Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach

Much economic growth research has been devoted to determining the explanatory variables that explain cross-country variation in growth rates. A frequently cited problem with this literature is that the number of potential growth regressors is vast, potentially exceeding the number of countries available for study. Thus, researchers are faced with the task of arbitrarily specifying which explanatory variables to include in their growth regressions, raising concerns about how confident we can be in their results. These concerns were magnified by the influential paper of Ross Levine and David Renelt (1992), in which they employ a variation of Edward E. Leamer's (1983) extreme bounds analysis to test the robustness of conventional growth regression coefficients to changes in the set of conditioning variables. They conclude that the results of this literature are extremely fragile, with the only robust determinants of growth being physical capital investment, initial income, and secondary school enrollment. In contrast, they demonstrate the fragility of a host of fiscal, monetary, and trade policy variables, as well as measures of political and economic stability and economic distortions.There have been two main responses to their findings. The pessimistic response has been to conclude, given the lack of a reliable statistical relationship between conventional macroeconomic indicators and growth, that cross-country growth regressions cannot tell us anything about growth. The more optimistic response has been to argue that the extreme bounds analysis of Leamer (1983) is excessively stringent, requiring the coefficient estimate of interest to be statistically significant in all specifications. Thus, other researchers have proposed alternative, less stringent, approaches to robustness in the growth context (e.g., Xavier X. Sala-iMartin (1997) andGernot Doppelhofer, Ronald I. Miller, andSala-i-Martin (2004)).

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confidence: 99%

Nonlinearities and Robustness in Growth Regressions

Minier

2005

“…The negative relation between the natural resource abundance and economic growth is well documented in the literature (Sachs and Warner, 1995, 1997, 1999a,b, Sala-i-Martin, 1997, Doppelhofer et al, 2000. A number of theories were proposed to explain this negative link.…”

Section: Introductionmentioning

confidence: 90%

Human Capital, Industrial Growth and Resource Curse

Suslova

Volchkova

2012

How the country's natural resource abundance affects the industrial growth? We argue that one of the transmission mechanisms is via the accumulation of country's high skilled human capital. In particular, we empirically investigate whether link between country's natural resource endowment and industry-level growth depends on industry human capital requirement. We show that in the 1980s and the 1990s, industrial sectors that are high-skilled labor intensive developed disproportionately slowly in countries with higher contribution of natural resource sectors to GDP. While low-skilled labor intensity did not differentiate industrial growth between resource rich and resource poor countries. Our findings are in line with the theoretical argument that deteriorative effect of natural resources on the development of industrial sector is a byproduct of the capital accumulation process in the resource abundant open economy that slows down the development of marginally high-skilled labor force compared to the resource poor economies.

“…It has also been used in a number of econometric applications, including output growth forecasting (Min and Zellner (1993), Koop and Potter (2003)), cross-country growth regressions (Doppelhofer, Miller and Sala-i-Martin (2000) and Fernandez, Ley and Steel (2001)) and stock return prediction (Avramov (2002) and Cremers (2002) The models do not have to be linear regression models, but I shall henceforth assume that they are. The ith model then specifies that…”

Section: Bayesian Model Averagingmentioning

confidence: 99%

Forecasting U.S. Inflation by Bayesian Model Averaging

Wright¹

2003

Recent empirical work has considered the prediction of inflation by combining the information in a large number of time series. One such method that has been found to give consistently good results consists of simple equal weighted averaging of the forecasts over a large number of different models, each of which is a linear regression model that relates inflation to a single predictor and a lagged dependent variable. In this paper, I consider using Bayesian Model Averaging for pseudo out-of-sample prediction of US inflation, and find that it gives more accurate forecasts than simple equal weighted averaging. This superior performance is consistent across subsamples and inflation measures. Meanwhile, both methods substantially outperform a naive time series benchmark of predicting inflation by an autoregression.