Big Data Analytics: A New Perspective

Arbeitspapier

Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised regression has become the de facto benchmark technique used to trade off parsimony and fit when the number of possible covariates is large, often much larger than the number of available observations. However, issues such as the choice of a penalty function and tuning parameters associated with the use of penalized regressions remain contentious. In this paper, we provide an alternative approach that considers the statistical significance of the individual covariates one at a time, whilst taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time Multiple Testing (OCMT) procedure The OCMT has a number of advantages over the penalised regression methods: It is based on statistical inference and is therefore easier to interpret and relate to the classical statistical analysis, it allows working under more general assumptions, it is computationally simple and considerably faster, and it performs better in small samples for almost all of the five different sets of experiments considered in this paper. Despite its simplicity, the theory behind the proposed approach is quite complicated. We provide extensive theoretical and Monte Carlo results in support of adding the proposed OCMT model selection procedure to the toolbox of applied researchers.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 5824

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Model Evaluation, Validation, and Selection ; Large Data Sets: Modeling and Analysis
• Thema: one covariate at a time ; multiple testing ; model selection ; high dimensionality ; penalized regressions ; boosting ; Monte Carlo experiments

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Chudik, Alexander ; Kapetanios, George ; Pesaran, M. Hashem
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2016

Weitere Informationen

• Handle: http://hdl.handle.net/10419/130449
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