Yu Yvette Zhang

Two-Stage Buy-It-Now Auctions

This paper studies second-price auctions with a temporary Buy-It-Now price (BIN auctions) using a two-stage model, in which two groups of bidders enter the auction at different times. The early bidders are offered a Buy-It-Now (BIN) option to purchase the item immediately at a listed price (BIN price). If no early bidder accepts the BIN option, an additional group of bidders (late bidders) enter the auction and both groups of bidders participate in a second-price sealed-bid auction without BIN option. When bidders are risk averse with concave utility functions, we establish the existence and uniqueness of a cutoff equilibrium such that an early bidder will accept the BIN option if his valuation is higher than the cutoff valuation. Moreover, bidders are more likely to accept the BIN option when fewer bidders are offered the BIN option. We show that when facing risk averse bidders, the seller can obtain higher expected revenue in BIN auctions than in standard second-price auctions. Furthermore, the expected seller revenue decreases with the number of early bidders. Consequently, the expected seller revenue is higher in the auctions with BIN only available to a subset of bidders than in the auctions with BIN available to all bidders. These results may help explain the popularity of temporary BIN auctions on eBay and the observed high acceptance frequencies of BIN prices in experimental and field studies.

A Density-Ratio Model of Crop Yield Distributions

This paper proposes a density ratio estimator of crop yield distributions, wherein the number of observations for individual distributions is often quite small. The density ratio approach models individual densities as distortions from a common baseline density. We introduce a probability integral transformation to the density ratio method that simplifies the modeling of distortion functions. We further present an implementation approach based on the Poisson regression, which facilitates model estimation and diagnostics. Monte Carlo simulations demonstrate good finite sample performance of the proposed method. We apply this method to estimate the corn yield distributions of 99 Iowa counties and calculate crop insurance premiums. Lastly we illustrate that we can employ the proposed method to effectively identify profitable insurance policies.

Nonparametric estimation of regression models with mixed discrete and continuous covariates by the K-nn method

ABSTRACT In this article we consider the problem of estimating a nonparametric conditional mean function with mixed discrete and continuous covariates by the nonparametric k-nearest-neighbor (k-nn) method. We derive the asymptotic normality result of the proposed estimator and use Monte Carlo simulations to demonstrate its finite sample performance. We also provide an illustrative empirical example of our method.

A Simple Consistent Nonparametric Estimator of the Lorenz Curve

We propose a nonparametric estimator of the Lorenz curve that satisfies its theoretical properties, including monotonicity and convexity. We adopt a transformation approach that transforms a constrained estimation problem into an unconstrained one, which is estimated nonparametrically. We utilize the splines to facilitate the numerical implementation of our estimator and to provide a parametric representation of the constructed Lorenz curve. We conduct Monte Carlo simulations to demonstrate the superior performance of the proposed estimator. We apply our method to estimate the Lorenz curve of the U.S. household income distribution and calculate the Gini index based on the estimated Lorenz curve.

Recent Developments in Semiparametric and Nonparametric Estimation of Panel Data Models with Incomplete Information: A Selected Review

This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.