Predictive validation and forecasts of short-term changes in healthcare expenditure associated with changes in smoking behavior in the United States
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DOI: 10.1371/journal.pone.0227493
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- Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2003.
"Instrumental variables and GMM: Estimation and testing,"
Stata Journal, StataCorp LP, vol. 3(1), pages 1-31, March.
- Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2002. "Instrumental variables and GMM: Estimation and testing," United Kingdom Stata Users' Group Meetings 2003 02, Stata Users Group.
- Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2002. "Instrumental variables and GMM: Estimation and testing," Boston College Working Papers in Economics 545, Boston College Department of Economics, revised 14 Feb 2003.
- Christopher F Baum & Mark E. Schaffer & Steven Stillman, 2002. "Instrumental variables and GMM: Estimation and testing," North American Stata Users' Group Meetings 2003 05, Stata Users Group.
- Peter Reinhard Hansen & Allan Timmermann, 2012.
"Choice of Sample Split in Out-of-Sample Forecast Evaluation,"
CREATES Research Papers
2012-43, Department of Economics and Business Economics, Aarhus University.
- Peter Reinhard HANSEN & Allan TIMMERMANN, 2012. "Choice of Sample Split in Out-of-Sample Forecast Evaluation," Economics Working Papers ECO2012/10, European University Institute.
- Graham Elliott & Allan Timmermann, 2016.
"Economic Forecasting,"
Economics Books,
Princeton University Press,
edition 1, number 10740.
- Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
- Timmermann, Allan & Elliott, Graham, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
- Todd E. Clark, 2004.
"Can out-of-sample forecast comparisons help prevent overfitting?,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
- Todd E. Clark, 2000. "Can out-of-sample forecast comparisons help prevent overfitting?," Research Working Paper RWP 00-05, Federal Reserve Bank of Kansas City.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2011.
"The Model Confidence Set,"
Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.
- Graham Elliott & Allan Timmermann, 2016.
"Forecasting in Economics and Finance,"
Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
- Timmermann, Allan & Elliott, Graham, 2016. "Forecasting in Economics and Finance," CEPR Discussion Papers 11354, C.E.P.R. Discussion Papers.
- Elliott, Graham & Timmermann, Allan G, 2016. "Forecasting in Economics and Finance," University of California at San Diego, Economics Working Paper Series qt6z55v472, Department of Economics, UC San Diego.
- Raffaella Giacomini & Halbert White, 2006.
"Tests of Conditional Predictive Ability,"
Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
- Raffaella Giacomini & Halbert White, 2003. "Tests of conditional predictive ability," Boston College Working Papers in Economics 572, Boston College Department of Economics.
- Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
- Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, University Library of Munich, Germany.
- Mauro Bernardi & Leopoldo Catania, 2018. "The model confidence set package for R," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 8(2), pages 144-158.
- Montalvo, Jose G., 1995. "Comparing cointegrating regression estimators: Some additional Monte Carlo results," Economics Letters, Elsevier, vol. 48(3-4), pages 229-234, June.
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