Econometric models of asymmetric ascending auctions

We develop econometric models of ascending (English) auctions which allow for both bidder asymmetries as well as common and/or private value components in bidders’ underlying valuations. We show that the equilibrium inverse bid functions in each round of the auction are implicitly defined (pointwise) by a system of nonlinear equations, so that conditions for the existence and uniqueness of an increasing-strategy equilibrium are essentially identical to those which ensure a unique and increasing solution to the system of equations. We exploit the computational tractability of this characterization in order to develop an econometric model, thus extending the literature on structural estimation of auction models. Finally, an empirical example illustrates how equilibrium learning affects bidding during the course of the auction.     

Bayesian estimation approaches to first-price auctions

This paper considers Bayesian estimation strategies for first-price auctions within the independent private value paradigm. We develop an ‘optimization’ error approach that allows for estimation of values assuming that observed bids differ from optimal bids. We further augment this approach by allowing systematic over or underbidding by bidders using ideas from the stochastic frontier literature. We perform a simulation study to showcase the appeal of the method and apply the techniques to timber auction data collected in British Columbia. Our results suggest that significant underbidding is present in the timber auctions.     

Markov-switching models with endogenous explanatory variables II: A two-step MLE procedure

This paper proposes a two-step maximum likelihood estimation (MLE) procedure to deal with the problem of endogeneity in Markov-switching regression models. A joint estimation procedure provides us with an asymptotically most efficient estimator, but it is not always feasible, due to the ‘curse of dimensionality’ in the matrix of transition probabilities. A two-step estimation procedure, which ignores potential correlation between the latent state variables, suffers less from the ‘curse of dimensionality’, and it provides a reasonable alternative to the joint estimation procedure. In addition, our Monte Carlo experiments show that the two-step estimation procedure can be more efficient than the joint estimation procedure in finite samples, when there is zero or low correlation between the latent state variables.     

Descending price multi-item auctions

We propose a simultaneous descending price auction mechanism to sell multiple heterogeneous items, each owned by a distinct seller, to a number of buyers. Each buyer has known private valuations on items, and wants at most one item. We show that if the sellers follow a descending price offer procedure and the buyers follow a greedy strategy for accepting the offers, the auction results in a nearly efficient allocation, and terminates close to a competitive equilibrium price vector. The descending price offer strategy of the sellers is close to a Nash equilibrium. However, we show that the buyers are better off waiting in our auction. There is a maximum limit (corresponding to the minimum competitive equilibrium price vector) till which they can wait without running into the risk of not winning any item. If the buyers wait within this limit, the prices can be brought arbitrarily close to a uniquely defined competitive equilibrium price vector.     

Optimal auctions with multidimensional types and the desirability of exclusion

Within the context of a single-unit, independent private values auction model, we show that if bidder types are multidimensional, then under the optimal auction exclusion of a positive measure of bidder types occur. The results complements Armstrong’s (1996) exclusion of types result.     

A two-stage procedure for partially identified models

This paper studies a two-stage procedure for estimating partially identified models, based on Chernozhukov, Hong, and Tamer’s (2007) theory of set estimation and inference. We consider the case where a sub-vector of parameters or their identified set can be estimated separately from the rest, possibly subject to a priori restrictions. Our procedure constructs the second-stage set estimator and confidence set by taking appropriate level sets of a criterion function, using a first-stage estimator to impose restrictions on the parameter of interest. We give conditions under which the two-stage set estimator is a set-valued random element that is measurable in an appropriate sense. We also establish the consistency of the two-stage set estimator.     

Behavioral mechanism design: evidence from the modified first-price auctions

This paper tests the behavioral equivalence of a class of strategically-equivalent mechanisms that also do not differ in terms of their procedures. In a private value setting, we introduce a family of mechanisms, so-called Mechanism (α), that generalizes the standard first-price sealed-bid auction. In Mechanism (α), buyers are asked to submit a value which will then be multiplied by α to calculate the bids in the auction. When α =?1, Mechanism (α) is the standard first-price sealed-bid auction. We show that for any α, calculated bids should be identical across mechanisms. We conduct a laboratory experiment to test the behavioral equivalence of this class of mechanisms under different values of α. Even though the procedure and environment do not change across auctions, we do not observe the same bidding behavior across these strategically-equivalent mechanisms. Our research can inform mechanism design literature with respect to the design of optimal mechanisms.     

Empirical implementation of nonparametric first-price auction models

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparametric estimation of first-price auction models. Specifically, we show how to impose monotonicity of the equilibrium bidding strategy; a key property of structural auction models not guaranteed in standard nonparametric estimation. We further develop methods for automatic bandwidth selection. Finally, we discuss how to impose monotonicity in auctions with differing numbers of bidders, reserve prices, and auction-specific characteristics. Finite sample performance is examined using simulated data as well as experimental auction data.     

Revenue and efficiency effects of resale in first-price auctions

We study first-price auctions in a model with asymmetric, independent private values. Asymmetries lead to inefficient allocations, thereby creating a motive for resale after the auction is over. In our model, resale takes place via monopoly pricing—the winner of the auction makes a take-it-or-leave-it offer to the loser. Our goal is to compare equilibria of the first-price auction without resale (FPA) with those of the first-price auction with resale (FPAR). For the three major families of distributions for which equilibria of the FPA are available in closed form, we show that resale possibilities increase the revenue of the original seller. We also show by example that, somewhat paradoxically, resale may actually decrease efficiency.     

Optimal auctions for asymmetrically budget constrained bidders

We consider an environment with a single divisible good and two bidders. The valuations of the bidders are private information but one bidder has a commonly known budget constraint. For this environment we derive the revenue maximizing subsidy free dominant strategy incentive compatible auction.      

