Denis Nekipelov

Estimating Static Models of Strategic Interactions

We study the estimation of static games of incomplete information with multiple equilibria. A static game is a generalization of a discrete choice model, such as a multinomial logit or probit, which allows the actions of a group of agents to be interdependent. While the estimator we study is quite flexible, in most cases it can be easily implemented using standard statistical packages such as STATA. We also propose an algorithm for simulating the model which finds all equilibria to the game. As an application of our estimator, we study recommendations for high technology stocks between 1998–2003. We find that strategic motives, typically ignored in the empirical literature, appear to be an important consideration in the recommendations submitted by equity analysts.

Game Theory and Econometrics: A Survey of Some Recent Research

We survey an emerging literature on the econometric analysis of static and dynamic models of strategic interactions. Econometric methods of identication and estimation allow researcher to make use of observed data on individual choice behavior and on the conditional transition distribution of state variables to recover the underlying structural parameters of payo functions and discount rates nonparametrically without imposing strong functional form assumptions. We also discuss the progress that the literature has made on understanding the role of unobserved heterogeneity in the estimation analysis of these models, and other related issues.

Mechanism design for data science

The promise of data science is that if data from a system can be recorded and understood then this understanding can potentially be utilized to improve the system. Behavioral and economic data, however, is different from scientific data in that it is subjective to the system. Behavior changes when the system changes, and to predict behavior for any given system change or to optimize over system changes, the behavioral model that generates the data must be inferred from the data. The ease with which this inference can be performed generally also depends on the system. Trivially, a system that ignores behavior does not admit any inference of a behavior generating model that can be used to predict behavior in a system that is responsive to behavior. To realize the promise of data science in economic systems, a theory for the design of such systems must also incorporate the desired inference properties. Consider as an example the revenue-maximizing auctioneer. If the auctioneer has knowledge of the distribution of bidder values then she can run the first-price auction with a reserve price that is tuned to the distribution. Under some mild distributional assumptions, with the appropriate reserve price the first-price auction is revenue optimal [Myerson 1981]. Notice that the historical bid data for the first-price auction with a reserve price will in most cases not have bids for bidders whose values are below the reserve. Therefore, there is no data analysis that the auctioneer can perform that will enable properties of the distribution of bidder values below the reserve price to be inferred. It could be, nonetheless, that over time the population of potential bidders evolves and the optimal reserve price lowers. This change could go completely unnoticed in the auctioneers data. The two main tools for optimizing revenue in an auction are reserve prices (as above) and ironing. Both of these tools cause pooling behavior (i.e., bidders with distinct values take the same action) and economic inference cannot thereafter differentiate these pooled bidders. In order to maintain the distributional knowledge necessary to be able to run a good auction in the long term, the auctioneer must sacrifice the short-term revenue by running a non-revenue-optimal auction.

Semiparametric efficiency in nonlinear LATE models

In this paper we study semiparametric efficiency for the estimation of a finite- dimensional parameter defined by generalized moment conditions under the lo- cal instrumental variable assumptions. These parameters identify treatment ef- fects on the set of compliers under the monotonicity assumption. The distrib- utions of covariates, the treatment dummy, and the binary instrument are not specified in a parametric form, making the model semiparametric. We derive the semiparametric efficiency bounds for both conditional models and uncondi- tional models. We also develop multistep semiparametric efficient estimators that achieve the semiparametric efficiency bound. To illustrate the efficiency gains from using the optimal semiparametric weights, we design a Monte Carlo study. It demonstrates that our semiparametric estimator performs well in nonlinear mod- els. Keywords. Semiparametric efficiency bound, local treatment effect, FTP, child achievement, unemployment benefits. JEL classification. C25, C26, C31.

