To Explain or To Predict?
Preprint
- 1 January 2010
- preprint
- Published by Elsevier BV in SSRN Electronic Journal
Abstract
Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines thereThis publication has 66 references indexed in Scilit:
- Does depression predict coronary heart disease and cerebrovascular disease equally well? The Health and Social Support Prospective Cohort StudyInternational Journal of Epidemiology, 2010
- Real-Time Forecasting of Online Auctions via Functional K-Nearest NeighborsSSRN Electronic Journal, 2009
- Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblagesJournal of Applied Ecology, 2009
- Bayes not Bust! Why Simplicity is no Problem for BayesiansThe British Journal for the Philosophy of Science, 2007
- A Comprehensive Look at The Empirical Performance of Equity Premium PredictionThe Review of Financial Studies, 2007
- Predictive Accuracy as an Achievable Goal of SciencePhilosophy of Science, 2002
- Common risk factors in the returns on stocks and bondsJournal of Financial Economics, 1993
- A Predictive View of the Detection and Characterization of Influential Observations in Regression AnalysisJournal of the American Statistical Association, 1983
- Investigating Causal Relations by Econometric Models and Cross-spectral MethodsEconometrica, 1969
- An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation BiasJournal of the American Statistical Association, 1962