Auctions with resale and bargaining power

We show that when the weak bidder’s bargaining power in the resale market is weakened, the auctioneer’s revenue from the first-price auction with resale is lower. Using the idea of Coase Theorem, we show that when the resale market is a sequential bargaining model with no commitment, the auctioneer’s revenue is substantially reduced, and the ranking is the opposite of Hafalir and Krishna (2009). We establish a version of the Coase Theorem in the context of the auctions with resale. When Coase Theorem holds, we show that the revenue of the auction with resale is lower than the revenue of the same auction without resale. We also provide the existence and uniqueness of equilibrium for our model of auctions with resale.     

Innovation contests with entry auction

We consider innovation contests for the procurement of an innovation under moral hazard and adverse selection. Innovators have private information about their abilities, and choose unobservable effort in order to produce innovations of random quality. Innovation quality is not contractible. We compare two procurement mechanisms—a fixed prize and a first-price auction. Before the contest, a fixed number of innovators is selected in an entry auction, in order to address the adverse selection problem. We find that–if effort and ability are perfect substitutes–both mechanisms implement the same innovations in symmetric pure-strategy equilibrium, regardless of whether the innovators’ private information is revealed or not. These equilibria are efficient if the procurer is a welfare-maximizer.     

Semiparametric estimation of a bivariate Tobit model

The existing semiparametric estimation literature has mainly focused on univariate Tobit models and no semiparametric estimation has been considered for bivariate Tobit models. In this paper, we consider semiparametric estimation of the bivariate Tobit model proposed by Amemiya (1974), under the independence condition without imposing any parametric restriction on the error distribution. Our estimator is shown to be consistent and asymptotically normal, and simulation results show that our estimator performs well in finite samples. It is also worth noting that while Amemiya’s (1974) instrumental variables estimator (IV) requires the normality assumption, our semiparametric estimator actually outperforms his IV estimator even when normality holds. Our approach can be extended to higher dimensional multivariate Tobit models.     

Estimating first-price auctions with an unknown number of bidders: A misclassification approach

In this paper, we consider nonparametric identification and estimation of first-price auction models when N^∗$N^{\ast }$, the number of potential bidders, is unknown to the researcher, but observed by bidders. Exploiting results from the recent econometric literature on models with misclassification error, we develop a nonparametric procedure for recovering the distribution of bids conditional on the unknown N^∗$N^{\ast }$. Monte Carlo results illustrate that the procedure works well in practice. We present illustrative evidence from a dataset of procurement auctions, which shows that accounting for the unobservability of N^∗$N^{\ast }$ can lead to economically meaningful differences in the estimates of bidders’ profit margins.     

Estimating dynamic local interactions models

This paper presents empirical methods for studying a class of local interactions models in which agents’ transitions are affected by their neighbors’ states. We consider an application to urban unemployment and social networks in job search using publicly available cross-section and retrospective data. Most links in our model are local, but some span an entire metropolitan area. Our methods are designed to accommodate the presence of strong cross-sectional dependence arising from these few cross-metro-area links. We also present simple methods to compare data and model spell distributions and to illustrate the model's dynamic properties.     

A consistent nonparametric test of affiliation in auction models

We propose a new nonparametric test of affiliation, a strong form of positive dependence with independence as a special, knife-edge, case. The test is consistent against all departures from the null of affiliation, and its null distribution is standard normal. Like most nonparametric tests, a sample-size dependent input parameter is needed. We provide an informal procedure for choosing the input parameter and evaluate the test’s performance using a simulation study. Our test can be used to test the fundamental assumptions of the auctions literature. We implement our test empirically using the Outer Continental Shelf (OCS) auction data.     

Information acquisition and/or bid preparation: A structural analysis of entry and bidding in timber sale auctions

Recently, several auction models with entry have been proposed: in one model ( and ), bidders are assumed to draw their private values after they decide to enter. In another model ( and ), bidders are assumed to learn their values before their entry decisions are made. The entry cost in the latter model can be interpreted as bid preparation cost, while the entry cost in the former model consists of both costs from information acquisition and bid preparation. Moreover, these two models have different implications for important policies, e.g., the optimal reserve price. In this paper we provide a unified structural framework where the two models can be estimated and distinguished using the Bayesian method. We apply our method to analyze Michigan timber sale auctions.     

First-price auctions where one of the bidders’ valuations is common knowledge

We analyze a private and independent valuation first-price auction under the assumption that one of the bidders’ valuations is common knowledge. We show that no pure strategy equilibrium exists and we characterize a mixed strategy equilibrium in which the bidder whose valuation is common knowledge randomizes her bid while the other bidders play pure strategies. In an example with the uniform distribution, we compare the expected profits of seller and buyers in this auction with those in a standard symmetric private valuation model.     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

Cross-validated SNP density estimates

We consider cross-validation strategies for the seminonparametric (SNP) density estimator, which is a truncation (or sieve) estimator based upon a Hermite series expansion with coefficients determined by quasi-maximum likelihood. Our main focus is on the use of SNP density estimators as an adjunct to efficient method of moments (EMM) structural estimation. It is known that for this purpose a desirable truncation point occurs at the last point at which the integrated squared error (ISE) curve of the SNP density estimate declines abruptly. We study the determination of the ISE curve for iid data by means of leave-one-out cross-validation and hold-out-sample cross-validation through an examination of their performance over the Marron–Wand test suite and models related to asset pricing and auction applications. We find that both methods are informative as to the location of abrupt drops, but that neither can reliably determine the minimum of the ISE curve. We validate these findings with a Monte Carlo study. The hold-out-sample method is cheaper to compute because it requires fewer nonlinear optimizations. We consider the asymptotic justification of hold-out-sample cross-validation. For this purpose, we establish rates of convergence of the SNP estimator under the Hellinger norm that are of interest in their own right.