Time Series Models For Business And Economic Forecasting
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationTime Series Models for Business andEconomic ForecastingWith a new author team contributing decades of practical experience, this fully updatedand thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time seriesanalysis. Taking a practical and example-driven approach, this textbook summarizesthe most critical decisions, techniques, and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirelynew set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends,seasonality, aberrant observations, conditional heteroskedasticity and ARCH models,non-linearity and multivariate time series, making this a complete practical guide.A companion website with downloadable datasets, solutions to exercises and lectureslides rounds out the full learning package.Philip Hans Franses is Professor of Applied Econometrics and Professor of Marketing Research at the Erasmus School of Economics.Dick van Dijk is Professor of Financial Econometrics at the Erasmus School ofEconomics.Anne Opschoor has recently completed a PhD at the Erasmus School of Economicsand is an Assistant Professor at the Free University. in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore information in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationTime Series Modelsfor Business andEconomic ForecastingSECOND EDITIONPhilip Hans Franses, Dick van Dijkand Anne Opschoor in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationUniversity Printing House, Cambridge CB2 8BS, United KingdomCambridge University Press is a part of the University of Cambridge.It furthers the University’s mission by disseminating knowledge in the pursuit ofeducation, learning and research at the highest international levels of excellence.www.cambridge.orgInformation on this title: www.cambridge.org/9780521817707C Philip Hans Franses, Dick van Dijk and Anne Opschoor 2014 This publication is in copyright. Subject to statutory exceptionand to the provisions of relevant collective licensing agreements,no reproduction of any part may take place without the writtenpermission of Cambridge University Press.First published 1998Second edition published 2014Reprinted 2015Printed in the United Kingdom by Clays, St Ives plcA catalogue record for this publication is available from the British LibraryISBN 978-0-521-81770-7 HardbackISBN 978-0-521-52091-1 PaperbackAdditional resources for this publication at www.cambridge.org/timeseriesCambridge University Press has no responsibility for the persistence or accuracy ofURLs for external or third-party internet websites referred to in this publication,and does not guarantee that any content on such websites is, or will remain,accurate or appropriate. in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationContentsList of figuresList of tablesPrefacepage viixxi1Introduction and overview12Key features of economic time series8r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rr r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r2.12.22.32.42.52.6345TrendsSeasonalityAberrant observationsConditional heteroskedasticityNon-linearityCommon features91422262729Useful concepts in univariate time series analysis333.13.23.33.43.535r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rAutoregressive moving average modelsAutocorrelation and identificationEstimation and diagnostic measuresModel r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rModeling trendsUnit root testsStationarity testsForecasting94102104Seasonality1105.1 Modeling seasonality112r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rv in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationviContents67895.2 Seasonal unit root tests5.3 Forecasting131Aberrant observations1396.16.26.36.4144124r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rModeling aberrant observationsWhat happens if we neglect outliers?What to do about outliers?Outliers and unit root tests152154160Conditional heteroskedasticity1667.17.27.37.4169r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rModels for conditional heteroskedasticityVarious extensionsSpecification, estimation and .28.38.48.5206r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rRegime-switching modelsEstimationTesting for nonlinearityDiagnostic checkingForecasting212220227232Multivariate time series2409.19.29.39.49.5244r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r r rRepresentationsEmpirical model buildingApplying VAR modelsCointegration: some preliminariesInference on cointegrationBibliographySubject index in this web service Cambridge University Press252256262269284298www.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore .33.4Annual indices of log real GDP per capita in Latin American countries page 9Annual stock of motorcycles in The Netherlands12Quarterly index of US industrial production13Monthly US new passenger car registrations14Quarterly growth rates of US industrial production15Vector-of-quarters representation of quarterly USindustrial production16Changing seasonality in US industrial production17Quarterly UK household final consumption expenditures18Quarterly growth rates of UK household final consumptionexpenditures19Vector-of-quarters representation of quarterly UK household finalconsumption expenditures19Changing seasonality in UK household consumption20Four-weekly advertising expenditures on radio and televisionin The Netherlands21Changing seasonality in television advertising expenditures21Monthly