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Full-Text Articles in Social and Behavioral Sciences
Aggregate Implications Of Firm Heterogeneity: A Nonparametric Analysis Of Monopolistic Competition Trade Models, Rodrigo Adão, Costas Arkolakis, Sharat Ganapati
Aggregate Implications Of Firm Heterogeneity: A Nonparametric Analysis Of Monopolistic Competition Trade Models, Rodrigo Adão, Costas Arkolakis, Sharat Ganapati
Cowles Foundation Discussion Papers
We measure the role of firm heterogeneity in counterfactual predictions of monopolistic competition trade models without parametric restrictions on the distribution of firm fundamentals. We show that two bilateral elasticity functions are sufficient to nonparametrically compute the counterfactual aggregate impact of trade shocks, and recover changes in economic fundamentals from observed data. These functions are identified from two semiparametric gravity equations governing the impact of bilateral trade costs on the extensive and intensive margins of firm-level exports. Applying our methodology, we estimate elasticity functions that imply an impact of trade costs on trade flows that falls when more firms serve …
Finite-Sample Optimal Estimation And Inference On Average Treatment Effects Under Unconfoundedness, Timothy B. Armstrong, Michal Kolesár
Finite-Sample Optimal Estimation And Inference On Average Treatment Effects Under Unconfoundedness, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates. Given nonparametric smoothness and/or shape restrictions on the conditional mean of the outcome variable, we derive estimators and confidence intervals (CIs) that are optimal infinite samples when the regression errors are normal with known variance. In contrast to conventional CIs, our CIs use a larger critical value that explicitly takes into account the potential bias of the estimator. When the error distribution is unknown, feasible versions of our CIs are valid asymptotically, even when √n-inference is not possible due …
Finite-Sample Optimal Estimation And Inference On Average Treatment Effects Under Unconfoundedness, Timothy B. Armstrong, Michal Kolesár
Finite-Sample Optimal Estimation And Inference On Average Treatment Effects Under Unconfoundedness, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates. We derive finite-sample optimal estimators and confidence intervals (CIs) under the assumption of normal errors when the conditional mean of the outcome variable is constrained only by nonparametric smoothness and/or shape restrictions. When the conditional mean is restricted to be Lipschitz with a large enough bound on the Lipschitz constant, we show that the optimal estimator reduces to a matching estimator with the number of matches set to one. In contrast to conventional CIs, our CIs use a larger …
Log Periodogram Regression: The Nonstationary Case, Chang Sik Kim, Peter C.B. Phillips
Log Periodogram Regression: The Nonstationary Case, Chang Sik Kim, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Estimation of the memory parameter ( d ) is considered for models of nonstationary fractionally integrated time series with d > (1/2). It is shown that the log periodogram regression estimator of d is inconsistent when 1 < d < 2 and is consistent when (1/2) < d = 1. For d > 1, the estimator is shown to converge in probability to unity.
Local Whittle Estimation Of Fractional Integration, Katsumi Shimotsu, Peter C.B. Phillips
Local Whittle Estimation Of Fractional Integration, Katsumi Shimotsu, Peter C.B. Phillips
Cowles Foundation Discussion Papers
An exact form of the local Whittle likelihood is studied with the intent of developing a general purpose estimation procedure for the memory parameter ( d ) that does not rely on tapering or differencing prefilters. The resulting exact local Whittle estimator is shown to be consistent and to have the same N (0,1/4) limit distribution for all values of d if the optimization covers an interval of width less than 9/2 and the initial value of the process is known.
Weighted Minimum Mean-Square Distance From Independence Estimation, Donald J. Brown, Marten H. Wegkamp
Weighted Minimum Mean-Square Distance From Independence Estimation, Donald J. Brown, Marten H. Wegkamp
Cowles Foundation Discussion Papers
In this paper we introduce a family of semi-parametric estimators, suggested by Manski’s minimum mean-square distance from independence estimator. We establish the strong consistency, asymptotic normality and consistency of bootstrap estimates of the sampling distribution and the asymptotic variance of these estimators.
Modified Local Whittle Estimation Of The Memory Parameter In The Nonstationary Case, Katsumi Shimotsu, Peter C.B. Phillips
Modified Local Whittle Estimation Of The Memory Parameter In The Nonstationary Case, Katsumi Shimotsu, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Semiparametric estimation of the memory parameter is studied in models of fractional integration in the nonstationary case, and some new representation theory for the discrete Fourier transform of a fractional process is used to assist in the analysis. A limit theory is developed for an estimator of the memory parameter that covers a range of values of d commonly encountered in applied work with economic data. The new estimator is called the modified local Whittle estimator and employs a version of the Whittle likelihood based on frequencies adjacent to the origin and modified to take into account the form of …
Local Whittle Estimation In Nonstationary And Unit Root Cases, Katsumi Shimotsu, Peter C.B. Phillips
Local Whittle Estimation In Nonstationary And Unit Root Cases, Katsumi Shimotsu, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Asymptotic properties of the local Whittle estimator in the nonstationary case (d > 1/2) are explored. For 1/2 < d < 1, the estimator is shown to be consistent, and its limit distribution and the rate of convergence depend on the value of d . For d = 1, the limit distribution is mixed normal. For d > 1 and when the process has a linear trend, the estimator is shown to be inconsistent and to converge in probability to unity.
