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Full-Text Articles in Social and Behavioral Sciences
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
Kernel-based estimators are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals may not have correct coverage, even if the estimator is unbiased. This paper proposes a simple adjustment that gives correct coverage in such situations: replace the Normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained. We tabulate these critical values and quantify the loss in coverage for conventional confidence intervals. For a …
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
>Kernel-based estimators are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals may not have correct coverage, even if the estimator is unbiased. This paper proposes a simple adjustment that gives correct coverage in such situations: replace the normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained. We tabulate these critical values and quantify the loss in coverage for conventional confidence intervals. For a …
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
Kernel-based estimators are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals may not have correct coverage, even if the estimator is unbiased. This paper proposes a simple adjustment that gives correct coverage in such situations: replace the Normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained. We tabulate these critical values and quantify the loss in coverage for conventional confidence intervals. For a …
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
A Simple Adjustment For Bandwidth Snooping, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
Kernel-based estimators such as local polynomial estimators in regression discontinuity designs are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals may not have correct coverage, even if the estimator is unbiased. This paper proposes a simple adjustment that gives correct coverage in such situations: replace the normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained. We tabulate these critical values and quantify the …
Functional Coefficient Nonstationary Regression, Jiti Gao, Peter C.B. Phillips
Functional Coefficient Nonstationary Regression, Jiti Gao, Peter C.B. Phillips
Cowles Foundation Discussion Papers
This paper studies a general class of nonlinear varying coefficient time series models with possible nonstationarity in both the regressors and the varying coffiecient components. The model accommodates a cointegrating structure and allows for endogeneity with contemporaneous correlation among the regressors, the varying coefficient drivers, and the residuals. This framework allows for a mixture of stationary and non-stationary data and is well suited to a variety of models that are commonly used in applied econometric work. Nonparametric and semiparametric estimation methods are proposed to estimate the varying coefficient functions. The analytical findings reveal some important differences, including convergence rates, that …
Fully Nonparametric Estimation Of Scalar Diffusion Models, Federico M. Bandi, Peter C.B. Phillips
Fully Nonparametric Estimation Of Scalar Diffusion Models, Federico M. Bandi, Peter C.B. Phillips
Cowles Foundation Discussion Papers
We propose a functional estimation procedure for homogeneous stochastic differential equations based on a discrete sample of observations and with minimal requirements on the data generating process. We show how to identify the drift and diffusion function in situations where one or the other function is considered a nuisance parameter. The asymptotic behavior of the estimators is examined as the observation frequency increases and as the time span lengthens (that is, we implement both infill and long span asymptotics). We prove consistency and convergence to mixtures of normal laws, where the mixing variates depend on the chronological local time of …
Nonparametric Estimation Of A Multifactor Heath-Jarrow-Morton Model: An Integrated Approach, Andrew Jeffrey, Oliver B. Linton, Thong Nguyen, Peter C.B. Phillips
Nonparametric Estimation Of A Multifactor Heath-Jarrow-Morton Model: An Integrated Approach, Andrew Jeffrey, Oliver B. Linton, Thong Nguyen, Peter C.B. Phillips
Cowles Foundation Discussion Papers
We develop a nonparametric estimator for the volatility structure of the zero coupon yield curve in the Heath, Jarrow-Morton framework. The estimator incorporates cross-sectional restrictions along the maturity dimension, and also allows for measurement errors, which arise from the estimation of the yield curve from noisy data. The estimates are implemented with daily CRSP bond data.
Additive Interactive Regression Models: Circumvention Of The Curse Of Dimensionality, Donald W.K. Andrews, Yoon-Jae Whang
Additive Interactive Regression Models: Circumvention Of The Curse Of Dimensionality, Donald W.K. Andrews, Yoon-Jae Whang
Cowles Foundation Discussion Papers
This paper considers series estimators of additive interactive regression (AIR) models. AIR models are nonparametric regression models that generalize additive regression models by allowing interactions between different regressor variables. They place more restrictions on the regression function, however, than do fully nonparametric regression models. By doing so, they attempt to circumvent the curse of dimensionality that afflicts the estimation of fully nonparametric regression models. In this paper, we present a finite sample bound and asymptotic rate of convergence results for the mean average squared error of series estimators that show the AIR models do circumvent the curse of dimensionality. 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 …