On the harm that ignoring pretesting can cause

By D. Danilov, J. R. Magnus. 2004

141 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5cba3e9e979f1d9c27153a557140c0b4e097edfc

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold (2012, methodology): “Some goodness-of-fit measures for Tobit models also have been developed by Amemiya (1981), Heckman (1984), Dhrymes (1986), and Laitila (1993).”
Regression with Imputed Covariates: A Generalized Missing Indicator Approach (2011, unknown): “In the terminology of Danilov and Magnus (2004), the K columns of W are the ''focus'' regressors, while the JK columns of Z are the ''auxiliary'' regressors.”
Growth slowdowns and the middle-income trap (2013, methodology): “The WALS is an alternative method building on Magnus and Durbin (1999) and Danilov and Magnus (2004) that addressed the issues and properties of pretest estimators.”
Optimal Weight Choice for Frequentist Model Average Estimators (2011, methodology): “The same model has been used to illustrate the consequences of ignoring pre-testing on forecasts by Danilov and Magnus (2004a).”
Model averaging by jackknife criterion in models with dependent data (2013, methodology): “…the Gaussian regressionmodel is themost widely used model in practice, and it also plays a central part in the majority of the model selection and averaging literature (e.g., Shibata, 1981; Hurvich and Tsai, 1989; Zhang, 1992; Danilov and Magnus, 2004; Leung and Barron, 2006; Bunea et al., 2007).”
The Forecast Combination Puzzle: A Simple Theoretical Explanation (2016, methodology): “It is well-known that ignoring the uncertainty involved with the selection procedure leads to incorrect inference (Kabaila, 1995; Pötscher, 1991; Hjort and Claeskens, 2003; Danilov and Magnus, 2004).”
A comparison of two model averaging techniques with an application to growth empirics (2010, methodology): “It is based on the equivalence theorem of Magnus and Durbin (1999) and Danilov and Magnus (2004), and was originally developed to better understand pretesting.”
Parametric or nonparametric? A parametricness index for model selection (2011, unknown): “(see, e.g., [21, 40, 44]).”
Model Selection (2018, methodology): “, Chapter 6 in Draper and Smith (1981) or Hocking (1976)) are a case in point.”
CAN ONE ESTIMATE THE UNCONDITIONAL DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS? (2007, unknown): “…not described by the traditional theory which assumes an a priori given model; in fact, they may differ substantially from the properties predicted by this theory, cf., e.g., Danilov and Magnus (2004), Dijkstra and Veldkamp (1988), Pötscher (1991, Section 3.3), or Rao and Wu (2001, Section 12).”
Model averaging, asymptotic risk, and regressor groups (2014, methodology): “For convenience, we amend the notation, and write the criterion (5) as”
Weighted‐Average Least Squares (WALS): A Survey (2016, methodology): “This follows from Lemma A1 in Danilov and Magnus (2004a).”