Journal Article Multivariate Stochastic Variance Models Get access Andrew Harvey, Harvey London School of Economics Search for other works by this author on: Oxford Academic Google Scholar Esther Ruiz, Ruiz University Carlos III de Madrid Neil Shephard Nuffield College, The Review Economic Studies, Volume 61, Issue 2, April 1994, Pages 247–264, https://doi.org/10.2307/2297980 Published: 01 1994 history Received: May 1992 Accepted: November 1993

Abstract The stylized facts of macroeconomic time series can be presented by fitting structural models. Within this framework, we analyse the consequences widely used detrending technique popularised Hodrick and Prescott (1980). It is shown that mechanical based on Hodrick–Prescott filter lead investigators to report spurious cyclical behaviour, point illustrated with empirical examples. Structural time‐series models also allow deal explicitly seasonal irregular movements may distort...

This new edition of A.C. Harvey's clearly written, upper-level text has been revised and several sections have completely rewritten. There is material on a number topics, including unit roots, ARCH, cointegration.The Econometric Analysis Time Series focuses the statistical aspects model building, with an emphasis providing understanding main ideas concepts in econometrics rather than presenting series rigorous proofs. It explores way which recent advances time analysis affected development...

Two structural time series models for annual observations are constructed in terms of trend, cycle, and irregular components. The then estimated via the Kalman filter using data on five U.S. macroeconomic series. results provide some interesting insights into dynamic structure series, particularly with respect to cyclical behavior. At same time, they illustrate development a model selection strategy models.

A stochastic volatility model may be estimated by a quasi-maximum likelihood procedure transforming to linear state-space form. The method is extended handle correlation between the two disturbances in and applied data on stock returns

The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided principal means analyzing, modeling and monitoring such changes. Taking into account that typically exhibit heavy tails - is, extreme values can occur from to Andrew Harvey's new book shows how a small but radical change in way GARCH are formulated leads resolution many theoretical problems inherent statistical theory....

Monthly data on road casualties in Great Britain are analyzed order to assess the effect casualty rates of seat belt law introduced January 31, 1983. Such analysis is known technically as intervention analysis. The form that used this paper based upon structural time series modelling and differs significant respects from standard ARIMA modelling. relative merits two approaches compared. Structural techniques estimate changes for various categories users following introduction law. We first...

Abstract Two related problems are considered. The first concerns the maximum likelihood estimation of parameters in an ARIMA model when some observations missing or subject to temporal aggregation. second observations. Both can be solved by setting up state space form and applying Kalman filter. Key Words: Autoregressive-integrated-moving average processesKalman filterMaximum estimationMissing observationsSmoothingTemporal aggregation

A class of model-based filters for extracting trends and cycles in economic time series is presented. These lowpass bandpass are derived a mutually consistent manner as the joint solution to signal extraction problem an unobserved-components model. The resulting computed finite samples using Kalman filter associated smoother. form which generalization Butterworth filters, widely used engineering. They very flexible have important property allowing relatively smooth be extracted from series....

The basic structural model is a univariate time series consisting of slowly changing trend component, seasonal and random irregular component. It part class models that have number advantages over the ARIMA adopted by Box Jenkins (1976). This article reports results an exercise in which was estimated for six U.K. macroeconomic forecasting performance compared with previously fitted Prothero Wallis

The paper examines various tests for assessing whether a time series model requires slope component. We first consider the simple t-test on mean of differences and show that it achieves high power against alternative hypothesis stochastic nonstationary also purely deterministic slope. test may be modified, parametrically or nonparametrically, to deal with serial correlation. Using both local limiting arguments finite-sample Monte Carlo results, we compare nonparametric Vogelsang (1998,...

Abstract A large number of statistical forecasting procedures for univariate time series have been proposed in the literature. These range from simple methods, such as exponentially weighted moving average, to more complex Box–Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper sets out show relationship between these various by adopting a framework which model is viewed terms trend, seasonal irregular components. The then extended cover models with explanatory...

Abstract A method for modeling a changing periodic pattern is developed. The use of time-varying splines enables this to be done relatively parsimoniously. applied in model used forecast hourly electricity demand, with the movements being intradaily or intraweekly. full contains other components, including temperature response, which also modeled using splines. Key Words: Cubic splinesForecastingKalman filterLoad curveNonlinear regressionSeasonalityStructural time series

Time series sometimes consist of count data in which the number events occurring a given time interval is recorded. Such are necessarily nonnegative integers, and an assumption Poisson or negative binomial distribution often appropriate. This article sets ups model level process generating observations changes over time. A recursion analogous to Kalman filter used construct likelihood function make predictions future observations. Qualitative variables, based on multinomial distribution, may...