The results are comparable to previous work, with several valuable improvements, including interpretation, computational efficiency, and reduced degrees-of-freedom used.Using the Panel Data Approach (PDA) of Hsiao et al. We apply high-pass filters to the predictors and response of the GAM. This points to spectral filtering as a possible alternative technique, where the smoothers are replaced by high-pass filters. From a spectral point of view, it is natural to think of smooth, long-term variations as low-frequency components. The standard model uses natural-spline-based smoothers to account for long-term variations in the mortality. The Air Health Index seeks to model the national risk due to short-term effects of air pollution on mortality. Towards Developing an Air Health Index using Spectral Decomposition of GAMs The proposed adaptive LASSO shows promising results for modelling stationary AR processes. The earlier papers ignore the autocorrelation information inherent in an AR process. We apply the adaptive LASSO to autoregressive time series modelling by formulating adaptive weights as functions of the Yule-Walker estimators and the partial autocorrelations of an AR process. The LASSO (Tibshirani, 1996) and the adaptive LASSO (Zou, 2006) are widely used in regression analysis for simultaneous variable selection and parameter estimation. Variable Selection and Estimation in AR Modelling via Adaptive LASSO Procedure ZI ZHEN LIU, University of Western Ontario In this talk we focus on practical aspects of detecting trends of a general form in time series with an ARCH/GARCH structure and discuss how it could change our interpretation of temporal dynamics in social, medical and environmental studies. ![]() VYACHESLAV LYUBCHICH, University of WaterlooĪRCH/GARCH Models and Trends: From Finance to Medicine, Weather and Further Īlthough conditional heteroscedasticity in time series was originally attributed only to financial processes, similar ARCH/GARCH effects are now detected in a variety of other applications, from wind speed prediction and air/water quality analysis to heart rate variability and patent volatility. The Markov methods result obtained is compared with established results from literature. This presentation studies the pure GARCH processes under a general Markov model framework and develops the asymptotic theory of the QML estimator from a set of Markov tools. Thus finding weak conditions that ensure such asymptotic properties has attracted considerable attention during the past decade. To estimate GARCH models, quasi-maximum likelihood (QML) estimation is often utilized as it produces a consistent estimator with limiting Gaussian distribution when certain parametric constraints are imposed. Generalized autoregressive conditional heteroscedastic (GARCH) processes are important tools in financial econometrics. General Markov Tools with Application to GARCH Models WEIWEI LIU, University of Western Ontario Moreover, I discuss its practical implementation and demonstrate its effectiveness. In this presentation, I propose a novel approach based on the Monte Carlo expectation-maximization algorithm to calculate the maximum likelihood estimator of the MS-GARCH model. ![]() ![]() ![]() This led some authors to propose estimation methods that do not depend on the likelihood while others suggested estimating by maximum likelihood modified versions of the model. Estimating these path dependent models is a challenging task because the exact computation of the likelihood is infeasible in practice. Markov-switching generalized autoregressive conditional heteroscedasticity (MS-GARCH) models offer rich dynamics to model financial data. MACIEJ AUGUSTYNIAK, Université de MontréalĮstimation of the Markov-switching GARCH Model by a Monte Carlo EM Algorithm Chair: Yulia Gel (University of Waterloo)
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