revenue passenger-kilometers flown for European airlines23Annual growth rates of revenue passenger-kilometers flown forEuropean airlines24Monthly growth rates of revenue passenger-kilometers flown forEuropean airlines25Monthly growth rates of revenue passenger-kilometers flown forEuropean airlines25Daily returns on the Dow Jones index26Quarterly US unemployment rate among men of 16 years and over28Monthly log prices of gold and silver31Daily returns of gold and silver31Simulated AR(1) time series38Simulated MA(1) time series43Theoretical autocorrelation function of an AR(2) process49Theoretical autocorrelation function of an AR(2) process50vii in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationviiiList of figures3.53.63.7Theoretical autocorrelation function of an AR(2) processTheoretical autocorrelation function of an AR(2) processEmpirical autocorrelation function of annual differences of logmonthly US industrial production3.8 Typical fit of an AR time series model4.1 Simulated time series from deterministic trend and stochastictrend models4.2 Results of a regression of US industrial production on a constant anda linear deterministic trend4.3 Results of a regression of stock of motorcycles on a quadraticdeterministic trend4.4 Partial sums of residuals for Latin American GDP per capita4.5 Example of a Gompertz curve and a logistic curve4.6 Empirical autocorrelation function for absolute daily gold returns4.7 Point forecasts and 95% interval forecasts from an AR(2) model forUS industrial production4.8 Point forecasts and 95% interval forecasts from an ARI(1,1) modelfor US industrial production5.1 Results of a regression of quarterly UK household consumption on anintercept and a linear deterministic trend5.2 Vector-of-quarters plot of deviations from linear trend of UKhousehold consumption5.3 Simulated quarterly seasonal random walk and transformations5.4 Vector-of-quarters plot of simulated seasonal random walk6.1 Quarterly log industrial production France6.2 Quarterly growth rates of industrial production France6.3 Daily returns on the Dow Jones index6.4 Daily returns on the Dow Jones index, September 1–December 31,19876.5 Example of an additive outlier6.6 Example of an additive outlier6.7 Effect of neglecting a single additive outlier on residuals ofAR(1) model6.8 Example of an innovation outlier6.9 Example of an innovation outlier6.10 Effect of neglecting a single innovation outlier on residualsof AR(1) model6.11 Example of a level shift6.12 Huber weight function in this web service Cambridge University 41141146146147149149150152157www.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationList of figuresix6.13 Quarterly (log) US manufacturers’ new orders for non-defensecapital goods6.14 Results from an AR(3) model for US manufacturers’ new orders fornon-defense capital goods7.1 QQ-plot of daily returns on the Dow Jones index7.2 Daily returns on the Dow Jones index, July 1, 1998–December 31,19987.3 Scatter of daily returns on the Dow Jones index, July 1,1998–December 31, 19987.4 Empirical autocorrelation function of daily returns, squared returns,and absolute returns on the Dow Jones index7.5 News impact curves from the GARCH(1,1), EGARCH(1,1) andTGARCH(1,1) models7.6 Daily MSCI Switzerland returns7.7 Empirical ACF and ACF implied by the GARCH(1,1) model ofsquared daily MSCI Switzerland returns7.8 Conditional standard deviation from GARCH(1,1) model for dailyreturns on the MSCI Switzerland index7.9 Empirical ACF of (squared) residuals for an ARCH(1) andGARCH(1,1) model for daily returns on the MSCI Switzerland index7.10 Conditional standard deviation from GARCH(1,1) andTGARCH(1,1) models for daily returns on the MSCI Switzerlandindex7.11 One-step ahead forecasts of conditional standard deviation fromTGARCH(1,1) models for daily returns on MSCI Switzerland7.12 One-step ahead 95% interval forecasts from TGARCH(1,1) modelsfor daily returns on MSCI Switzerland8.1 Logistic functions8.2 Quarterly seasonally adjusted US unemployment rates8.3 Sequence of Wald statistics for testing threshold nonlinearity in USunemployment rates8.4 Transition function in LSTAR model for quarterly seasonally adjustedUS unemployment rate9.1 Impulse response function with 95% confidence bounds9.2 Simulated cointegrated time series9.3 Monthly white and black pepper price series9.4 Cointegration relation between the logarithm of white and blackpepper prices in this web service Cambridge University 224225226260264274276www.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore 9.19.29.39.49.59.6Trends in real GDP per capita in Latin American countriesTrends in US industrial productionEmpirical (partial) autocorrelation functions for monthlyrevenue-passenger kilometers of European airlinesCritical values for tests to select between deterministic trend andstochastic trend modelsTesting for unit roots: some empirical examplesForecast standard errors for the stock of motorcyclesEmpirical autocorrelation functions of UK consumptionCritical values for HEGY seasonal unit root tests in quarterly timeseriesTesting for seasonal unit roots: some empirical examplesAsymptotic critical values of Dickey-Fuller t-test in the presence oflevel shifts and breaking trends at a known dateAsymptotic critical values of HEGY test statistics in the presence ofseasonal level shifts at known break dateVAR model selection for gold and silver pricesVariance decomposition in VAR(2) model for gold and silver pricesAsymptotic critical values for the Engle and Granger (1987)cointegration methodAsymptotic critical values for the Johansen cointegration methodEmpirical (partial) autocorrelation functions for the cointegrationvariable of white and black pepper pricesAsymptotic critical values for the cointegration test based on aconditional error correction 78x in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationPrefaceThe econometric analysis of economic and business time series is a major field ofresearch and application. The last few decades have witnessed an increasing interestin both theoretical and empirical developments in constructing time series modelsand in their important application in forecasting. This book aims at reviewing severalimportant developments within the context of forecasting business and economic timeseries.A full-blown textbook on all aspects of time series analysis will cover thousandsof pages. For example, the field of unit root analysis has expanded in the last threedecades with such a pace and variation that a book only on this topic would takemore pages than the current book does. This book is therefore not intended to be asurvey of all that is available and that can be done in time series analysis. Obviously,such a selection comes with a cost, that is, the discussion will sometimes not be astheoretically precise as some readers would have liked. Merely, it is our purpose thatthe readers should be able to generate their own forecasts from time series models thatadequately describe the key features of the data, to evaluate these forecasts and to comeup with suggestions for possible modifications if necessary. In some interesting cases,though, we also recommend further reading. To attain this, we make a selection betweenall the possible routes to constructing and evaluating time series models, between allthe possible estimation methods, and between all the various tests that can be used.Basically, our choice is also motivated by the availability of methods in such statisticalpackages as Eviews, while sometimes a little bit of Gauss, R or Matlab programmingis needed. In fact, all empirical results in this book are thus obtained. An additionalmotivation for our choice is given by our own practical experience in forecastingbusiness and economic time series. This experience is also based on supervisingprojects of our econometrics undergraduate students during their internships at banks,investment companies, and consultancy agencies.The second purpose with this book is that the reader will be able to get someunderstanding of novel approaches reported in recent and future issues of, say, theJournal of Time Series Analysis, Journal of Econometrics, Journal of Business andEconomic Statistics, Journal of Forecasting, International Journal of Forecasting, Journal of Applied Econometrics and the Journal of the American Statistical Association.xi in this web service Cambridge University Presswww.cambridge.org
Cambridge University Press978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second EditionPhilip Hans Franses, Dick van Dijk and Anne OpschoorFrontmatterMore informationxiiPrefaceIt is hoped that the reader finds the material in this book helpful to understand whysuch new methods can be useful for forecasting.Although this book amounts to an introduction to the field of time series analysis andforecasting, it is necessary that the reader has knowledge of introductory econometrics.Specifically, regression analysis, matrix algebra and various concepts in estimationshould be included in that knowledge. This book should then be useful to advancedundergraduate students and graduate students in business and economics, but also topractitioners and applied economists who wish to obtain a first, but not too technical,impression of time series forecasting. In fact, most of the material has already beenused in “Time Series Analysis” courses for third year undergraduate students at theEconometric Institute in Rotterdam ever since 1996.The first edition of this book (Franses (1998)) contained material that has nowbeen deleted. Periodic models for seasonal data are not included anymore and also anextensive discussion of common features has been deleted. On the other hand, moredetails on ARCH models and on non-linear models have been included, where wedraw from the material in a 2000 Cambridge University Press textbook by the firsttwo authors. More importantly, this fully revised second edition contains exercises.Answers to selected exercises will be made available on a special website. Theseexercises match with those presented at past exams to our students.This book was written during our affiliation with the Econometric Institute atthe Erasmus University Rotterdam. This Institute is a very stimulating teaching andresearch environment. We wish to express our gratitude to our (then) colleagues TeunKloek, Christiaan Heij, Herman van Dijk, Dennis Fok, Richard Paap, Andre Lucas,Marius Ooms and anonymous reviewers for their kind willingness to comment onsome or all chapters.Philip Hans FransesDick van DijkAnne Opschoor in this web service Cambridge University Presswww.cambridge.org
3.5 Forecasting 66 4 Trends 77 4.1 Modeling trends 79 4.2 Unit root tests 94 4.3 Stationarity tests 102 4.4 Forecasting 104 5 Seasonality 110 5.1 Modeling seasonality 112 v Cambridge University Press 978-0-521-81770-7 - Time Series Models for Business and Economic Forecasting: Second Edition Philip Hans Franses, Dick van Dijk and Anne Opschoor .
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