Growth Determinants Revisited Using Limited‐Information Bayesian Model Averaging

Journal of Applied Econometrics - Tập 31 Số 1 - Trang 106-132 - 2016
Alin Mirestean1, Charalambos Tsangarides1
1International Monetary Fund Research Department Washington DC USA

Tóm tắt

SummaryWe revisit the growth empirics debate using a novel limited‐information Bayesian model averaging framework in short T panels that addresses model uncertainty, dynamics, and endogeneity. We construct an estimator without restrictive distributional assumptions, illustrate its performance using simulations, and apply it to the investigation of growth determinants. Once model uncertainty, dynamics, and endogeneity are accounted for, we identify several factors that are robustly correlated with growth. We find the strongest support for the neoclassical growth variables including initial income and proxies for physical and human capital accumulation, as well as evidence in favor of both fundamental and proximate factors including macroeconomic policy, geography, and ethnic heterogeneity. In addition, we demonstrate that applying methodologies that do not account for either dynamics or endogeneity yields different sets of robust determinants. Copyright © 2015 John Wiley & Sons, Ltd.

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Tài liệu tham khảo

10.1016/0304-4076(94)01641-C

10.1016/S0304-4076(00)00077-4

ArellanoM.2003.Modelling optimal instrumental variables for dynamic panel data models. CEMFI working paper 0310.

10.1016/0304-4076(94)01642-D

10.2307/2951771

Barro RJ, 1997, Determinants of Economic Growth: A Cross‐Country Empirical Study

10.1016/S0304-4076(98)00009-8

10.1093/wber/15.2.229

10.1016/j.jeconom.2005.02.006

10.1111/j.1368-423X.2009.00299.x

10.1016/j.jmva.2004.05.010

ChenH MiresteanA TsangaridesC.2009.Limited information Bayesian model averaging for dynamic panels with short time periods. IMF working paper 09/74.

ChenH MiresteanA TsangaridesC.2011.Limited information Bayesian model averaging for dynamic panels with an application to a trade gravity model. IMF working paper 11/230.

10.1016/j.jeconom.2008.10.013

10.1016/j.jeconom.2004.11.004

10.1016/S1574-0048(99)01007-1

10.1016/S1574-0684(05)01008-7

10.1111/j.1468-0297.2007.02123.x

EicherTS LenkoskiA RafteryA 2009Bayesian Model Averaging and Endogeneity Under Model Uncertainty: An Application to Development Determinants Working Papers UWEC‐2009‐19‐FC University of Washington Department of Economics.

10.1002/jae.1112

Lenkoski A, 2014, Two‐Stage Bayesian Model Averaging in Endogenous Variable Models, Econometric Reviews, Taylor & Francis Journals, 33, 122

10.1002/jae.623

10.1016/S0304-4076(00)00076-2

Hall AR, 2005, Generalized Method of Moments

10.1111/j.1468-2354.2007.00463.x

HongH PrestonB.2008.Bayesian averaging prediction and nonnested model selection. National Bureau of Economic Research. NBER working paper 14284.

10.1080/01621459.1995.10476572

10.1080/01621459.1995.10476592

10.1016/S0304-4076(01)00119-1

10.1016/j.jeconom.2012.06.005

LeamerEE.1978Specification Searches: Ad Hoc Inference with Nonexperimental Data Wiley.

10.1162/REST_a_00154

10.2307/271063

10.1093/biomet/83.2.251

10.1177/0049124199027003005

Ragusa G, 2008, Bayesian likelihoods for moment condition models

10.1111/j.1468-0084.2008.00542.x

SachsJ‐D WarnerA‐M.1995.Natural Resource Abundance and Economic Growth Papers 517a Harvard ‐ Institute for International Development.

10.1257/0002828042002570

10.1093/biomet/92.1.31

10.2307/1884513

TsangaridesC.2004.A Bayesian approach to model uncertainty. IMF working paper 04/68.

10.1016/j.jeconom.2004.02.005

ZellnerA TobiasJ RyuH.1997.Bayesian method of moments (BMOM) analysis of parametric and semiparametric regression models Proceedings of the Section on Bayesian Statistical Science:Alexandria VA;211–216.

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Journal of Applied Econometrics

Tập 31 Số 1

106-132

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