Semiparametric Estimation of Signaling Games

This paper studies an econometric modeling of a signaling game with two players where one player has one of two types. In particular, we develop an estimation strategy that identifies the payoffs structure and the distribution of types from data of observed actions. We can achieve uniqueness of equilibrium using a refinement, which enables us to identify the parameters of interest. In the game, we consider non-strategic public signals about the types. Because the mixing distribution of these signals is nonparametrically specified, we propose to estimate the model using a sieve conditional MLE. We achieve the consistency and the asymptotic normality of the structural parameters estimates. As an alternative, we allow for the possibility of multiple equilibria, without using an equilibrium selection rule. As a consequence, we adopt a set inference allowing for multiplicity of equilibria.Semiparametric Estimation, Signaling Game, Set Inference, Infinite Dimensional Parame- ters, Sieve Simultaneous Conditional MLE

Higher Order Bias Correcting Moment Equation for M-Estimation and its Higher Order Efficiency

This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment equation in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically bias-corrected estimator and the alternative estimator and finds that the third-order stochastic expansions of these two estimators are identical. This implies that at least in terms of the third order stochastic expansion, we cannot improve on the simple one-step bias correction by using the bias correction of moment equations. Though the result in this paper is for a fixed number of parameters, our intuition may extend to the analytical bias correction of the panel data models with individual specific effects. Noting the M-estimation can nest many kinds of estimators including IV, 2SLS, MLE, GMM, and GEL, our finding is a rather strong result.Third-order Stochastic Expansion, Bias Correction, M-estimation

Higher Order Bias Correcting Moment Equation for M-Estimation and its Higher Order Efficiency

This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment equation in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically bias-corrected estimator and the alternative estimator and finds that the third-order stochastic expansions of these two estimators are identical. This implies that at least in terms of the third order stochastic expansion, we cannot improve on the simple one-step bias correction by using the bias correction of moment equations. Though the result in this paper is for a .xed number of parameters, our intuition may extend to the analytical bias correction of the panel data models with individual speci.c eects. Noting the M-estimation can nest many kinds of estimators including IV, 2SLS, MLE, GMM, and GEL, our .nding is a rather strong result.Third-order Stochastic Expansion, Bias Correction, M-estimation

Uniform Convergence Rate of the SNP Density Estimator and Testing for Similarity of Two Unknown Densities

This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.SNP Density Estimator, Uniform Convergence Rate, Comparison of Two Densities

Set Inference for Semiparametric Discrete Games

We consider estimation and inference of parameters in discrete games allowing for multiple equilibria, without using an equilibrium selection rule. We do a set inference while a game model can contain infinite dimensional parameters. Examples can include signaling games with discrete types where the type distribution is nonparametrically specified and entry-exit games with partially linear payoffs functions. A consistent set estimator and a confidence interval of a function of parameters are provided in this paper. We note that achieving a consistent point estimation often requires an information reduction. Due to this less use of information, we may end up a point estimator with a larger variance and have a wider confidence interval than those of the set estimator using the full information in the model. This finding justifies the use of the set inference even though we can achieve a consistent point estimation. It is an interesting future research to compare these two alternatives: CI from the point estimation with the usage of less information vs. CI from the set estimation with the usage of the full information.Semiparametric Estimation, Set Inference, Infinite Dimensional Parameters, Inequality Moment Conditions, Signaling Game with Discrete Types

Uniform Convergence Rate of the SNP Density Estimator and Testing for Similarity of Two Unknown Densities

This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.SNP Density Estimator, Uniform Convergence Rate, Comparison of Two Densities

Set Inference for Semiparametric Discrete Games

We consider estimation and inference of parameters in discrete games allowing for multiple equilibria, without using an equilibrium selection rule. We do a set inference while a game model can contain infinite dimensional parameters. Examples can include signaling games with discrete types where the type distribution is nonparametrically specified and entry-exit games with partially linear payoffs functions. A consistent set estimator and a con.dence interval of a function of parameters are provided in this paper. We note that achieving a consistent point estimation often requires an information reduction. Due to this less use of information, we may end up a point estimator with a larger variance and have a wider confidence interval than those of the set estimator using the full information in the model. This finding justifies the use of the set inference even though we can achieve a consistent point estimation. It is an interesting future research to compare these two alternatives : CI from the point estimation with the usage of less information vs. CI from the set estimation with the usage of the full information.Semiparametric Estimation, Set Inference, Inequality Moment Conditions, Signaling Game with Discrete Types

A New Control Function Approach for Non-Parametric Regressions with Endogenous Variables

When the endogenous variable enters the structural equation non-parametrically the linear Instrumental Variable (IV) estimator is no longer consistent. Non-parametric IV (NPIV) can be used but it requires one to impose restrictions during estimation to make the problem well-posed. The non-parametric control function estimator of Newey, Powell, and Vella (1999) (NPV-CF) is an alternative approach that uses the residuals from the conditional mean decomposition of the endogenous variable as controls in the structural equation. While computationally simple identification relies upon independence between the instruments and the expected value of the structural error conditional on the controls, which is hard to motivate in many economic settings including estimation of returns to education, production functions, and demand or supply elasticities. We develop an estimator for non-linear and non-parametric regressions that maintains the simplicity of the NPV-CF estimator but allows the conditional expectation of the structural error to depend on both the control variables and the instruments. Our approach combines the conditional moment restrictions (CMRs) from NPIV with the controls from NPV-CF setting. We show that the CMRs place shape restrictions on the conditional expectation of the error given instruments and controls that are sufficient for identification. When sieves are used to approximate both the structural function and the control function our estimator reduces to a series of Least Squares regressions. Our monte carlos are based on the economic settings suggested above and illustrate that our new estimator performs well when the NPV-CF estimator is biased. Our empirical example replicates NPV-CF and we reject the maintained assumption of the independence of the instruments and the expected value of the structural error conditional on the controls in their setting.

Semiparametric Estimation of Signaling Games

This paper studies an econometric modeling of a signaling game with two players where one player has one of two types. In particular, we develop an estimation strategy that identi\u85es the payo¤s structure and the distribution of types from data of observed actions. We can achieve uniqueness of equilibrium using a re nement, which enables us to identify the parameters of interest. In the game, we consider non-strategic public signals about the types. Because the mixing distribution of these signals is nonparametrically speci ed, we propose to estimate the model using a sieve conditional MLE. We achieve the consistency and the asymptotic normality of the structural parameters estimates. As an alternative, we allow for the possibility of multiple equilibria, without using an equilibrium selection rule. As a consequence, we adopt a set inference allowing for multiplicity of equilibria

Higher Order Bias Correcting Moment Equation for M-Estimation and Its Higher Order Efficiency

Published in Econometrics 2016, https://doi.org/10.3390/econometrics4040048</p