Penalized Mallow's model averaging
In: Communications in statistics. Theory and methods, Band 53, Heft 20, S. 7417-7435
ISSN: 1532-415X
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In: Communications in statistics. Theory and methods, Band 53, Heft 20, S. 7417-7435
ISSN: 1532-415X
In: Journal of economic and social measurement, Band 36, Heft 4, S. 253-287
ISSN: 1875-8932
In: The Econometrics Journal, Band 16, Heft 3, S. 463-472
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Working paper
In: American economic review, Band 107, Heft 11, S. 3589-3616
ISSN: 1944-7981
A decision maker doubts the stationarity of his environment. In response, he uses two models, one with time-varying parameters, and another with constant parameters. Forecasts are then based on a Bayesian model averaging strategy, which mixes forecasts from the two models. In reality, structural parameters are constant, but the (unknown) true model features expectational feedback, which the reduced-form models neglect. This feedback permits fears of parameter instability to become self-confirming. Within the context of a standard asset-pricing model, we use the tools of large deviations theory to show that even though the constant parameter model would converge to the rational expectations equilibrium if considered in isolation, the mere presence of an unstable alternative drives it out of consideration. (JEL C63, D83, D84)
In: American journal of political science: AJPS, Band 41, Heft 2, S. 641-674
ISSN: 0092-5853
In: American journal of political science, Band 41, Heft 2, S. 641
ISSN: 1540-5907
In: Communications in statistics. Theory and methods, Band 42, Heft 23, S. 4342-4356
ISSN: 1532-415X
In: International journal of forecasting, Band 36, Heft 1, S. 86-92
ISSN: 0169-2070
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Band 20, Heft 3, S. 271-291
ISSN: 1476-4989
We present ensemble Bayesian model averaging (EBMA) and illustrate its ability to aid scholars in the social sciences to make more accurate forecasts of future events. In essence, EBMA improves prediction by pooling information from multiple forecast models to generate ensemble predictions similar to a weighted average of component forecasts. The weight assigned to each forecast is calibrated via its performance in some validation period. The aim is not to choose some "best" model, but rather to incorporate the insights and knowledge implicit in various forecasting efforts via statistical postprocessing. After presenting the method, we show that EBMA increases the accuracy of out-of-sample forecasts relative to component models in three applied examples: predicting the occurrence of insurgencies around the Pacific Rim, forecasting vote shares in U.S. presidential elections, and predicting the votes of U.S. Supreme Court Justices.
This paper offers two innovations for empirical growth research. First, the paper discusses principal components augmented regressions to take into account all available information in well-behaved regressions. Second, the paper proposes a frequentist model averaging framework as an alternative to Bayesian model averaging approaches. The proposed methodology is applied to three data sets, including the Sala-i-Martin et al. (2004) and Fernandez et al. (2001) data as well as a data set of the European Union member states' regions. Key economic variables are found to be significantly related to economic growth. The findings highlight the relevance of the proposed methodology for empirical economic growth research.
BASE
In: International review of law and economics, Band 68, S. 106019
ISSN: 0144-8188
In: Communications in statistics. Theory and methods, Band 53, Heft 19, S. 6799-6831
ISSN: 1532-415X
In: Social indicators research: an international and interdisciplinary journal for quality-of-life measurement, Band 151, Heft 3, S. 897-919
ISSN: 1573-0921