LEADER 03091nam 2200457 450 001 9910467479803321 005 20200520144314.0 010 $a1-119-32078-X 010 $a1-119-32072-0 035 $a(CKB)4100000007447498 035 $a(MiAaPQ)EBC5630247 035 $a(CaSebORM)9781119320760 035 $a(Au-PeEL)EBL5630247 035 $a(OCoLC)1082202981 035 $a(EXLCZ)994100000007447498 100 $a20190129d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aForecasting with the Theta method $etheory and applications /$fKostas I. Nikolopoulos, Dimitrios D. Thomakos 205 $a1st edition 210 1$aHoboken, NJ :$cWiley,$d2019. 215 $a1 online resource (187 pages) 311 $a1-119-32076-3 320 $aIncludes bibliographical references and index. 330 $aThe first book to be published on the Theta method, outlining under what conditions the method outperforms other forecasting methods This book is the first to detail the Theta method of forecasting ? one of the most difficult-to-beat forecasting benchmarks, which topped the biggest forecasting competition in the world in 2000: the M3 competition. Written by two of the leading experts in the forecasting field, it illuminates the exact replication of the method and under what conditions the method outperforms other forecasting methods. Recent developments such as multivariate models are also included, as are a series of practical applications in finance, economics, and healthcare. The book also offers practical tools in MS Excel and guidance, as well as provisional access, for the use of R source code and respective packages. Forecasting with the Theta Method: Theory and Applications includes three main parts. The first part, titled Theory, Methods, Models & Applications details the new theory about the method. The second part, Applications & Performance in Forecasting Competitions, describes empirical results and simulations on the method. The last part roadmaps future research and also include contributions from another leading scholar of the method ? Dr. Fotios Petropoulos. First ever book to be published on the Theta Method Explores new theory and exact conditions under which methods would outperform most forecasting benchmarks Clearly written with practical applications Employs R ? open source code with all included implementations Forecasting with the Theta Method: Theory and Applications is a valuable tool for both academics and practitioners involved in forecasting and respective software development. 606 $aBusiness forecasting 608 $aElectronic books. 615 0$aBusiness forecasting. 676 $a658.40355 700 $aNikolopoulos$b Kostas I.$0867425 702 $aThomakos$b Dimitrios D. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910467479803321 996 $aForecasting with the Theta method$91936140 997 $aUNINA LEADER 02976nam 22004935 450 001 9910255030003321 005 20240628121258.0 010 $a9781137313034 010 $a113731303X 024 7 $a10.1057/978-1-137-31303-4 035 $a(CKB)3710000001363102 035 $a(DE-He213)978-1-137-31303-4 035 $a(MiAaPQ)EBC4856765 035 $a(Perlego)3505549 035 $a(EXLCZ)993710000001363102 100 $a20170509d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultivariate Modelling of Non-Stationary Economic Time Series /$fby John Hunter, Simon P. Burke, Alessandra Canepa 205 $a2nd ed. 2017. 210 1$aLondon :$cPalgrave Macmillan UK :$cImprint: Palgrave Macmillan,$d2017. 215 $a1 online resource (XIII, 502 p.) 225 1 $aPalgrave Texts in Econometrics,$x2662-6608 311 08$a9780230243309 311 08$a0230243304 320 $aIncludes bibliographical references and index. 327 $aChapter 1. Introduction: Time Series, Common Trends and Equilibrium -- Chapter 2. Multivariate Time Series -- Chapter 3. Cointegration -- Chapter 4. Testing for Cointegration: Under Standard and Non-Standard Conditions -- Chapter 5. Structure and Evaluation -- Chapter 6. Testing in VECMs with Small Sample -- Chapter 7. Heteroscedasticity and Multivariate Volatility -- Chapter 8. Models with Alternative Orders of Integration -- Chapter 9. The Structural Analysis of Time Series. 330 $aThis book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists. 410 0$aPalgrave Texts in Econometrics,$x2662-6608 606 $aEconometrics 606 $aEconometrics 615 0$aEconometrics. 615 14$aEconometrics. 676 $a330.015195 700 $aHunter$b John$4aut$4http://id.loc.gov/vocabulary/relators/aut$0424730 702 $aBurke$b Simon P$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aCanepa$b Alessandra$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910255030003321 996 $aMultivariate Modelling of Non-Stationary Economic Time Series$91942939 997 $aUNINA