Download or read book Nonparametric Identification of Incomplete Information written by Erhao Xie and published by . This book was released on 2022 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the literature that estimates discrete games with incomplete information, researchers usually impose two assumptions. First, either the payoff function or the distribution of private information or both are restricted to follow some parametric functional forms. Second, players' behaviors are assumed to be consistent with the Bayesian Nash equilibrium. This paper jointly relaxes both assumptions. The framework non-parametrically specifies both the payoff function and the distribution of private information. In addition, each player's belief about other players' behaviors is also modeled as a nonparametric function. I allow this belief function to be any probability distribution over other players' action sets. This specification nests the equilibrium assumption when each player's belief corresponds to other players' actual choice probabilities. It also allows non-equilibrium behaviors when some players' beliefs are biased or incorrect. Under the above framework, this paper first derives a testable implication of the equilibrium condition. It then obtains the identification results for the payoff function, the belief function and the distribution of private information.

Download or read book Identification and Estimation of Incomplete Information Games with Multiple Equilibria written by Ruli Xiao and published by . This book was released on 2015 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Nonparametric Identification and Estimation of All Pay Auctions and Contests written by Ksenia Shakhgildyan and published by . This book was released on 2019 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Microeconometrics written by Steven Durlauf and published by Springer. This book was released on 2016-06-07 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.

Download or read book Semiparametric Estimation of a Dynamic Game of Incomplete Information written by Patrick L. Bajari and published by . This book was released on 2006 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, empirical industrial organization economists have proposed estimators for dynamic games of incomplete information. In these models, agents choose from a finite number actions and maximize expected discounted utility in a Markov perfect equilibrium. Previous econometric methods estimate the probability distribution of agents' actions in a first stage. In a second step, a finite vector of parameters of the period return function are estimated. In this paper, we develop semiparametric estimators for dynamic games allowing for continuous state variables and a nonparametric first stage. The estimates of the structural parameters are T1/2 consistent (where T is the sample size) and asymptotically normal even though the first stage is estimated nonparametrically. We also propose sufficient conditions for identification of the model

Download or read book Essays on Nonparametric and Semiparametric Identification and Estimation written by Shenshen Yang and published by . This book was released on 2021 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three chapters in econometric theory, with a focus on identification and estimation of treatment effect in semi-parametric and nonparametric models, when there exists endogeneity problem. These methods are applied on policy and program evaluation in health and labor economics. \indent In the first chapter, I examine the common problem of multiple missing variables, which we refer to as multiple missingness, with non-monotone missing pattern and is usually caused by sub-sampling and a combination of different data sets. One example of this is missingness in both the endogenous treatment and outcome when two variables are collected via different stages of follow-up surveys. Two types of dependence assumptions for multiple missingness are proposed to identify the missing mechanism. The identified missing mechanisms are used later in an Augmented Inverse Propensity Weighted moment function, based on which a two-step semiparametric GMM estimator of the coefficients in the primary model is proposed. This estimator is consistent and more efficient than the previously used estimation methods because it includes incomplete observations. We demonstrate that robustness and asymptotic variances differ under two sets of identification assumptions, and we determine sufficient conditions when the proposed estimator can achieve the semiparametric efficiency bound. This method is applied to the Oregon Health Insurance Experiment and shows the significant effects of enrolling in the Oregon Health Plan on improving health-related outcomes and reducing out-of-pocket costs for medical care. The method proposed here provides unbiased and more efficient estimates. There is evidence that simply dropping the incomplete data creates downward biases for some of the chosen outcome variables. Moreover, the estimator proposed in this paper reduced standard errors by 6-24% of the estimated effects of the Oregon Health Plan. \indent The second chapter is a joint work with Sukjin Han. In this chapter, we consider how to extrapolate the general local treatment effect in a non-parametric setting, with endogenous self-selection problem and lack of external validity. For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to the policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This chapter investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to incorporate a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services. \indent In the third chapter, I investigate the partial identification bound for treatment effect in a dynamic setting. First, I develop the sharp partial identification bounds of dynamic treatment effect on conditional transition probabilities when the treatment is randomly assigned. Then I relax the randomization assumption and gives partial identification bounds, under a conditional mean independence assumption. Using MTR and MTS assumptions, this bound is further tightened. These bounds are used on estimating labor market return of college degree in a long term, with data from NLSY79

Download or read book Nonparametric Tests for Complete Data written by Vilijandas Bagdonavicius and published by John Wiley & Sons. This book was released on 2013-02-04 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book concerns testing hypotheses in non-parametric models. Classical non-parametric tests (goodness-of-fit, homogeneity, randomness, independence) of complete data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.

Download or read book Handbook of Industrial Organization written by and published by Elsevier. This book was released on 2021-12-09 with total page 788 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Industrial Organization, Volume Four highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of expert authors. Presents authoritative surveys and reviews of advances in theory and econometrics Reviews recent research on capital raising methods and institutions Includes discussions on developing countries

Download or read book Foundations of Statistical Inference written by Yoel Haitovsky and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a collection of papers presented at a conference held in Shoresh Holiday Resort near Jerusalem, Israel, in December 2000 organized by the Israeli Ministry of Science, Culture and Sport. The theme of the conference was "Foundation of Statistical Inference: Applications in the Medical and Social Sciences and in Industry and the Interface of Computer Sciences". The following is a quotation from the Program and Abstract booklet of the conference. "Over the past several decades, the field of statistics has seen tremendous growth and development in theory and methodology. At the same time, the advent of computers has facilitated the use of modern statistics in all branches of science, making statistics even more interdisciplinary than in the past; statistics, thus, has become strongly rooted in all empirical research in the medical, social, and engineering sciences. The abundance of computer programs and the variety of methods available to users brought to light the critical issues of choosing models and, given a data set, the methods most suitable for its analysis. Mathematical statisticians have devoted a great deal of effort to studying the appropriateness of models for various types of data, and defining the conditions under which a particular method work. " In 1985 an international conference with a similar title* was held in Is rael. It provided a platform for a formal debate between the two main schools of thought in Statistics, the Bayesian, and the Frequentists.

