Nonparametric Identication and Structural Estimation of Auction Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Identication and Structural Estimation of Auction Models write by Ming He. This book was released on 2016. Nonparametric Identication and Structural Estimation of Auction Models available in PDF, EPUB and Kindle. This dissertation contributes to the structural auction literature in two different auction models, namely the pure common value model and the affiliated private value model. The goal of structural analysis of auction data is to recover the model primitives and to provide policy guidance for welfare analysis. In Chapter 1, we study identification in the first-price and the second-price sealed-bid auctions within the pure common value framework. In Chapter 2, we apply the identification results and estimation method in Chapter 1 to analyze the U.S. Outer Continental Shelf (OCS) wildcat auction data and provide policy guidance for welfare analysis. In Chapter 3, we develop identification and partial identification results for the first-price and the second-price sealed-bid auction models with affiliated private values and incomplete sets of bids. Chapter 1: In this chapter, we establish novel identification results for both the first-price and the second-price sealed-bid auction models within the pure common value framework. We show that the policy parameters, including the expected total welfare, the seller's expected revenue, and the bidders' expected surplus under any reserve price are identified for a general nonparametric class of latent joint distributions when the ex-post common value is unobserved. Moreover, we establish that these policy parameters are nonparametric identified without normalization assumption when the ex-post common value is observed. We propose a semiparametric estimation method and establish consistency of the estimator. Results from Monte Carlo experiments reveal good finite sample performance of the estimator. Chapter 2: In this chapter, we employ the identification strategy and estimation method in Chapter 1 to analyze data from the U.S. Outer Continental Shelf (OCS) wildcat auctions in the pure common value framework. We study the welfare implication of different counterfactual reserve prices, focusing on the cases with two and three bidders. The empirical results suggest that if the U.S. government had set reserve prices optimally using the newly-developed econometric method in Chapter 1, its expected revenue can be increased by around $34\%$ and $30\%$ for these two cases, respectively. Lastly, we compare our results with those estimated under the affiliated private value framework, and find that the estimated welfare curves under the two different frameworks are very different. Chapter 3: In this chapter, we address the identification issue in the first-price sealed-bid affiliated private value model when an incomplete set of bids is observed. In the simple case with symmetric bidders and non-binding reserve price, we establish identification or partial identification results in two scenarios of practical interest. First, when the two highest bids are observed, we achieve identification of the joint distribution function of private values by assuming the copula function of private values to be a nonparametric Archimedean copula with weak requirement. Second, when only the highest bid is observed, we establish partial identification for the quantile function of private value and several policy parameters by parameterizing the copula function. Further, we extend the identification/partial identification results to the cases with asymmetric bidders and/or binding reserve price. We also extend our identification/partial identification results to the second-price sealed-bid auction.
Identification and Estimation of Auction Models with a Random Number of Bidders
Identification and Estimation of Auction Models with a Random Number of Bidders - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Identification and Estimation of Auction Models with a Random Number of Bidders write by . This book was released on 2013. Identification and Estimation of Auction Models with a Random Number of Bidders available in PDF, EPUB and Kindle. This dissertation is a collection of three chapters on structural analysis of auctions. The first chapter studies nonparametric identification of the distribution of bidder valuations in auctions where valuations are independently and symmetrically distributed, the number of bidders follows a Poisson distribution, and the number is not known to the bidders. I consider both first and second-price sealed bid auctions. If the data set consists of all auctions, including auctions with no bids or only one bid, then I show that data on either the first or second highest bid is sufficient for the model to be identified. If the data set does not include auctions with no bids and only the highest bids are observed, then information on the number of bidders is also needed for identification. In the second chapter, I develop a method for identifying and estimating a dynamic model of auctions like eBay. The market is modeled as an infinite sequence of second-price, sealed bid auctions of a homogenous good. Bidders arrive randomly and, upon arrival, they enter a pool of potential bidders. The actual bidders in an auction are drawn randomly from the pool. Conditional on bidding, a bidder exits if she wins and returns to the pool if she loses. Then bidders in the pool exit with some probability each period. I define and solve for the oblivious equilibrium (Weintraub et al. (2008)). I prove the stochastic stability and the existence of an equilibrium. The equilibrium yields a closed form solution for the bid function in which bidders shade their bids by their continuation values. I demonstrate that the model is identified (modulo the discount factor) from the data of bidder identities and the second highest bid. Based on the identification result, an estimation procedure is developed. In the third chapter I apply the model to a data from a Japanese online auction website. The estimation results suggest that market dynamics are important. The estimate of the valuations obtained when each auction is treated independently is 23% smaller than the estimates obtained from the dynamic model.
