seemingly Unrelated Cointegrating Regressions

We propose the parametric Dynamic Seemingly Unrelated Regression (DSUR) estimator for simultaneous estimation of multiple cointegrating regressions. DSUR is efficient when the equilibrium errors are correlated across equations and is applicable for panel cointegration estimation in environments where the cross section is small relative to the available time series. We study the asymptotic and small sample properties of the DSUR estimator for both heterogeneous and homogeneous cointegrating vectors. We then apply the method to analyse two long-standing problems in international economics. Our first application revisits the estimation of long-run correlations between national investment and national saving. Our second application revisits the question of whether the forward exchange rate is an unbiased predictor of the future spot rate.     

A model for disruptive technology forecasting in strategic regional economic development

As regions look to increase their economic development activities, technology-based developments and the penchant for long-term developments in disruptive technologies like nanotechnology become an important part of the options available to these regions. There are typically many technologies and therefore product areas that the region, however, can further develop by investing resources in these areas. At the same time, other regions in the world are considering the same areas of great growth and potential financial and social returns. This paper proposes a model that analyzes several important factors that can lead to success in analyzing these factors promoting the idea that policy makers should analyze the situation from different perspectives to reach justifiable decisions. These factors include the research capabilities of the region, its commercialization and manufacturing capabilities and the markets on which they should focus. Several mathematical models are then presented to help in that endeavor.     

Shareholder Conflict as a Determinant of Corporate Social Responsibility Commitment for Shared Growth

In this study, we seek to explore shareholder conflicts as a determinant of large firms' corporate social responsibility (CSR) commitment for shared growth with subcontractors. Using a sample of 232 firms in the Korean chaebols during 2006'12, we find that CSR commitment is negatively related to controlling shareholders' cash flow rights but positively related to control-ownership disparity. We also find that foreign blockholders mitigate the positive relationship between CSR commitment and control-ownership disparity. This study offers a view of how controlling shareholders and blockholders protect their interests while enhancing their reputations as good citizens through CSR commitment.     

Economic transition and growth

Some extensions of neoclassical growth models are discussed that allow for cross‐section heterogeneity among economies and evolution in rates of technological progress over time. The models offer a spectrum of transitional behavior among economies that includes convergence to a common steady‐state path as well as various forms of transitional divergence and convergence. Mechanisms for modeling such transitions, measuring them econometrically, assessing group behavior and selecting subgroups are developed in the paper. Some econometric issues with the commonly used augmented Solow regressions are pointed out, including problems of endogeneity and omitted variable bias which arise under conditions of transitional heterogeneity. Alternative regression methods for analyzing economic transition are given which lead to a new test of the convergence hypothesis and a new procedure for detecting club convergence clusters. Transition curves for individual economies and subgroups of economies are estimated in a series of empirical applications of the methods to regional US data, OECD data and Penn World Table data. Copyright © 2009 John Wiley & Sons, Ltd.     

Panel unit root tests under cross section dependence with recursive mean adjustment

Utilizing recursive mean adjustment (RMA) we provide two unit root tests: the covariate RMA unit root test and the panel feasible generalized RMA unit root test. The proposed panel unit root tests are precise and powerful, especially when T > N.     

Asymptotic distribution of factor augmented estimators for panel regression

In this paper we derive an asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal components estimate of the factor space it is shown that these conditions are satisfied when T/N→0$T/N\to 0$ and N/T³→0$N/T^3\to 0$ under regularity. Monte Carlo studies verify the asymptotic theory.     

Cross-border venture capital investments in Asia: Selection and exit performance

We investigate the investment behavior and exit performance of VCs that have pursued expansion outside their home locations, specifically, in Asia. Our findings indicate that, in the Asian VC markets, foreign VCs have relative advantages over local VCs in terms of size and experience while they are at a disadvantage in information collection and monitoring due to both geographic and cultural distances. When investing alone, foreign VCs are more likely to invest in more information-transparent ventures. Partnership with local VCs helps alleviate information asymmetry and monitoring problem and has positive implication for the exit performance of local entrepreneurial firms. Specifically, we find that after controlling for the endogeneity of selection, firms with both foreign and local VC partnership are about 5% more likely to successfully exit.