Download or read book Bayesian Nonparametric and Semi parametric Methods for Incomplete Longitudinal Data written by Chenguang Wang and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 4, we discuss pattern mixture models. Pattern mixture modeling is a popular approach for handling incomplete longitudinal data. Such models are not identifiable by construction. Identifying restrictions are one approach to mixture model identification (Daniels and Hogan, 2008; Kenward et al., 2003; Little, 1995; Little and Wang, 1996; Thijs et al., 2002) and are a natural starting point for missing not at random sensitivity analysis (Daniels and Hogan, 2008; Thijs et al., 2002). However, when the pattern specific models are multivariate normal (MVN), identifying restrictions corresponding to missing at random may not exist. Furthermore, identification strategies can be problematic in models with covariates (e.g. baseline covariates with time-invariant coefficients). In this paper, we explore conditions necessary for identifying restrictions that result in missing at random (MAR) to exist under a multivariate normality assumption and strategies for identifying sensitivity parameters for sensitivity analysis or for a fully Bayesian analysis with informative priors. A longitudinal clinical trial is used for illustration of sensitivity analysis. Problems caused by baseline covariates with time-invariant coefficients are investigated and an alternative identifying restriction based on residuals is proposed as a solution.

Download or read book Essays on Nonparametric Identification written by Dan Ben-Moshe and published by . This book was released on 2012 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 1, I extend the techniques in Li and Vuong (1998), Schennach (2004a), and Bonhomme and Robin (2010) to identify nonparametric distributions of unobserved variables in a system of linear equations with more unobserved variables than outcome variables and with subsets of statistically dependent unobserved variables. I construct estimators of the distributions of unobserved variables and derive their uniform convergence rates. In Chapter 2, I develop a method for identification and estimation of coefficients in a linear regression model with measurement error in all the variables. The method is extended to identification in a system of linear equations in which only some of the coefficients on the unobserved variables are known. The estimator uses an assumption that is testable in the data and is in the class of Extremum estimators. The asymptotic distribution of the estimator is derived. In Chapter 3, I identify the nonparametric joint distribution of random coefficients in a linear panel data regression model. The distributions of the coefficients can depend on covariates, coefficients can be statistically dependent or equal in distribution, and there can be more coefficients than the fixed number of time periods. I construct estimators from the identification proofs. In finite sample simulations all the estimators have tight confidence bands around their theoretical counterparts.

Download or read book Nonparametric Methods for Incomplete Data in Reliability and for Changepoints in Smooth Functions written by and published by . This book was released on 1994 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research funded during this period led to more than 30 accepted or published papers in refereed statistical journals. Several graduate students were involved in the research during this period who contributed significantly to the success of the project as well as benefited form opportunities for educational and research achievements leading to the PhD degree (Sam Hawala, Kathryn Prewitt and Kai-Sheng Song). The research achievements for this project roughly fall into five areas of research, all of which are in nonparametric statistics. They cover a wide range of topics from the applied to the theoretical and have important implications for data analysis, as well as for the theory of Statistics. (AN).

Download or read book Nonparametric Identification and Estimation of Contests with Uncertainty written by Ksenia Shakhgildyan and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonparametric Inference written by Z. Govindarajulu and published by World Scientific. This book was released on 2007 with total page 692 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and serves as an easily accessible source for researchers in the area.With the exception of some sections requiring familiarity with measure theory, readers with an advanced calculus background will be comfortable with the material.

Download or read book Dynamic econometric analysis of insurance markets with imperfect information written by Tibor Zavadil and published by Rozenberg Publishers. This book was released on 2008 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Finite Mixture and Markov Switching Models written by Sylvia Frühwirth-Schnatter and published by Springer Science & Business Media. This book was released on 2006-11-24 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.

Download or read book Identification of Biased Beliefs in Games of Incomplete Information Using Experimental Data written by Victor Aguirregabiria and published by . This book was released on 2016 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies the identification of players' preferences and beliefs in empirical applications of discrete choice games using experimental data. The experiment comprises a set of games with similar features (e.g., two-player coordination games) where each game has different values for the players' monetary payoffs. Each game can be interpreted as an experimental treatment group. The researcher assigns randomly subjects to play these games and observes the outcome of the game as described by the vector of players' actions. Data from this experiment can be described in terms of the empirical distribution of players' actions conditional on the treatment group. The researcher is interested in the nonparametric identification of players' preferences (utility function of money) and players' beliefs about the expected behavior of other players, without imposing restrictions such as unbiased or rational beliefs or a particular functional form for the utility of money. We show that the hypothesis of unbiased/rational beliefs is testable and propose a test of this null hypothesis. We apply our method to two sets of experiments conducted by Goeree and Holt (2001) and Heinemann, Nagel and Ockenfels (2009). Our empirical results suggest that in the matching pennies game, a player is able to correctly predict other player's behavior. In the public good coordination game, our test can reject the null hypothesis of unbiased beliefs when the payoff of the non-cooperative action is relatively low.