Model uncertainty in cross‐country growth regressions

Fernández, Carmen; Ley, Eduardo; Steel, Mark F. J.

doi:10.1002/jae.623

Cited by 708 publications

(691 citation statements)

References 25 publications

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“…It is frequently used to test the robustness of empirical results in the recent literature (see, e.g., Fernández, Ley and Steel (2001), Sturm and de Haan (2004), and Sturm, Berger, de Haan (2005). We explain the method in some detail below.…”

Section: Introductionmentioning

confidence: 99%

Cross-country determinants of life satisfaction: exploring different determinants across groups in society

2007

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. We thank Stephen Lea and participants at the 30 th IAREP conference in Prague for comments on an earlier version and Gilles Winkler for excellent research assistance. AbstractThis paper explores a wide range of cross-country determinants of life satisfaction exploiting a database of 90,000 observations in 70 countries. We distinguish four groups of aggregate variables as potential determinants of satisfaction: political, economic, institutional, and human development and culture. We use ordered probit to investigate the importance of these variables on individual life satisfaction and test the robustness of our results with Extreme Bounds Analysis. The results show that only a small number of factors, such as openness, business climate, postcommunism, the number of chambers in parliament, Christian majority, and infant mortality robustly influence life satisfaction across countries while the importance of many variables suggested in the previous literature is not confirmed. This remains largely true when the analysis splits national populations according to gender, income and political orientation also. JEL Codes: I31, H10, H40

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Section: Introductionmentioning

confidence: 99%

Cross-country determinants of life satisfaction: exploring different determinants across groups in society

2007

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“…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

SSRN Journal

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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.

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“…The use of techniques that explicitly assess model uncertainty (mostly within the class of linear regression models) has thus become widespread in econometric research dealing with the empirical determinants of income growth differences across countries (for some seminal contributions to this literature, see e.g. Fernandez et al, 2001;Sala-i Martin et al, 2004;Masanjala and Papageorgiou, 2008;Durlauf et al, 2008;Ley and Steel, 2009b).…”

Section: Introductionmentioning

confidence: 99%

“…We apply this approach to the two datasets that have been most widely used for assessing the robustness of economic growth determinants (those in Fernandez et al, 2001, andSala-i Martin et al, 2004, henceforth FLS andSDM, respectively). Our results for the FLS dataset reveal patterns of complementarity and substitutability across geographical, institutional and religious variables.…”

Section: Introductionmentioning

confidence: 99%

Unveiling covariate inclusion structures in economic growth regressions using latent class analysis

Cuaresma

Grün

Hofmarcher

et al. 2016

European Economic Review

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We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian Model Averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.JEL Classification: C11, C21, O47.

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Model uncertainty in cross‐country growth regressions

Cited by 708 publications

References 25 publications

Cross-country determinants of life satisfaction: exploring different determinants across groups in society

Cross-country determinants of life satisfaction: exploring different determinants across groups in society

Forecasting U.S. Inflation by Bayesian Model Averaging

Unveiling covariate inclusion structures in economic growth regressions using latent class analysis

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