Author: Whitney K. NeweyPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Consistency of Quasi Maximum Likelihood Estimators for Models with Conditional Heteroscedasticity
Author: Whitney K. NeweyConsistency of Quasi-Maximum Likelihood Estimators for Models with Conditional Heteroskedasticity
Author: Whitney K. NeweyPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Virtually all empirical studies that assume a time-varying conditional variance use a quasi-maximum likelihood estimator (QMLE). If the density from which the likelihood is constructed is assumed to be Gaussian, the QMLE is known to be consistent under correct specification of both the conditional mean and conditional variance. We show that if both the assumed density and the true density are symmetric a QMLE remains consistent. If, however, either the assumed density or the true density is asymmetric, a QMLE is generally not consistent. To ensure that a QMLE is consistent under asymmetric densities, we include the conditional standard deviation as a regressor. We calculate the efficiency loss associated with the added regressor if the densities are symmetric and show that for a QMLE of the conditional variance parameters of a GARCH process there is no efficiency loss. Finally, we develop a test of consistency of a QMLE from the significance of the additional regressor.
Maximum Likelihood Estimation of Misspecified Models
Author: T. FombyPublisher: Elsevier
ISBN: 0762310758
Category : Business & Economics
Languages : en
Pages : 266
Book Description
Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
Consistency of Quasi-maximum Likelihood Estimators for the Reduced Regime-switching GARCH Models
Author: Yingfu XieConsistency of Quasi-maximum Likelihood Estimators for the Regime-switching GARCH Models
Author: Yingfu XieOn the Weak Consistency of the Quasi-maximum Likelihood Estimator in Var Models with Bekkk-Garch (1,q) Errors
Author: L. BauwensQuasi-Maximum Likelihood Estimation for Conditional Expectiles
Author: Collin PhilippsPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We characterize the quasi-likelihood functions that may elicit expectiles and find that the family has a unique representation under standard conditions for linear regression. The only distribution that elicits expectiles as its quasi-maximum likelihood estimator under general conditions is an asymmetric normal distribution. Next, we analyze the quasi maximum likelihood estimator and give conditions for consistency, asymptotic normality, and efficiency. The estimator is unique up to the choice of weights on individual observations and nests the usual GLS estimator. We give the asymptotic MVUE and a uniform Cramer-Rao theorem for expectile regression.
Estimation in Conditionally Heteroscedastic Time Series Models
Author: Daniel StraumannPublisher: Springer Science & Business Media
ISBN: 3540269789
Category : Business & Economics
Languages : en
Pages : 239
Book Description
In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.
Macroeconometrics
Author: Kevin D. HooverPublisher: Springer Science & Business Media
ISBN: 9780792395898
Category : Business & Economics
Languages : en
Pages : 602
Book Description
Each chapter of Macroeconometrics is written by respected econometricians in order to provide useful information and perspectives for those who wish to apply econometrics in macroeconomics. The chapters are all written with clear methodological perspectives, making the virtues and limitations of particular econometric approaches accessible to a general readership familiar with applied macroeconomics. The real tensions in macroeconometrics are revealed by the critical comments from different econometricians, having an alternative perspective, which follow each chapter.
Poisson QMLE of Count Time Series Models
Author: Ali AhmadPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Regularity conditions are given for the consistency of the Poisson quasi-maximum likelihood estimator of the conditional mean parameter of a count time series model. The asymptotic distribution of the estimator is studied when the parameter belongs to the interior of the parameter space and when it lies at the boundary. Tests for the significance of the parameters and for constant conditional mean are deduced. Applications to specific integer-valued autoregressive (INAR) and integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models are considered. Numerical illustrations, Monte Carlo simulations and real data series are provided.