Pooled Log Periodogram Regression, Katsumi Shimotsu, Peter C.B. Phillips
Pooled Log Periodogram Regression, Katsumi Shimotsu, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Estimation of the memory parameter in time series with long range dependence is considered. A pooled log periodogram regression estimator is proposed that utilizes a set of mL periodogram ordinates with L approaching infinity rather than m ordinates used in the conventional log periodogram estimator. Consistency and asymptotic normality of the pooled regression estimator are established. The pooled estimator is shown to have smaller variance but larger bias than the conventional log periodogram estimator. Finite sample performance is assessed in simulations, and the methods are illustrated in an empirical application with inflation and stock returns.
Discrete Fourier Transforms Of Fractional Processes, Peter C.B. Phillips
Discrete Fourier Transforms Of Fractional Processes, Peter C.B. Phillips
Cowles Foundation Discussion Papers
Discrete Fourier transforms (dft’s) of fractional processes are studied and an exact representation of the dft is given in terms of the component data. The new representation gives the frequency domain form of the model for a fractional process, and is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d > 1/2. Various asymptotic approximations are suggested. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested.
Second Order Approximation In A Linear Regression With Heteroskedasticity For Unknown Form, Oliver B. Linton
Second Order Approximation In A Linear Regression With Heteroskedasticity For Unknown Form, Oliver B. Linton
Cowles Foundation Discussion Papers
We develop stochastic expansions with remainder o P ( n –2µ ), where 0 < µ < 1/2, for a standardised semiparametric GLS estimator, a standard error, and a studentized statistic, in the linear regression model with heteroskedasticity of unknown form. We calculate the second moments of the truncated expansion, and use these approximations to compare two competing estimators and to define a method of bandwidth choice.
Second Order Approximation In The Partially Linear Regression Model, Oliver B. Linton
Second Order Approximation In The Partially Linear Regression Model, Oliver B. Linton
Cowles Foundation Discussion Papers
We examine the second order properties of various quantities of interest in the partially linear regression model. We obtain a stochastic expansion with remainder o P ( n -2µ ), where µ < 1/2, for the standardized semiparametric least squares estimator, a standard error estimator, and a studentized statistic. We use the second order expansions to correct the standard error estimates for second order effects, and to define a method of bandwidth choice. A Monte Carlo experiment provides favorable evidence on our method of bandwidth choice.
An Introduction To Econometric Random Variables, Donald W.K. Andrews
An Introduction To Econometric Random Variables, Donald W.K. Andrews
Cowles Foundation Discussion Papers
This paper discusses some uses econometrics of functional limit theory for dependent random variables. Attention is focused on empirical process-type results rather than partial sum results that are prevalent in unit root econometrics. Examples considered include nonstandard parametric hypotheses tests and semiparametric estimation. The application of bracketing functional limit results is discussed in some detail.
Estimating Long Run Economic Equilibria, Peter C.B. Phillips, Mico Loretan
Estimating Long Run Economic Equilibria, Peter C.B. Phillips, Mico Loretan
Cowles Foundation Discussion Papers
Our subject is econometric estimation and inference concerning long-run economic equilibria in models with stochastic trends. Our interest is focused on single equation specifications such as those employed in the Error Correction Model (ECM) methodology of David Hendry (1987, 1989 inter alia) and the semiparametric modified least squares method of Phillips and Hansen (1989). We start by reviewing the prescriptions for empirical time series research that are presently available. We argue that the diversity of choices is confusing to practitioners and obscures the fact that statistical theory is clear about optimal inference procedures. Part of the difficulty arises from the …
Asymptotics For Semiparametric Econometric Models: I. Estimation, Donald W.K. Andrews
Asymptotics For Semiparametric Econometric Models: I. Estimation, Donald W.K. Andrews
Cowles Foundation Discussion Papers
This paper provides a general framework for proving the square root of T consistency and asymptotic normality of a wide variety of semiparametric estimators. The results apply in time series and cross-sectional modeling contexts. The class of estimators considered consists of estimators that can be defined as the solution to a minimization problem based on a criterion function that may depend on a preliminary infinite dimensional nuisance parameter estimator. The criterion function need not be differentiable. The method of proof exploits results concerning the stochastic equicontinuity or weak convergence of normalized sums of stochastic processes. This paper also considers tests …
Semiparametric Estimation Of Monotonic And Concave Utility Functions: The Discrete Choice Case, Rosa L. Matzkin
Semiparametric Estimation Of Monotonic And Concave Utility Functions: The Discrete Choice Case, Rosa L. Matzkin
Cowles Foundation Discussion Papers
This paper develops a semiparametric method for estimating the nonrandom part V ( ) of a random utility function U ( v ,ω) – V ( v ) + e (ω) from data on discrete choice behavior. Here v and ω are, respectively, vectors of observable and unobservable attributes of an alternative, and e(ω) is the random part of the utility for that alternative. The method is semiparametric because it assumes that the distribution of the random parts is know up to a finite-dimensional parameter θ, while not requiring specification of a parametric form for V ( ). The nonstochastic …