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67
Econometric model determination
 Econometrica
, 1996
"... The copyright to this Article is held by the Econometric Society. It may be downloaded, printed and reproduced only for educational or research purposes, including use in course packs. No downloading or copying may be done for any commercial purpose without the explicit permission of the Econometric ..."
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Cited by 56 (13 self)
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The copyright to this Article is held by the Econometric Society. It may be downloaded, printed and reproduced only for educational or research purposes, including use in course packs. No downloading or copying may be done for any commercial purpose without the explicit permission of the Econometric Society. For such commercial purposes contact the Office of the Econometric Society (contact information may be found at the website
Optimal Inference in Regression Models with Nearly Integrated Regressors
, 2004
"... This paper considers the problem of conducting inference on the regression coefficient in a bivariate regression model with a highly persistent regressor. Gaussian power envelopes are obtained for a class of testing procedures satisfying a conditionality restriction. In addition, the paper proposes ..."
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Cited by 56 (2 self)
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This paper considers the problem of conducting inference on the regression coefficient in a bivariate regression model with a highly persistent regressor. Gaussian power envelopes are obtained for a class of testing procedures satisfying a conditionality restriction. In addition, the paper proposes feasible testing procedures that attain these Gaussian power envelopes whether or not the innovations of the regression model are normally distributed.
On adaptive estimation in nonstationary ARMA models with GARCH errors
 Ann. Statist
, 2003
"... This paper considers adaptive estimation in nonstationary autoregressive moving average models with the noise sequence satisfying a generalised autoregressive conditional heteroscedastic process. The locally asymptotic quadratic form of the loglikelihood ratio for the model is obtained. It is show ..."
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Cited by 46 (34 self)
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This paper considers adaptive estimation in nonstationary autoregressive moving average models with the noise sequence satisfying a generalised autoregressive conditional heteroscedastic process. The locally asymptotic quadratic form of the loglikelihood ratio for the model is obtained. It is shown that the limit experiment is neither LAN nor LAMN, but is instead LABF. Adaptivity is discussed and it is found that the parameters in the model are generally not adaptively estimable if the density of the rescaled error is asymmetric. For the model with symmetric density of the rescaled error, a new efficiency criterion is established for a class of defined Mνestimators. It is shown that such efficient estimators can be constructed when the density is known. Using the kernel estimator for the score function, adaptive estimators are constructed when the density of the rescaled error is symmetric, and it is shown that the adaptive procedure for the parameters in the conditional mean part uses the full sample without splitting. These estimators are demonstrated to be
Root n Consistent and Optimal Density Estimators for Moving Average Processes
"... The marginal density of a first order moving average process can be written as convolution of two innovation densities. Saavedra and Cao (2000) propose to estimate the marginal density by plugging in kernel density estimators for the innovation densities, based on estimated innovations. They obtain ..."
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Cited by 21 (17 self)
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The marginal density of a first order moving average process can be written as convolution of two innovation densities. Saavedra and Cao (2000) propose to estimate the marginal density by plugging in kernel density estimators for the innovation densities, based on estimated innovations. They obtain that for an appropriate choice of bandwidth the variance of their estimator decreases at the rate 1/n. Their estimator can be interpreted as a specific Ustatistic. We suggest a slightly simpli ed Ustatistic as estimator of the marginal density, prove that it is asymptotically normal at the same rate, and describe the asymptotic variance explicitly. We show that the estimator is asymptotically efficient if no structural assumptions are made on the innovation density. For innovation densities known to have mean zero or to be symmetric, we describe improvements of our estimator which are again asymptotically efficient.
Weighted ResidualBased Density Estimators For Nonlinear Autoregressive Models
"... This paper considers residualbased and randomly weighted kernel estimators for innovation densities of nonlinear autoregressive models. The weights are chosen to make use of the information that the innovations have mean zero. Rates of convergence are obtained in weighted L1norms. These estimators ..."
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Cited by 18 (13 self)
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This paper considers residualbased and randomly weighted kernel estimators for innovation densities of nonlinear autoregressive models. The weights are chosen to make use of the information that the innovations have mean zero. Rates of convergence are obtained in weighted L1norms. These estimators give rise to smoothed and weighted empirical distribution functions and moments. It is shown that the latter are efficient if an efficient estimator for the autoregression parameter is used to construct the residuals.
Estimating the Innovation Distribution in Nonlinear Autoregressive Models
 Department of Mathematics, University of Siegen. http://www.math.unisiegen.de/statistik/wefelmeyer.html
"... The usual estimator for the expectation of a function under the innovation distribution of a nonlinear autoregressive model is the empirical estimator based on estimated innovations. It can be improved by exploiting that the innovation distribution has mean zero. We show that the resulting estimator ..."
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Cited by 17 (17 self)
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The usual estimator for the expectation of a function under the innovation distribution of a nonlinear autoregressive model is the empirical estimator based on estimated innovations. It can be improved by exploiting that the innovation distribution has mean zero. We show that the resulting estimator is efficient if the innovations are estimated with an efficient estimator for the autoregression parameter. Efficiency of this estimator is necessary except when the expectation of the function can be estimated adaptively. Analogous results hold for heteroscedastic models.
Testing the Capital Asset Pricing Model Efficiently Under Elliptical Symmetry: A Semiparametric Approach
, 2001
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Estimation of dynamic models with nonparametric simulated maximum likelihood
, 2007
"... We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated observa ..."
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Cited by 15 (7 self)
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We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated observations, we estimate the unknown density of the model nonparametrically by kernel methods, and then obtain the SMLEs of the model parameters. Our method avoids the issue of nonidentification arising from poor choice of auxiliary models in simulated methods of moments (SMM) or indirect inference. More importantly, our SMLEs achieve higher efficiency under weak regularity conditions. Finally, our method allows for potentially nonstationary processes, including timeinhomogeneous dynamics.
Improving Size and Power in Unit Root Testing
"... A frequent criticism of unit root tests concerns the poor power and size properties that many such tests exhibit. However, during the past decade or so intensive research has been conducted to alleviate these problems and great advances have been made. The present paper provides a selective survey o ..."
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Cited by 14 (1 self)
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A frequent criticism of unit root tests concerns the poor power and size properties that many such tests exhibit. However, during the past decade or so intensive research has been conducted to alleviate these problems and great advances have been made. The present paper provides a selective survey of recent contributions to improve upon both the size and power of unit root tests and, in so doing, the approach of using rigorous statistical optimality criteria in the development of such tests is stressed. In addition to presenting tests where improved size can be achieved by modifying the standard Dickey–Fuller class of tests, the paper presents the theory of optimal testing and the construction of power envelopes for unit root tests under different conditions allowing for serial correlation, deterministic components, assumptions regarding the initial condition, nonGaussian errors, and the use of covariates.
A Complete Class of Tests When the Likelihood is Locally Asymptotically Quadratic
, 1999
"... This paper constructs an asymptotically complete class of tests (i.e. for a test not within this class one can find a test within the class that has better or equal asymptotical powerfunction) for testing one parametric restriction when the likelihoodfunction can asymptotically be approximated by a ..."
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Cited by 13 (0 self)
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This paper constructs an asymptotically complete class of tests (i.e. for a test not within this class one can find a test within the class that has better or equal asymptotical powerfunction) for testing one parametric restriction when the likelihoodfunction can asymptotically be approximated by a quadratic function: The coefficients of this quadratic form may be random variables, so that our results also apply to the problem of unitroot testing.