The Experts below are selected from a list of 18045 Experts worldwide ranked by ideXlab platform
Halbert White - One of the best experts on this subject based on the ideXlab platform.
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Asymptotic Distribution Theory for nonparametric entropy measures of serial dependence
Econometrica, 2005Co-Authors: Yongmiao Hong, Halbert WhiteAbstract:Entropy is a classical statistical concept with appealing properties. Establishing Asymptotic Distribution Theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an Asymptotic Theory for a class of kernel-based smoothed nonparametric entropy measures of serial dependence in a time-series context. We use this Theory to derive the limiting Distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our Theory to construct a new entropy-based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is Asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach. Copyright The Econometric Society 2005.
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Asymptotic Distribution Theory FOR NONPARAMETRIC ENTROPY MEASURES OF SERIAL DEPENDENCE
Econometrica, 2005Co-Authors: Yongmiao Hong, Halbert WhiteAbstract:Entropy is a classical statistical concept with appealing properties. Establishing Asymptotic Distribution Theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an Asymptotic Theory for a class of kernel-based smoothed nonparametric entropy measures of serial dependence in a time-series context. We use this Theory to derive the limiting Distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our Theory to construct a new entropy-based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is Asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach.
James H Stock - One of the best experts on this subject based on the ideXlab platform.
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instrumental variables regression with weak instruments
Econometrica, 1997Co-Authors: Douglas O Staiger, James H StockAbstract:This paper develops Asymptotic Distribution Theory for instrumental variable regression when the partial correlation between the instruments and a single included endogenous variable is weak, here modeled as local to zero. Asymptotic representations are provided for various instrumental variable statistics, including the two-stage least squares (TSLS) and limited information maximum- likelihood (LIML) estimators and their t-statistics. The Asymptotic Distributions are found to provide good approximations to sampling Distributions with just 20 observations per instrument. Even in large samples, TSLS can be badly biased, but LIML is, in many cases, approximately median unbiased. The Theory suggests concrete quantitative guidelines for applied work. These guidelines help to interpret Angrist and Krueger's (1991) estimates of the returns to education: whereas TSLS estimates with many instruments approach the OLS estimate of 6%, the more reliable LIML and TSLS estimates with fewer instruments fall between 8% and 10%, with a typical confidence interval of (6%, 14%).
Frank Kleibergen - One of the best experts on this subject based on the ideXlab platform.
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likelihood based cointegration analysis in panels of vector error correction models
Journal of Business & Economic Statistics, 2003Co-Authors: Jan Groen, Frank KleibergenAbstract:We propose a likelihood-based framework for cointegration analysis in panels of a fixed number of vector error-correction (VEC) models. We obtain likelihood ratio statistics to test for a common cointegration rank across the individual VEC models with both heterogeneous and homogeneous cointegrating vectors. Their limiting Distributions are a summation of the limiting behavior of Johansen trace statistics. We extend the Asymptotic Distribution Theory to cover the case of an infinite cross-sectional dimension. We apply the framework to a dataset of exchange rates and appropriate monetary fundamentals. We find evidence for the validity of the monetary exchange rate model within a panel of VEC models for three major European countries, whereas the results based on individual VEC models for each of these countries separately are less supportive.
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likelihood based cointegration analysis in panels of vector error correction models
WO Research Memoranda, 2001Co-Authors: Jan Groen, Frank KleibergenAbstract:We propose in this paper a likelihood-based framework for cointegration analysis in panels of a fixed number of vector error correction models. Maximum likelihood estimators of the cointegrating vectors are constructed using iterated Generalized Method of Moments estimators. Using these estimators we construct likelihood ratio statistics to test for a common cointegration rank across the individual vector error correction models, both with heterogeneous and homogeneous cointegrating vectors. The corresponding limiting Distributions are a summation of the limiting behavior of Johansen (1991) trace statistics. We also incorporate both unrestricted and restricted deterministic components which are either homogeneous or heterogeneous, and extend the Asymptotic Distribution Theory to cover the case of an infinite cross-section dimension. The proposed framework is applied on a data set of exchange rates and appropriate monetary fundamentals. The test results show strong evidence for the validity of the monetary exchange rate model within a panel of vector error correction models for three major European countries, whereas the results based on individual vector error correction models for each of these countries separately are less supportive.
Yongmiao Hong - One of the best experts on this subject based on the ideXlab platform.
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Asymptotic Distribution Theory for nonparametric entropy measures of serial dependence
Econometrica, 2005Co-Authors: Yongmiao Hong, Halbert WhiteAbstract:Entropy is a classical statistical concept with appealing properties. Establishing Asymptotic Distribution Theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an Asymptotic Theory for a class of kernel-based smoothed nonparametric entropy measures of serial dependence in a time-series context. We use this Theory to derive the limiting Distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our Theory to construct a new entropy-based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is Asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach. Copyright The Econometric Society 2005.
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Asymptotic Distribution Theory FOR NONPARAMETRIC ENTROPY MEASURES OF SERIAL DEPENDENCE
Econometrica, 2005Co-Authors: Yongmiao Hong, Halbert WhiteAbstract:Entropy is a classical statistical concept with appealing properties. Establishing Asymptotic Distribution Theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an Asymptotic Theory for a class of kernel-based smoothed nonparametric entropy measures of serial dependence in a time-series context. We use this Theory to derive the limiting Distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our Theory to construct a new entropy-based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is Asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach.
Douglas O Staiger - One of the best experts on this subject based on the ideXlab platform.
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instrumental variables regression with weak instruments
Econometrica, 1997Co-Authors: Douglas O Staiger, James H StockAbstract:This paper develops Asymptotic Distribution Theory for instrumental variable regression when the partial correlation between the instruments and a single included endogenous variable is weak, here modeled as local to zero. Asymptotic representations are provided for various instrumental variable statistics, including the two-stage least squares (TSLS) and limited information maximum- likelihood (LIML) estimators and their t-statistics. The Asymptotic Distributions are found to provide good approximations to sampling Distributions with just 20 observations per instrument. Even in large samples, TSLS can be badly biased, but LIML is, in many cases, approximately median unbiased. The Theory suggests concrete quantitative guidelines for applied work. These guidelines help to interpret Angrist and Krueger's (1991) estimates of the returns to education: whereas TSLS estimates with many instruments approach the OLS estimate of 6%, the more reliable LIML and TSLS estimates with fewer instruments fall between 8% and 10%, with a typical confidence interval of (6%, 14%).
