Estimating Log Models: To Transform or Not to Transform?

Willard Manning and John Mullahy ()

No 246, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: Data on health care expenditures, length of stay, utilization of health services, consumption of unhealthy commodities, etc. are typically characterized by: (a) nonnegative outcomes; (b) nontrivial fractions of zero outcomes in the population (and sample); and (c) positively-skewed distributions of the nonzero realizations. Similar data structures are encountered in labor economics as well. This paper provides simulation-based evidence on the finite-sample behavior of two sets of estimators designed to look at the effect of a set of covariates x on the expected outcome, E(y|x), under a range of data problems encountered in every day practice: generalized linear models (GLM), a subset of which can simply be viewed as differentially weighted nonlinear least-squares estimators, and those derived from least-squares estimators for the ln(y). We consider the first- and second- order behavior of these candidate estimators under alternative assumptions on the data generating processes. Our results indicate that the choice of estimator for models of ln(E(x|y)) can have major implications for empirical results if the estimator is not designed to deal with the specific data generating mechanism. Garden-variety statistical problems - skewness, kurtosis, and heteroscedasticity - can lead to an appreciable bias for some estimators or appreciable losses in precision for others.

JEL-codes: C2 I1 (search for similar items in EconPapers)
Date: 1999-11
New Economics Papers: this item is included in nep-ecm
Note: TWP
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Citations: View citations in EconPapers (17)

Published as Manning, Willard G. and John Mullahy. "Estimating Log Models: To Transform Or Not To Transform?," Journal of Health Economics, 2001, v20(4,Jul), 461-494.

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Journal Article: Estimating log models: to transform or not to transform? (2001)
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