Abstract
AbstractEconomics researchers often assume that random variables are drawn from distributions that are members of scale or location‐scale families of distributions. This article generalizes earlier results in the literature on the bias in least squares estimates of multiplicative error models, and uses those results to construct a test of the scale and location‐scale hypotheses. A Monte Carlo simulation shows that the test is powerful in large samples. The empirical relevance of these findings is illustrated with estimates of a supply function for U.S. wheat production. Implications for applied economics research are discussed.
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