Andrew Harvey
University of Cambridge, Economics, Faculty Member
- Emeritus Professor of Econometrics, University of Cambridge, Author of 4 books and over 100 articles on time series and econometrics.edit
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Quantiles provide a comprehensive description of the properties of a variable, and tracking changes in quantiles over time using signal extraction methods can be informative. It is shown here how departures from strict stationarity can be... more
Quantiles provide a comprehensive description of the properties of a variable, and tracking changes in quantiles over time using signal extraction methods can be informative. It is shown here how departures from strict stationarity can be detected using stationarity tests based on weighted quantile indicators. Corresponding tests based on expectiles are also proposed; these might be expected to be more powerful for distributions that are not heavy-tailed. Tests for changing dispersion and asymmetry may be based on contrasts between particular quantiles or expectiles. An overall test of the null hypothesis of strict stationarity can be constructed using the indicators from a range of quantiles. Residuals from fitting a time-varying level or trend may be used to construct tests for relative time invariance. Empirical examples, using stock returns and US inflation, demonstrate the practical value of the tests.
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Page 1. Journal of Business & Economic Statistics, October 1992, Vol. 10, No. 4 Diagnostic Checking of Unobserved-Components Time Series Models Andrew C. Harvey and Siem Jan Koopman Department of Statistics, London ...
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Page 1. Estimating Missing Observations in Economic Time Series AC HARVEY and RG PIERSE* Two related problems are considered. The first concerns the maximum likelihood estimation of the parameters in an ARIMA model ...
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Many series are subject to data irregularities such as missing values, outliers, structural breaks and irregular spacing. Data can also be messy, and hence difficult to handle by standard procedures, when they are intrinsically... more
Many series are subject to data irregularities such as missing values, outliers, structural breaks and irregular spacing. Data can also be messy, and hence difficult to handle by standard procedures, when they are intrinsically non-Gaussian or contain complicated periodic patterns because they are observed on an hourly or weekly basis. This paper presents a unified approach to the analysis of
Research Interests: Time Series, Statistical Analysis, Kalman Filter, Data Structure, Linear Model, and 15 moreSplines, State Space, ARIMA Models, Importance Sampling, Outliers, Moving average, Data Aggregation, Missing Values, Continuous Time Signals, State Space Methods, Time Series Model, Arima Model, Simulation Technique, Continuous time, and Structural Break
Page 1. A Simple Test for Serial Correlation in Regression Analysis GDA PHILLIPS and AC HARVEY* An exact test for serial correlation in regression models is proposed based on the fact that under classical assumptions and ...
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ABSTRACT Lagrange multiplier tests against nonstationary unobserved components such as stochastic trends and seasonals are based on statistics which, under the null hypothesis, have asymptotic distributions belonging to the class of... more
ABSTRACT Lagrange multiplier tests against nonstationary unobserved components such as stochastic trends and seasonals are based on statistics which, under the null hypothesis, have asymptotic distributions belonging to the class of generalized Cramér-von Mises distributions. Conversely, unit root tests can be formulated, again using the Lagrange multiplier principle, so as to yield test statistics which also have Cramér-von Mises distributions under the null hypothesis. These ideas may be extended to multivariate models and to models with structural breaks thereby providing a simple unified approach to testing in nonstationary time series.
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Page 1. Applied Economics, 1991, 23, 1077-1086 Inter-fuel substitution, technical change and the demand for energy in the UK economy ANDREW C . HARVEY*and PABLO MARSHALLt *London School of Economics Houghton ...
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(see Hendricks, Koenker, and Poirier 1979). But it is im-portant to allow such splines to evolve over time. The intra-daily pattern may change over a period of several years due to new technology. It certainly changes within the year, as... more
(see Hendricks, Koenker, and Poirier 1979). But it is im-portant to allow such splines to evolve over time. The intra-daily pattern may change over a period of several years due to new technology. It certainly changes within the year, as can be seen in Figure 1, which shows ...
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... STAMP stands for Structural Time series Analyser, Modeller and Predictor ... variance matrices, higher order multivariate components, missing observations allowed, forecasting, exact likelihood computation, automatic outlier and... more
... STAMP stands for Structural Time series Analyser, Modeller and Predictor ... variance matrices, higher order multivariate components, missing observations allowed, forecasting, exact likelihood computation, automatic outlier and break detection, fixing parameters is made easy. ...
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Abstract We examine the properties of a multivariate Dickey-Fuller t-statistic designed to test for a unit root in a panel while taking account of cross-correlations. The asymptotic distribution is presented and critical values provided.... more
Abstract We examine the properties of a multivariate Dickey-Fuller t-statistic designed to test for a unit root in a panel while taking account of cross-correlations. The asymptotic distribution is presented and critical values provided. When intercepts are present, a ...
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This paper, prepared for the Handbook of Statistics, vol.14, Statistical Methods in Finance, surveys the subject of Stochastic Volatility. The following subjects are covered : volatility in financial markets (instantaneous volatility of... more
This paper, prepared for the Handbook of Statistics, vol.14, Statistical Methods in Finance, surveys the subject of Stochastic Volatility. The following subjects are covered : volatility in financial markets (instantaneous volatility of asset returns, implied volatilities in option prices and related stylized facts), statistical modelling in discrete and continuous time and finally statistical inference ( methods of moments, Quasi-Maximum-Likelihood, Likelihood
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The implied signal extraction filters in unobserved components models depend on key signal-noise ratios. This paper examines how these ratios change with the observation interval. The analysis is based on continuous time models and is... more
The implied signal extraction filters in unobserved components models depend on key signal-noise ratios. This paper examines how these ratios change with the observation interval. The analysis is based on continuous time models and is carried out for both stocks and flows. As a by-product, a connection is established between continuous time flow models and the canonical decomposition. The implications of using the Hodrick-Prescott filter to extract cycles at annual and monthly frequencies are discussed. Many of the arguments used in the literature to set the smoothing constant are shown to be flawed. The analysis suggests that a model-based approach is the best way to proceed. A model formulated in continuous time, or in discrete time at a fine time interval, automatically adapts to any observation interval if it is set up in state space form. Concerns about the change in the shape of the filter and the way in which the signal-noise ratio adapts are then no longer an issue