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Titlebook: New Developments in Time Series Econometrics; Jean-Marie Dufour (Director of the C.R.D.E. (Centr Conference proceedings 1994 Physica-Verla

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書目名稱New Developments in Time Series Econometrics
編輯Jean-Marie Dufour (Director of the C.R.D.E. (Centr
視頻videohttp://file.papertrans.cn/666/665044/665044.mp4
叢書名稱Studies in Empirical Economics
圖書封面Titlebook: New Developments in Time Series Econometrics;  Jean-Marie Dufour (Director of the C.R.D.E. (Centr Conference proceedings 1994 Physica-Verla
描述This book contains eleven articles which provide empirical applications as well as theoretical extensions of some of the most exciting recent developments in time-series econometrics. The papers are grouped around three broad themes: (I) the modeling of multivariate times series; (II) the analysis of structural change; (III) seasonality and fractional integration. Since these themes are closely inter-related, several other topics covered are also worth stressing: vector autoregressive (VAR) models, cointegration and error-correction models, nonparametric methods in time series, and fractionally integrated models. Researchers and students interested in macroeconomic and empirical finance will find in this collection a remarkably representative sample of recent work in this area.
出版日期Conference proceedings 1994
關(guān)鍵詞Econometric; Multivariate Time Series; Multivariate Zeitreihen; VAR-Models; Zeitreihenanalyse; cointegrat
版次1
doihttps://doi.org/10.1007/978-3-642-48742-2
isbn_softcover978-3-642-48744-6
isbn_ebook978-3-642-48742-2Series ISSN 1431-8830 Series E-ISSN 2196-8950
issn_series 1431-8830
copyrightPhysica-Verlag Heidelberg 1994
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Usefulness of Linear Transformations in Multivariate Time-Series Analysisseries. The topics considered include vector ARMA models, principal component analysis, scalar component models, canonical correlation analyses, co-integration, and unit-root tests. We illustrate the methods considered by an example using Taiwan’s interest-rate series and provide critiques of these developments.
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Seasonal Cointegration, Common Seasonals, and Forecasting Seasonal Seriesvaluation of the predictive value of accounting for seasonal cointegration shows that seasonal cointegration may be difficult to exploit to improve predictive accuracy even in cases where seasonal non-cointegration is clearly rejected on statistical grounds. The findings from the real-world examples are corroborated by Monte Carlo simulation.
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New Developments in Time Series Econometrics: An Overviewnot be made independent and optimal experimental designs are not available, modeling and inference often require an exceptional degree of sophistication. Fortunately, in recent years, statistical methods for the analysis of time series have developed considerably and several remarkable innovations have been introduced.
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On the (Mis)Specification of Seasonality and its Consequences: An Empirical Investigation with US Daerties of the adjusted series. We also investigate which procedures are most appropriate given the properties of the data. Overall, we find very significant differences and evidence that several U.S. macroeconomic time series contain a mixture of deterministic and stochastic seasonal components.
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A Note on Johansen’s Cointegration Procedure when Trends are Present Johansen’s method by including a vector of deterministic linear trends in the estimated model. We present tabulated critical values of the maximal eigenvalue and trace statistics appropriate for this case. We discuss the circumstances under which our modification may be useful.
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