Model Averaging and its Use in Economics

Steel, Mark F. J. (2017): Model Averaging and its Use in Economics.

Abstract

The method of model averaging has become an important tool to deal with model uncertainty, in particular in empirical settings with large numbers of potential models and relatively limited numbers of observations, as are common in economics. Model averaging is a natural response to model uncertainty in a Bayesian framework, so most of the paper deals with Bayesian model averaging. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on the problem of variable selection in linear regression models, but the paper also discusses other, more challenging, settings. Some of the applied literature is reviewed with particular emphasis on applications in economics. The role of the prior assumptions in Bayesian procedures is highlighted, and some recommendations for applied users are provided

• Item Type: MPRA Paper
• Language: English
• Keywords: Bayesian methods; Model uncertainty; Normal linear model; Prior specification; Robustness
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C20 - General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence

References

About 300 references, starting with

Adler, K. and C. Grisse (2017). Thousands of BEERs: Take your pick. Review of International Economics, forthcoming.

Aijun, Y., X. Ju, Y. Hongqiang, and L. Jinguan (2017). Sparse Bayesian variable selection in probit model for forecasting U.S. recessions using a large set of predictors. Computational Economics 50, forthcoming.

Albert, J. H. and S. Chib (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88, 669–79.

Alvarez, J. and M. Arellano (2003). The time series and cross-section asymptotics of dynamic panel data estimators. Econometrica 71, 1121–59.

Amini, S. and C. Parmeter (2011). Bayesian model averaging in R. Journal of Economic and Social Measurement 36, 253–87.

Amini, S. M. and C. F. Parmeter (2012). Comparison of model averaging techniques: Assessing growth determinants. Journal of Applied Econometrics 27, 870–76.

Arin, K. and E. Braunfels (2017). The resource curse revisited: A Bayesian model averaging approach. Working paper, Zayed University.

Asatryan, Z. and L. Feld (2015). Revisiting the link between growth and federalism: A Bayesian model averaging approach. Journal of Comparative Economics 43, 772–81.

Atchad´e, Y. and J. Rosenthal (2005). On adaptive Markov chain Monte Carlo algorithms. Bernoulli 11, 815–28.

Barto´n, K. (2016). MuMIn - R package for model selection and multi-model inference. http: //mumin.r-forge.r-project.org/.

Bates, J. and C. Granger (1969). The combination of forecasts. Operations Research Quarterly 20, 451–68.

...............

until

Zeugner, S. and M. Feldkircher (2015). Bayesian model averaging employing fixed and flexible priors: The BMS package for R. Journal of Statistical Software 68.

Zhang, H., X. Huang, J. Gan, W. Karmaus, and T. Sabo-Attwood (2016). A two-component g-prior for variable selection. Bayesian Analysis 11, 353–80.

Zhang, X., D. Yu, G. Zou, and H. Liang (2016). Optimal model averaging estimation for generalized linear models and generalized linear mixed- effects models. Journal of the American Statistical Association 111, 1775–90.

Zigraiova, D. and T. Havranek (2016). Bank competition and financial stability: Much ado about nothing? Journal of Economic Surveys 30, 944–81.

Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101, 1418–29.