@Article{amini2012replication,
	author = {Amini, Shahram M. and Parmeter, Christopher F.},
	title = {Comparison of model averaging techniques: assessing growth determinants},
	journal = {Journal of Applied Econometrics},
	volume = {27},
	number = {5},
	pages = {870-876},
	doi = {10.1002/jae.2288},
	year = {2012}
}

@Manual{countreg,
  title  = {{countreg}: Count Data Regression},
  author = {Achim Zeileis and Christian Kleiber},
  note   = {R package version 0.2-1},
  year   = {2023},
  url    = {https://r-forge.r-project.org/projects/countreg/},
}

@Article{countreghurdle,
  title = {Regression Models for Count Data in {R}},
  author = {Achim Zeileis and Christian Kleiber and Simon Jackman},
  journal = {Journal of Statistical Software},
  year = {2008},
  volume = {27},
  number = {8},
  pages = {1--25},
  doi = {10.18637/jss.v027.i08},
}

@Article{deluca2011stata,
  author    = {De Luca, Giuseppe and Magnus, Jan R.},
  journal   = {The Stata Journal},
  title     = {{B}ayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues},
  year      = {2011},
  number    = {4},
  pages     = {518--544},
  volume    = {11},
  doi       = {10.1177/1536867X1201100402},
  publisher = {SAGE Publications Sage CA: Los Angeles, CA},
}

@Article{deluca2018glm,
  author    = {De Luca, Giuseppe and Magnus, Jan R and Peracchi, Franco},
  journal   = {Journal of Econometrics},
  title     = {Weighted-average least squares estimation of generalized linear models},
  year      = {2018},
  number    = {1},
  pages     = {1--17},
  volume    = {204},
  doi       = {10.1016/j.jeconom.2017.12.007},
  publisher = {Elsevier},
}

@Article{deluca2020laplace,
  author    = {De Luca, Giuseppe and Magnus, Jan R and Peracchi, Franco},
  journal   = {Communications in Statistics - Theory and Methods},
  title     = {Posterior moments and quantiles for the normal location model with Laplace prior},
  year      = {2020},
  number    = {0},
  pages     = {1-11},
  volume    = {0},
  doi       = {10.1080/03610926.2019.1710756},
  publisher = {Taylor & Francis},
}

@TechReport{huynhwalsnb,
  title = {Weighted-Average Least Squares for Negative Binomial Regression},
  author = {Kevin Huynh},
  institution = {arXiv.org E-Print Archive},
  year = {2024a},
  type = {arXiv},
  number = {2404.11324},
  month = {April},
  doi = {10.48550/arXiv.2404.11324},
}

@TechReport{huynhwals,
  title = {{WALS}: Weighted-Average Least Squares Model Averaging in {R}},
  author = {Kevin Huynh},
  institution = {University of Basel},
  year = {2024b},
  note = {Mimeo},
}

@Article{kumar2013normallocation,
  author    = {Kumar, Kamlesh and Magnus, Jan R},
  journal   = {Sankhya B},
  title     = {A characterization of {B}ayesian robustness for a normal location parameter},
  year      = {2013},
  number    = {2},
  pages     = {216--237},
  volume    = {75},
  doi       = {10.1007/s13571-013-0060-9},
  publisher = {Springer},
}

@Article{magnus2010growth,
  author   = {Jan R. Magnus and Owen Powell and Patricia Prüfer},
  journal  = {Journal of Econometrics},
  title    = {A comparison of two model averaging techniques with an application to growth empirics},
  year     = {2010},
  number   = {2},
  pages    = {139-153},
  volume   = {154},
  abstract = {Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present.},
  doi      = {10.1016/j.jeconom.2009.07.004},
  keywords = {Model averaging, Bayesian analysis, Growth determinants},
}

@Article{magnus2016wals,
  author   = {Magnus, Jan R. and De Luca, Giuseppe},
  journal  = {Journal of Economic Surveys},
  title    = {Weighted-average least squares ({WALS}): A survey},
  year     = {2016},
  number   = {1},
  pages    = {117-148},
  volume   = {30},
  abstract = {Abstract Model averaging has become a popular method of estimation, following increasing evidence that model selection and estimation should be treated as one joint procedure. Weighted-average least squares (WALS) is a recent model-average approach, which takes an intermediate position between frequentist and Bayesian methods, allows a credible treatment of ignorance, and is extremely fast to compute. We review the theory of WALS and discuss extensions and applications.},
  doi      = {10.1111/joes.12094},
  keywords = {Computing time, Frequentist versus Bayesian, Least squares, Model averaging, Priors},
}

@Article{masanjala2008growth,
  author = {Masanjala, Winford H. and Papageorgiou, Chris},
  title = {Rough and lonely road to prosperity: a reexamination of the sources of growth in {A}frica using {B}ayesian model averaging},
  journal = {Journal of Applied Econometrics},
  volume = {23},
  number = {5},
  pages = {671-682},
  doi = {10.1002/jae.1020},
  abstract = {Abstract This paper takes a fresh look at Africa's growth experience by using the Bayesian model averaging (BMA) methodology. BMA enables us to consider a large number of potential explanatory variables and sort out which of these variable can effectively explain Africa's growth experience. Posterior coefficient estimates reveal that key engines of growth in Africa are substantially different from those in the rest of the world. More precisely, it is shown that mining, primary exports and initial primary education exerted differential effect on African growth. These results are examined in relation to the existing literature. Copyright © 2008 John Wiley \& Sons, Ltd.},
	year = {2008}
}

@Book{mass2002,
  author    = {W. N. Venables and B. D. Ripley},
  publisher = {Springer-Verlag},
  title     = {Modern Applied Statistics with {S}},
  year      = {2002},
  address   = {New York},
  edition   = {4th},
  series    = {Statistics and Computing},
  doi       = {10.1007/978-0-387-21706-2},
  url       = {https://www.stats.ox.ac.uk/pub/MASS4/},
}

@Article{mboost,
  title = {Model-based Boosting in {R}: A Hands-on Tutorial Using the {R} Package mboost},
  author = {Benjamin Hofner and Andreas Mayr and Nikolay Robinzonov and Matthias Schmid},
  journal = {Computational Statistics},
  year = {2014},
  volume = {29},
  pages = {3--35},
}

@Manual{R2023,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2023},
    url = {https://www.R-project.org/},
}

@Article{sala1997reg,
  author = {Sala-I-Martin, Xavier},
  journal = {The American Economic Review},
  number = {2},
  pages = {178--183},
  publisher = {American Economic Association},
  title = {I Just Ran Two Million Regressions},
  volume = {87},
  year = {1997}
}

@Article{sala2004bace,
  author = {Sala-I-Martin, Xavier and Doppelhofer, Gernot and Miller, Ronald I.},
  title = {Determinants of Long-Term Growth: A {B}ayesian Averaging of Classical Estimates (BACE) Approach},
  journal = {American Economic Review},
  volume = {94},
  number = {4},
  year = {2004},
  month = {September},
  pages = {813-835},
  doi = {10.1257/0002828042002570},
}
