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Statistical model selection with 'big data'
Doornik, Jurgen A.; Hendry, David F. - 2014
Persistent link: https://www.econbiz.de/10010442444
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Selection criteria in regime switching conditional volatility models
Chuffart, Thomas - In: Econometrics : open access journal 3 (2015) 2, pp. 289-316
A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a...
Persistent link: https://www.econbiz.de/10011297653
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Improving the teaching of econometrics
Hendry, David F.; Mizon, Grayham E. - In: Cogent economics & finance 4 (2016) 1, pp. 1-26
equation specifications, formulated to include all theory-based variables, their lags and possibly non-linear functional forms …, can be improved by nesting so-called "theory-driven" and "data-driven" approaches. In our methodology, the theory …-model's parameter estimates are unaffected by selection when the theory is complete and correct, so nothing is lost, whereas when the …
Persistent link: https://www.econbiz.de/10011487537
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Spatial econometric modelling of massive datasets : the contribution of data mining
Arbia, Giuseppe; Tabasso, M. - 2013
In this paper we provide a brief overview of some of the most recent empirical research on spatial econometric models and spatial data mining. Data mining in general is the search for hidden patterns that may exist in large databases. Spatial data mining is a process to discover interesting,...
Persistent link: https://www.econbiz.de/10011532588
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Economic predictions with big data : the illusion of sparsity
Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E. - 2021
We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and ftnance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate...
Persistent link: https://www.econbiz.de/10012506019
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Bayesian Model Selection for Structural Break Models
Levin, Andrew T. - 2008
We take a Bayesian approach to model selection in regression models with structural breaks in conditional mean and residual variance parameters. A novel feature of our approach is that it does not assume knowledge of the parameter subset that undergoes structural breaks, but instead conducts...
Persistent link: https://www.econbiz.de/10012724369
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Asymmetry and leverage in conditional volatility models
McAleer, Michael - 2014
The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or...
Persistent link: https://www.econbiz.de/10010405194
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Testing parameter constancy in unit root autoregressive models against continuous change
He, Changli (contributor); Sandberg, Rickard (contributor) - 2005 - [Elektronische Ressource], Rev. February 8, 2005
In this paper we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new...
Persistent link: https://www.econbiz.de/10002570513
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Nonparametric regression on latent covariates with an application to semiparametric GARCH-in-Mean models
Conrad, Christian; Mammen, Enno - 2008
kernel smoothing of the conditional mean function. An asymptotic theory for the resulting kernel estimator is developed and …
Persistent link: https://www.econbiz.de/10003747376
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On the usefulness of the Diebold-Mariano test in the selection of prediction models : some Monte Carlo evidence
Costantini, Mauro; Kunst, Robert M. - 2011
In evaluating prediction models, many researchers flank comparative ex-ante prediction experiments by significance tests on accuracy improvement, such as the Diebold-Mariano test. We argue that basing the choice of prediction models on such significance tests is problematic, as this practice may...
Persistent link: https://www.econbiz.de/10009685472