Abstract:This paper explores the impact of error cross-sectional dependence (modelled as a factor structure) on a number of widely used IV and generalized method of moments (GMM) estimators in the context of a linear dynamic panel data model. It is shown that, under such circumstances, the standard moment conditions used by these estimators are invalid -- a result that holds for any lag length of the instruments used. Transforming the data in terms of deviations from time-specific averages helps to reduce the asymptoti… Show more
“…Second, the problem of cross‐sectional error dependence can lead to serious problems in the estimation of short dynamic panels. Sarafidis and Robertson (2009) demonstrate that under cross‐sectional error dependence, the GMM estimator is inconsistent as N →∞ for fixed T , which holds for any lag length of the instruments used.…”
Cross-province growth regressions for China are estimated for the reform period. Two research questions are asked. Can the regressions help us to understand why China as a whole has grown so fast? What types of investment matter for China's growth? We address the problem of model uncertainty by adopting two approaches to model selection to consider a wide range of candidate predictors of growth. Starting from the baseline equation, the growth impact of physical and human capital is examined using panel data techniques. Both forms of capital promote economic growth. 'Investment in innovation' and private investment are found to be particularly important. Secondary school enrolment contributes to growth, and higher education enrolment even more so.
“…Second, the problem of cross‐sectional error dependence can lead to serious problems in the estimation of short dynamic panels. Sarafidis and Robertson (2009) demonstrate that under cross‐sectional error dependence, the GMM estimator is inconsistent as N →∞ for fixed T , which holds for any lag length of the instruments used.…”
Cross-province growth regressions for China are estimated for the reform period. Two research questions are asked. Can the regressions help us to understand why China as a whole has grown so fast? What types of investment matter for China's growth? We address the problem of model uncertainty by adopting two approaches to model selection to consider a wide range of candidate predictors of growth. Starting from the baseline equation, the growth impact of physical and human capital is examined using panel data techniques. Both forms of capital promote economic growth. 'Investment in innovation' and private investment are found to be particularly important. Secondary school enrolment contributes to growth, and higher education enrolment even more so.
“…In the same line of argument, GMM estimates are also inconsistent because the moment conditions used by GMM are violated as N ? ∞ for fixed T (Sarafidis & Robertson, 2009). Westerlund and Edgerton (2008, p. 666) note that:…”
Section: Estimation Methodsmentioning
confidence: 97%
“…Moscone and Tosetti () point out that when the data are cross‐sectionally dependent, the conventional estimates are inefficient and estimated standard errors are biased. In the same line of argument, GMM estimates are also inconsistent because the moment conditions used by GMM are violated as N → ∞ for fixed T (Sarafidis & Robertson, ). Westerlund and Edgerton (, p. 666) note that:…important problem is that the first generation of tests has been unable to handle cross‐sectional dependence.…”
The study examines the role of foreign capital and remittance inflows in the domestic savings of 63 developing countries for 1971–2010, paying attention to likely differential effects of FDI, portfolio investment, foreign aid and remittances. The conventional homogeneous panel estimates suggest that foreign aid and remittance flows have a significant negative impact on domestic savings. However, these techniques ignore cross‐section dependence and parameter heterogeneity properties and hence yield biased and inconsistent estimates. When we allow for parameter heterogeneity and cross‐sectional dependence by employing Pesaran's () Common Correlated Effects Mean Group estimator technique, only remittances crowd out savings.
“…The IV approach is preferable when errors show a cross-sectional dependence, as in our case (Sarafidis and Robertson, 2009) and where institutions are characterized by high persistence across time, as in Italy. In the presence of interaction terms in the model, moreover, the two-stage IV strategy can be the most parsimonious way of addressing endogeneity (Wooldridge, 2010).…”
Section: Addressing the Endogeneity Of The Institutional Variablementioning
European and national policy‐makers have highlighted the role of the cohesion policy in smoothing the effects of the crisis during the programme period 2007–13. To support these claims, however, specific evidence is needed. This article studied the relations between the absorption of the EU funds and regional labour markets in Italian regions during the Great Recession. By applying different panel data models to new data on cohesion policy, three main results were achieved. We found that the cohesion policy made a contribution to the resilience of Italian regional labour markets. Yet the short‐term consequences of the cohesion policy on regional economies were conditional on the heterogeneous quality of regional institutions. We also found that the policy changes introduced in Italy during the crisis increased the effectiveness of the cohesion policy. The analysis was controlled for endogeneity issues and alternative specifications.
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