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Modelling non-stationary "big data"
Castle, Jennifer; Doornik, Jurgen A.; Hendry, David F. - 2020
Persistent link: https://www.econbiz.de/10012202702
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Big data analytics : a new perspective
Chudik, Alexander; Kapetanios, George; Pesaran, M. Hashem - 2016
experiments considered in this paper. Despite its simplicity, the theory behind the proposed approach is quite complicated. We …
Persistent link: https://www.econbiz.de/10011444508
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Detecting location shifts during model selection by step-indicator saturation
Castle, Jennifer; Doornik, Jurgen A.; Hendry, David F.; … - In: Econometrics : open access journal 3 (2015) 2, pp. 240-264
To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a...
Persistent link: https://www.econbiz.de/10011297656
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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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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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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 finance. 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/10013231185
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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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A brief history of general-to-specific modelling
Hendry, David F. - In: Oxford bulletin of economics and statistics 86 (2024) 1, pp. 1-20
Persistent link: https://www.econbiz.de/10014481330
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Inference trees, competitive models, and sparse big data heuristics
Nourani, Cyrus F.; Fähndrich, Johannes - In: Computing predictive analytics, business intelligence, …, (pp. 247-258). 2020
Persistent link: https://www.econbiz.de/10012055569