Approximation Properties of Laplace-Type Estimators

The Laplace-type estimator (LTE) is a simulation-based alternative to the classical extremum estimator that has gained popularity in applied research. We show that even though the estimator has desirable asymptotic properties, in small samples the point estimate provided by LTE may not necessarily converge to the extremum of the sample objective function. Furthermore, we suggest a simple test to verify if the estimator converges. We illustrate these results by estimating a prototype dynamic stochastic general equilibrium model widely used in macroeconomics research.

Consistent Variance of the Laplace‐Type Estimators: Application to DSGE Models

The Laplace‐type estimator has become popular in applied macroeconomics, in particular for estimation of dynamic stochastic general equilibrium (DSGE) models. It is often obtained as the mean and variance of a parameters quasi‐posterior distribution, which is defined using a classical estimation objective. We demonstrate that the objective must be properly scaled; otherwise, arbitrarily small confidence intervals can be obtained if calculated directly from the quasi‐posterior distribution. We estimate a standard DSGE model and find that scaling up the objective may be useful in estimation with problematic parameter identification. It this case, however, it is important to adjust the quasi‐posterior variance to obtain valid confidence intervals.

Robust Data-Driven Guarantees in Auctions

Analysis of welfare in auctions comes traditionally via one of two approaches: precise but fragile inference of the exact details of a setting from data or robust but coarse theoretical price of anarchy bounds that hold in any setting. As markets get more and more dynamic and bidders become more and more sophisticated, the weaknesses of each approach are magnified.In this paper, we provide tools for analyzing and estimating the empirical price of anarchy of an auction. The empirical price of anarchy is the worst case efficiency loss of any auction that could have produced the data, relative to the optimal. Our techniques are based on inferring simple properties of auctions: primarily the expected revenue and the expected payments and allocation probabilities from possible bids. These quantities alone allow us to empirically estimate the revenue covering parameter of an auction which allows us to re-purpose the theoretical machinery of Hartline et al. [2014] for empirical purposes. Moreover, we show that under general conditions the revenue covering parameter estimated from the data approaches the true parameter with the error decreasing at the rate proportional to the square root of the number of auctions and at most polynomially in the number of agents. While we focus on the setting of position auctions, and particularly the generalized second price auction, our techniques are applicable far more generally. Finally, we apply our techniques to a selection of advertising auctions on Microsofts Bing and find empirical results that are a significant improvement over the theoretical worst-case bounds.

Eliciting preferences of sponsored search advertisers: implications for mechanism design

Sponsored search advertising attracts hundreds of thousands of advertisers, many with dozens or even thousands of campaigns, leading to tens of millions of distinct keyword bids. Advertiser objectives are heterogeneous. Some advertisers primarily focus on making immediate sales that are referred by clicks, while others want to promote their brand with a top-position placement. In this letter we demonstrate how one can use the empirical bidding data to recover the values of bidders in a sponsored search marketplace when the type of bidder preferences is known (i.e. whether a given bidder values clicks). We also show how one can use the history of bid changes for a given bidder to recover both the type of preferences for this bidder and the value at once. This methodology has direct implications for mechanism design making the case for combining the empirical work and auction design to avoid the optimization of the auction mechanism for the wrong preference type of the bidders.

The Role of Royalties in Resource Extraction Contracts

The manner in which governments charge mineral resource producers has been the subject of considerable debate. Income-based charges such as resource rent taxes have been advocated on the theory that royalties and other output-based charges create inefficiency by distorting production decisions. Using a principal-agent approach to resource contracts, separating asset ownership from asset use, we demonstrate that royalties can be efficient under conditions of certainty and also when there is uncertainty and asymmetric information. Royalties serve a key pricing purpose, signaling the marginal impact of extraction on the residual value of reserves and surrounding land or sea. (JEL H21, Q38)

Structural Econometrics of Auctions: A Review

We review the literature concerned with the structural econometrics of observational data from auctions, discussing the problems that have been solved and highlighting those that remain unsolved as well as suggesting areas for future research. Where appropriate, we discuss different modeling choices as well as the fragility or robustness of different methods.