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Applied time series analysis : a practical guide to modeling and forecasting /
Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public health. Te...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Mills, Terence C., |e author. | |
| 245 | 1 | 0 | |a Applied time series analysis : |b a practical guide to modeling and forecasting / |c Terence C. Mills. |
| 264 | 1 | |a London, United Kingdom : |b Academic Press, |c [2019] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | |a Online resource; title from PDF title page (EBSCO, viewed January 30, 2019). | ||
| 520 | |a Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public health. Terence Mills provides a practical, step-by-step approach that emphasizes core theories and results without becoming bogged down by excessive technical details. Including univariate and multivariate techniques, Applied Time Series Analysis provides data sets and program files that support a broad range of multidisciplinary applications, distinguishing this book from others. | ||
| 505 | 0 | |a Time series and their features -- Transforming time series -- ARMA models for stationary time series -- ARIMA models for nonstationary time series -- Unit roots, difference and trend stationarity, and fractional differencing -- Breaking and nonlinear trends -- An introduction to forecasting with univariate models -- Unobserved component models, signal extraction, and filters -- Seasonality and exponential smoothing -- Volatility and generalized autoregressive conditional heteroskedastic processes -- Nonlinear stochastic processes -- Transfer functions and autoregressive distributed lag modeling -- Vector autoregressions and Granger causality -- Error corection, spurious regressions, and cointegration -- Vector autoregressions with integrated variables, vector error correction models, and common trends -- Compositional and count time series -- State space models. | |
| 650 | 0 | |a Time-series analysis. | |
| 650 | 6 | |a S�erie chronologique. |0 (CaQQLa)201-0018628 | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Time-series analysis |2 fast |0 (OCoLC)fst01151190 | |
| 776 | 0 | 8 | |i Print version: |a Mills, Terence C. |t Applied time series analysis. |d London, United Kingdom : Academic Press, [2019] |z 0128131179 |z 9780128131176 |w (OCoLC)1057646417 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128131176 |z Texto completo |