This paper examines some of the recent literature on the estimation of production functions. We focus on techniques suggested in two recent papers, Olley and Pakes (1996) and Levinsohn and Petrin (2003). While there are some solid and intuitive identification ideas in these papers, we argue that the techniques can suffer from functional dependence problems. We suggest an alternative approach that is based on the ideas in these papers, but does not suffer from the functional dependence problems and produces consistent estimates under alternative data generating processes for which the original procedures do not.

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This article empirically analyzes different effects of advertising in a nondurable, experience good market. A dynamic learning model of consumer behavior is presented in which I allow both "informative" effects of advertising and "prestige" or "image" effects of advertising. This learning model is estimated using consumer level panel data tracking grocery purchases and advertising exposures over time. Empirical results suggest that in this market, advertising's primary effect was that of informing consumers. The estimates are used to quantify the value of this information to consumers and evaluate the welfare implications of an alternative advertising regulatory regime.

This article introduces techniques to empirically distinguish different effects of brand advertising in nondurable, experience-goods markets. I argue that advertisements that give consumers product information should primarily affect consumers who have never tried the brand, whereas advertisements that create prestige or image effects should affect both inexperienced and experienced users. I apply this empirical argument to consumer-level data on purchases of a newly introduced brand of yogurt. Empirical results indicate that the advertisements for this brand primarily affected inexperienced users of the brand. I conclude that the primary effect of these advertisements was that of informing consumers.

Standard discrete choice models such as logit, nested logit, and random coef¿cients models place very strong restrictions on how unobservable product space increases with the number of products. We argue (and show with Monte Carlo experiments) that these restrictions can lead to biased conclusions regarding price elasticities and welfare consequences from additional products. In addition, these restrictions can identify parameters which are not intuitively identi¿ed given the data at hand. We suggest two alternative models that relax these restrictions, both motivated by structural interpretations. Monte-Carlo experiments and an application to data show that these alternative models perform well in practice.

The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric variances using standard formulas derived in the well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations as if it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.

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