Kyoo il Kim

Uniform convergence rate of the seminonparametric density estimator and testing for similarity of two unknown densities

This paper studies the uniform convergence rate of the truncated seminonparametric (SNP) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded. Copyright Royal Economic Society 2007

A simple estimator for the distribution of random coefficients

We propose a simple mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program, and computationally attractive compared to alternative estimators for random coefficient models. For complex structural models, one does not need to nest a solution to the economic model during optimization. We present a Monte Carlo study and an empirical application to dynamic programming discrete choice with a serially correlated unobserved state variable.

Tests for Price Endogeneity in Differentiated Product Models

Abstract We develop simple tests for endogenous prices arising from omitted demand factors in discrete choice models. Our approach only requires one to locate testing proxies that have some correlation with the omitted factors when prices are endogenous. We use the difference between prices and their predicted values given observed demand and supply factors. If prices are exogenous, these proxies should not explain demand given prices and other explanatory variables. We reject exogeneity if these proxies enter significantly in utility as additional explanatory variables. The tests are easy to implement as we show with several Monte Carlos and discuss for three recent demand applications.

Identifying combinatorial valuations from aggregate demand

We study identification of combinatorial valuations from aggregate demand. Each utility function takes as arguments subsets or, alternatively, quantities of the multiple goods. We exploit mathematical insights from auction theory to generically identify the distribution of utility functions. In our setting, aggregate demand for each item is observable while demand for bundles is not. Nevertheless, our identification result allows us to recover the latter.

Identification of Demand Models of Multiple Purchases

We study the nonparametric identification of distributions of utility functions in a multiple purchase setting with a finite number of consumers. Each utility function takes as arguments subsets or, alternatively, quantities of the multiple goods. We exploit mathematical insights from auction theory to generically identify the distribution of utility functions. We use price variation and aggregate data on the sales of each product, but not on individual level purchases or the total sales of bundles of products.

The role of online buzz for leader versus challenger brands: the case of the MP3 player market

Online buzz reflects the perceived quality of products in a positive, negative, or neutral way. We have limited understanding of how customers’ quality perceptions, often referred to as e-sentiment, affect the movement of prices. In this paper, we examine the effect of e-sentiment on the daily price fluctuations of MP3 players by using daily buzz information collected from diverse online documents. Econometric panel data modeling reveals that e-sentiment is a leading indicator of price fluctuations. Furthermore, we find the effect is moderated by the brand’s market position: the leading (challenger) brand’s price responds more strongly to negative (positive) online buzz. In other words, negative buzz has a greater adverse effect on leading brands, whereas positive buzz has a greater beneficial effect on challenger brands. These findings establish the relevance of e-sentiment information to online price movements and suggest that managers should frequently monitor the online buzz surrounding their products, especially as it relates to their relative perceived quality.