Comparar métodos

	Model Confidence Set	Regressão Passo a Passo
Área	Econometria	Estatística
Família	Hypothesis test	Regression model
Ano de origem	2011	1960
Autor original	Hansen, Lunde & Nason	M. A. Efroymson
Tipo	Sequential hypothesis testing procedure for model comparison	Automated variable selection
Fonte seminal	Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.	Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical Methods for Digital Computers (pp. 191–203). Wiley.
Outros nomes	MCS Procedure, Superior Set of Models, Model Selection Confidence Set, Model Güven Kümesi	stepwise selection, forward stepwise regression, backward stepwise regression, forward-backward selection
Relacionados	3	5
Resumo	The Model Confidence Set (MCS) is a sequential hypothesis-testing procedure introduced by Hansen, Lunde, and Nason (2011) that identifies the smallest collection of forecasting or predictive models statistically indistinguishable from the best-performing model at a given confidence level. Instead of selecting a single winner, MCS returns a set of superior models, making it especially valuable in econometric forecast comparisons where the true best model is unknown.	Stepwise regression is an automated variable selection procedure for multiple linear regression that adds or removes predictor variables one at a time according to a statistical criterion, typically the F-statistic or a p-value threshold. The forward-selection algorithm was formally described by Efroymson (1960) and the bidirectional variant was popularised by Draper and Smith in their landmark 1966 text Applied Regression Analysis. Despite widespread historical use, the method is now widely critiqued, making its documentation essential in any canonical methods library.