Are Any Growth Theories Robust?

Durlauf, Steven N.; Kourtellos, Andros; Tan, Chih Ming

doi:10.2139/ssrn.969530

Cited by 69 publications

(148 citation statements)

References 52 publications

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“…This is not surprising since this indicates that those variables are not correlated with growth per capita (see Magnus, Powell, and Prufer (2010)). The sign of 'dpop' that is found for the plug-in and mtrMSE methods is in line with Solow's model, see Solow (1956) and Durlauf, Kourtellos, and Tan (2008). The results for the mdetMSE method are close to those of the OPT method.…”

Section: Growth Model Linear Regressionsupporting

confidence: 79%

Minimum Mean Squared Error Model Averaging in Likelihood Models

2016

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A data-driven method for frequentist model averaging weight choice is developed for general likelihood models. We propose to estimate the weights which minimize an estimator of the mean squared error of a weighted estimator in a local misspecification framework. We find that in general there is not a unique set of such weights, meaning that predictions from multiple model averaging estimators might not be identical. This holds in both the univariate and multivariate case. However, we show that a unique set of empirical weights is obtained if the candidate models are appropriately restricted. In particular a suitable class of models are the so-called singleton models where each model only includes one parameter from the candidate set. This restriction results in a drastic reduction in the computational cost of model averaging weight selection relative to methods which include weights for all possible parameter subsets. We investigate the performance of our methods in both linear models and generalized linear models, and illustrate the methods in two empirical applications.

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Section: Growth Model Linear Regressionsupporting

confidence: 79%

Minimum Mean Squared Error Model Averaging in Likelihood Models

2016

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“…In this paper we will follow an approach for the construction of best-case scenario of aggregate indices based on stochastic dominance e¢ ciency (SDE hereafter) analysis that avoids the problems mentioned above. 3 Constructing a best-case scenario governance index based on SDE analysis has advantages, since it provides an e¢ cient index resulting from the least variable combination of components 1 See, for example, Neumayer (2002); Easterly and Levine (2003); Beck and Laeven (2006); Easterly (2007); Durlauf et al (2008) for the similar use of the world governance indicators.…”

Section: Introductionmentioning

confidence: 99%

Measuring world governance: revisiting the institutions hypothesis

Pinar

2014

Empir Econ

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We consider the weighting scheme that results in a best-case scenario in the construction of the World Governance Indicators (WGIs), a proxy of institutional quality. To do that we use an approach that relies on consistent tests for stochastic dominance e¢ ciency of a given index with respect to all possible indices constructed from a set of individual components. The test statistics and the estimators are computed using mixed integer programming methods. The results show that the equally-weighted (…xed weights) composite WGI index is not the best-case scenario and that governance indicators at di¤erent years should be weighted di¤erently. Furthermore, we revisit the institutions hypothesis in the empirical growth literature, where institutional quality is the main determinant of long term development. We …nd that not only institutions matter for economic development, but also geography and macroeconomic policies do a¤ect economic development directly. JEL Classi…cations: C12; C13; C14; C15; O1; O57

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“…11 Apart from these endogeneity-related aspects, the estimations might be biased by a fifth issue. Although growth theory provides well-established suggestions for the estimation of growth relationships, it is ex ante not clear which economic growth model to use and which functional form is appropriate for the effect of SF payments (Durlauf, Kourtellos and Tan, 2008). There may be non-linearities and interactions with covariates, which may lead to biased estimates if they are not taken into account.…”

Section: Main Econometric Issues and Potential Solutionsmentioning

confidence: 99%

Econometric Evaluation of EU Cohesion Policy: A Survey

Hagen

Mohl²

2016

Empirical Evidence on the Macroeconomic Effects of EU Cohesion Policy

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp09052.pdf Non-technical summaryMore than one third of the total EU budget is spent on so-called Cohesion Policy via the structural funds. Its main purpose is to reduce disparities among EU regions and to promote economic growth and convergence. Therefore, the convergence process of EU regions is a question of high political importance. The overall empirical evidence points to a small convergence effect of all or at least some European regions. However, whether or not this potential success results from EU Cohesion Policy remains an open question. The existing empirical evidence has provided mixed, if not contradictory, results. While some authors do find evidence of a positive impact of structural funds on economic growth, others find little to no impact at all. Against this background, this paper provides a fundamental review of the econometric evaluation of EU Cohesion Policy in order to shed light on the reasons for the diverging results. It has been shown that the econometric evaluation of EU Cohesion Policy is hampered by several econometric issues. Based on these issues we discuss potential solutions on how to cope with these problems and discuss the related literature.The most that can be concluded from empirical studies using country-level data is that Cohesion Policy seems to be only conditionally effective. Given a good quality institutional setup or decentralised governmental structures, Cohesion Policy has a positive impact on growth. However, using regional level data might be the preferable alternative because, first, EU Cohesion Policy focuses on the development and convergence of regions and, second, the robustness of the results is increased by the higher number of cross sections. The majority of the studies based on EU regions find at least a weak positive effect.One explanation for the weak results might be the fact that almost all studies are derived from a neoclassical growth model assuming that EU Cohesion Policy increases investments, and ultimately raising the economic growth rate. However, there is some empirical evidence that Cohesion Policy may only have a modest impact on investments. Moreover, we know very little about the labour market impact of EU Cohesion Policy. Hence, one task for future studies will be to investigate more thoroughly the channels through i which EU Cohesion Policy works.Another reason for the inconclusive empirical results m...

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Are Any Growth Theories Robust?

Cited by 69 publications

References 52 publications

Minimum Mean Squared Error Model Averaging in Likelihood Models

Minimum Mean Squared Error Model Averaging in Likelihood Models

Measuring world governance: revisiting the institutions hypothesis

Econometric Evaluation of EU Cohesion Policy: A Survey

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