Journal ArticleDOI
Time series models : in econometrics, finance and other fields
Abstract:
Statistical Aspects of ARCH and Scholastic Volatility Likelihood-Based Inference for Cointegration of Some Non-Stationary Time Series Forecasting in Macroeconomics Longitudinal Panel Data: An Overview of Current Methodologyread more
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Journal ArticleDOI
Generalized R-estimators under conditional heteroscedasticity
TL;DR: In this article, rank estimators of the parameters associated with the conditional mean function of an autoregressive model are defined and their asymptotic distributions derived using a uniform approximation of a randomly weighted empirical process by a perturbed empirical process.
Book ChapterDOI
On a Simple Two-Stage Closed-form Estimator for a Stochastic Volatility in a General Linear Regression
Jean-Marie Dufour,Pascale Valéry +1 more
TL;DR: In this article, the estimation of volatility parameters in the context of a linear regression where the disturbances follow a stochastic volatility (SV) model of order one with Gaussian log-volatility is considered.
Dissertation
Essays on Monte Carlo Methods for State Space Models
TL;DR: In this article, the authors present new Monte Carlo methods which can solve complex statistical and computational problems that arise in the analysis of nonlinear non-Gaussian state space models and develop new econometric models that contribute to the understanding of the dynamic nature of economic risks.
Book ChapterDOI
Generalized state-space models for modeling nonstationary EEG time-series
A. Galka,K.K.F. Wong,T. Ozaki +2 more
TL;DR: A comprehensive framework for decomposing nonstationary time-series into a set of constituent processes based on autoregressive moving-average (ARMA) modeling and on state-space modeling is discussed.
Book ChapterDOI
Chapter 12 Modeling Foreign Exchange Rates with Jumps
John M. Maheu,Thomas H. McCurdy +1 more
TL;DR: In this article, the authors proposed a new discrete-time model of returns in which jumps capture persistence in the conditional variance and higher-order moments, and used realized volatility to assess out-of-sample variance forecasts.