Estimating Log Models: To Transform or Not to Transform? / Willard G. Manning, John Mullahy.

Format:

Book

Author/Creator:

Manning, Willard G.

Contributor:

National Bureau of Economic Research.

Mullahy, John.

Series:

Technical Working Paper Series (National Bureau of Economic Research) no. t0246.

NBER technical working paper series no. t0246

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

Estimating Log Models

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1999.

Summary:

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.

Notes:

Print version record

November 1999.