Essays on Nonparametric Identification and Estimation of All-Pay Auctions and Contests
Essays on Nonparametric Identification and Estimation of All-Pay Auctions and Contests - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Essays on Nonparametric Identification and Estimation of All-Pay Auctions and Contests write by Ksenia Shakhgildyan. This book was released on 2019. Essays on Nonparametric Identification and Estimation of All-Pay Auctions and Contests available in PDF, EPUB and Kindle. My dissertation contributes to the structural nonparametric econometrics of auctions and contests with incomplete information. It consists of three chapters. The first chapter investigates the identification and estimation of an all-pay auction where the object is allocated to the player with the highest bid, and every bidder pays his bid regardless of whether he wins or not. As a baseline model, I consider the setting, where one object is allocated among several risk-neutral participants with independent private values (IPV); however, I also show how the model can be extended to the multiunit case. Moreover, the model is not confined to the IPV paradigm, and I further consider the case where the bidders' private values are affiliated (APV). In both IPV and APV settings, I prove the identification and derive the consistent estimators of the distribution of the bidders' valuations using a structural approach similar to that of Guerre et al. (2000). Finally, I consider the model with risk-averse bidders. I prove that in general the model in this set-up is not identified even in the semi-parametric case where the utility function of the bidders is restricted to belong to the class of functions with constant absolute risk aversion (CARA). The second chapter proves the identification and derives the asymptotically normal estimator of a nonparametric contest of incomplete information with uncertainty. By uncertainty, I mean that the contest success function is not only determined by the bids of the players, but also by the variable, which I call uncertainty, with a nonparametric distribution, unknown to the researcher, but known to the bidders. This work is the first to consider the incomplete information contest with a nonparametric contest success function. The limiting case of the model when there is no uncertainty is an all-pay auction considered in the first chapter. The model with two asymmetric players is examined. First, I recover the distribution of uncertainty using the information on win outcomes and bids. Next, I adopt the structural approach of Guerre et al. (2000) to obtain the distribution of the bidders' valuations (or types). As an empirical application, I study the U.S. House of Representatives elections. The model provides a method to disentangle two sources of incumbency advantage: a better reputation, and better campaign financing. The former is characterized by the distribution of uncertainty and the latter by the difference in the distributions of candidates' types. Besides, two counterfactual analyses are performed: I show that the limiting expenditure dominates public campaign financing in terms of lowering total campaign spending as well as the incumbent's winning probability. The third chapter is a semiparametric version of the second chapter. In the case when the data is sparse, some restrictions on the nonparametric structure need to be put. In this work, I prove the identification and derive the consistent estimator of a contest of incomplete information, in which an object is allocated according to the serial contest success function. As in previous chapters, I recover the distribution of the bidders' valuations from the data on observed bids using a structural approach similar to that of Guerre et al. (2000) and He and Huang (2018). As a baseline model, I consider the symmetric contest. Further, the model is extended to account for the bidders' asymmetry.
Nonparametric Identification and Estimation of K-Double Auctions
Nonparametric Identification and Estimation of K-Double Auctions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Identification and Estimation of K-Double Auctions write by Huihui Li. This book was released on 2016. Nonparametric Identification and Estimation of K-Double Auctions available in PDF, EPUB and Kindle. This dissertation consists of two chapters on nonparametrically identifying and estimating the sealed-bid k-double auction models between single buyer and single seller.Chapter 1: Nonparametric Identification and Estimation of k-Double Auctions Using Bid DataThis chapter studies the nonparametric identification and estimation of double auctions with one buyer and one seller. This model assumes that both bidders submit their own sealed bids, and the transaction price is determined by a weighted average between the submitted bids when the buyers offer is higher than the sellers ask. It captures the bargaining process between two parties. Working within this double auction model, we first establish the nonparametric identification of both the buyers and the sellers private value distributions in two bid data scenarios; from the ideal situation in which all bids are available, to a more realistic setting in which only the transacted bids are available. Specifically, we can identify both private value distributions when all of the bids are observed. However, we can only partially identify the private value distributions on the support with positive (conditional) probability of trade when only the transacted bids are available in the data. Second, we estimate double auctions with bargaining using a two-step procedure that incorporates bias correction. We then show that our value density estimator achieves the same uniform convergence rate as Guerre, Perrigne, and Vuong (2000) for one-sided auctions. Monte Carlo experiments show that, in finite samples, our estimation procedure works well on the whole support and significantly reduces the large bias of the standard estimator without bias correction in both interior and boundary regions.Chapter 2: Nonparametric Identification of k-Double Auctions Using Price DataThis chapter studies the model identification problem of k-double auctions between one buyer and one seller when the transaction price, rather than the traders bids, can be observed. Given that only the price data is available, I explore an identification strategy that utilizes the double auctions with extreme pricing weight (k=1 or 0) and exclusive covariates that shift only one traders value distribution to identify both the buyers and the sellers value distributions nonparametrically. First, as each exclusive covariate can take at least two values, the buyers and the sellers value distributions are partially identified from the price distribution for k=1 or k=0. The identified set is sharp and can be easily computed. I provide a set of sufficient conditions under which the traders value distributions are point identified. Second, when the exclusive covariates are continuous, it is shown that the buyers and the sellers value distributions will be uniquely determined by a partial differential equation that only depends on the price distribution, provided that the value distributions are known for at least one value of the exclusive covariates.
Identification, Estimation and Testing of Auction Models
Identification, Estimation and Testing of Auction Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Identification, Estimation and Testing of Auction Models write by Jie Wei. This book was released on 2014. Identification, Estimation and Testing of Auction Models available in PDF, EPUB and Kindle. The third chapter shows nonparametric identification and estimation of private value distribution and density functions in first-price auctions with endogenous entry. In the model, symmetric bidders face a nontrivial entry cost and a binding reserve price. We identify latent structures by solving a two stage game, and estimate density functions (point-wisely) by using and comparing two different methods. Monte Carlo experiments show good performance of our estimators.
