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Small sample adjustments for the J-test
Godfrey, L. G.; Godfrey, L.G.; Pesaran, M.H. - 1983
Persistent link: https://www.econbiz.de/10000012596
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Randomization inference in a group-randomized trial of treatments for depression : covariate adjustment, noncompliance and quantile effects
Small, Dylan S.; Have, Thomas R. ten; Rosenbaum, Paul R. - In: Journal of the American Statistical Association : JASA 103 (2008) 481, pp. 271-279
Persistent link: https://www.econbiz.de/10003676746
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Inference with imperfect randomization : the case of the Perry Preschool program
Heckman, James J.; Pinto, Rodrigo; Shaikh, Azeem M.; … - 2011
Persistent link: https://www.econbiz.de/10009008640
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Inference with imperfect randomization : the case of the Perry Preschool program
Heckman, James J.; Pinto, Rodrigo; Shaikh, Azeem M.; … - 2011
This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of...
Persistent link: https://www.econbiz.de/10009235526
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Out-of-sample tests or granger causality
Chao, John; Corradi, Valentina; Swanson, Norman R. - In: Macroeconomic dynamics 5 (2001) 4, pp. 598-620
Persistent link: https://www.econbiz.de/10001625176
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Small sample adjustments for the J-test
Godfrey, L. G.; Pesaran, Hashem - 1982
Persistent link: https://www.econbiz.de/10001532016
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Joint censoring of regressors and outcomes : survey nonresponse and attrition
Horowitz, Joel; Manski, Charles F. - 1994
Persistent link: https://www.econbiz.de/10000896408
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Statistics, data analysis, and decision modeling
Evans, James R. - 2010 - 4. ed., internat. ed.
Persistent link: https://www.econbiz.de/10003857594
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Statistics, data analysis, and decision modeling
Evans, James R.; Basu, Ayanendranath - 2013 - 5th ed., internat. ed.
Persistent link: https://www.econbiz.de/10009525190
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U-MIDAS: MIDAS regressions with unrestricted lag polynomials
Foroni, Claudia; Marcellino, Massimiliano; Schumacher, … - 2011
Mixed-data sampling (MIDAS) regressions allow to estimate dynamic equations that explain a low-frequency variable by … high-frequency variables and their lags. When the difference in sampling frequencies between the regressand and the … macroeconomic applications, however, differences in sampling frequencies are often small. In such a case, it might not be necessary …
Persistent link: https://www.econbiz.de/10009490826