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Regression and time series model selection in small samples
SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The correction is of particular use when the sample size is small,
Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion
Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each
The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series
We establish some asymptotic properties of a log‐periodogram regression estimator for the memory parameter of a long‐memory time series. We consider the estimator originally proposed by Geweke and
Predictive Regressions: A Reduced-Bias Estimation Method
Standard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series
ESTIMATION OF THE MEMORY PARAMETER FOR NONSTATIONARY OR NONINVERTIBLE FRACTIONALLY INTEGRATED PROCESSES
. We consider the asymptotic characteristics of the periodogram ordinates of a fractionally integrated process having memory parameter d≥ 0.5, for which the process is nonstationary, or d≤ -.5, for
ON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELS
We consider semiparametric estimation of the memory parameter in a long memory stochastic volatility model. We study the estimator based on a log periodogram regression as originally proposed by
Multiple-Predictor Regressions: Hypothesis Testing
We propose a new hypothesis-testing method for multipredictor regressions in small samples, where the dependent variable is regressed on lagged variables that are autoregressive. The new test is
A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION
Abstract. We develop a small-sample criterion (AICC) for the selection of the order of vector autoregressive models. AICC is an approximately unbiased estimator of the expected Kullback-Leibler
Estimating Long Memory in Volatility
We consider semiparametric estimation of the memory parameter in a model that includes as special cases both long-memory stochastic volatility and fractionally integrated exponential GARCH (FIEGARCH)
ASYMPTOTICS FOR THE LOW‐FREQUENCY ORDINATES OF THE PERIODOGRAM OF A LONG‐MEMORY TIME SERIES
. We consider the asymptotic distribution of the normalized periodogram ordinates I(ωj)/f(ωj) (j= 1,2,…) of a general long-memory time series. Here, I(ω;) is the periodogram based on a sample size n,
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