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Asymptotic Variance

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Jinyong Hahn – 1st expert on this subject based on the ideXlab platform

  • Asymptotic Variance of semiparametric estimators with generated regressors
    Econometrica, 2013
    Co-Authors: Jinyong Hahn, Geert Ridder

    Abstract:

    We study the Asymptotic distribution of three-step estimators of a finite-dimensional parameter vector where the second step consists of one or more nonparametric regressions on a regressor that is estimated in the first step. The first-step estimator is either parametric or nonparametric. Using Newey’s (1994) path-derivative method, we derive the contribution of the first-step estimator to the influence function. In this derivation, it is important to account for the dual role that the first-step estimator plays in the second-step nonparametric regression, that is, that of conditioning variable and that of argument.

  • a practical Asymptotic Variance estimator for two step semiparametric estimators
    The Review of Economics and Statistics, 2011
    Co-Authors: Daniel A Ackerberg, Xiaohong Chen, Jinyong Hahn

    Abstract:

    The goal of this paper is to develop techniques to simplify semiparametric inference. We do this by deriving a number of numerical equivalence results. These illustrate that in many cases, one can obtain estimates of semiparametric Variances using standard formulas derived in the already-well-known parametric literature. This means that for computational purposes, an empirical researcher can ignore the semiparametric nature of the problem and do all calculations “as if”it were a parametric situation. We hope that this simplicity will promote the use of semiparametric procedures.

Johan Lyhagen – 2nd expert on this subject based on the ideXlab platform

  • Asymptotic properties of spearman s rank correlation for variables with finite support
    PLOS ONE, 2016
    Co-Authors: Petra Ornstein, Johan Lyhagen

    Abstract:

    The Asymptotic Variance and distribution of Spearman’s rank correlation have previously been known only under independence. For variables with finite support, the population version of Spearman’s rank correlation has been derived. Using this result, we show convergence to a normal distribution irrespectively of dependence, and derive the Asymptotic Variance. A small simulation study indicates that the Asymptotic properties are of practical importance.

Marvin K. Nakayama – 3rd expert on this subject based on the ideXlab platform

  • Confidence intervals for quantiles with standardized time series
    2013 Winter Simulations Conference (WSC), 2013
    Co-Authors: James M. Calvin, Marvin K. Nakayama

    Abstract:

    Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (CIs) for the steady-state mean of a stationary process. STS techniques cancel out the Variance constant in the Asymptotic distribution of the centered and scaled estimator, thereby eliminating the need to consistently estimate the Asymptotic Variance to obtain a CI. This is desirable since estimating the Asymptotic Variance in steady-state simulations presents nontrivial challenges. Difficulties also arise in estimating the Asymptotic Variance of a quantile estimator. We show that STS methods can be used to build CIs for a quantile for the case of crude Monte Carlo (i.e., no Variance reduction) with independent and identically distributed outputs. We present numerical results comparing CIs for quantiles using STS to other procedures.