By Zivot E., Wang J.
This booklet represents an integration of conception, equipment, and examples utilizing the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the perform of monetary econometrics. this can be the 1st booklet to teach the ability of S-PLUS for the research of time sequence info. it really is written for researchers and practitioners within the finance undefined, educational researchers in economics and finance, and complex MBA and graduate scholars in economics and finance. Readers are assumed to have a simple wisdom of S-PLUS and an effective grounding in simple facts and time sequence techniques.
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This publication represents an integration of idea, tools, and examples utilizing the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the perform of economic econometrics. this is often the 1st ebook to teach the facility of S-PLUS for the research of time sequence facts. it's written for researchers and practitioners within the finance undefined, educational researchers in economics and finance, and complex MBA and graduate scholars in economics and finance.
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Additional info for Modeling Financial Time Series with S-PLUS
2 May 1990 NA NA To create a “timeSeries” with multiple lagged values, simply specify the lags to create in the call to tslag. timeSeries is a method function for the generic S-PLUS function diff for objects of class “timeSeries” and creates a speciﬁed number of dierences of a “timeSeries” object. 3 Time Series Manipulation in S-PLUS 43 of times to dierence the series, trim determines if the resulting series is to have NA values removed and trimmed and pad speciﬁes the value to be padded to the series in the positions where the dierencing operation exceeds the start or the end positions.
Heiberger and Holland (2004) emphasize the importance of graphical techniques in statistical analysis. Pinheiro and Bates (2000) detail the analysis of mixed eects (panel data) models. Therneau and Grambsch (2000) survey survival analysis models. Wilcox (1997), and Atkinson and Riani (2000) discuss robust statistical methods. Bruce and Gao (1996) describe wavelet analysis. Hastie, Tibshirani and Friedman (2001) cover aspects of statistical learning and data mining. Davison and Hinkley (1997) survey bootstrap methods, and Bowman and Azzalini (1997) disucss nonparametric and smoothing methods.
Statistical Models in S. Chapman & Hall. H. (2002). Time Series: Applications to Finance. John Wiley & Sons, New York. C. V. Hinkley (1997). Bootstrap Methods and Their Application. Cambridge University Press, Cambridge, UK. E. (2001). Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer-Verlag, New York. , R. Tibshirani and J. Friedman (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction. SpringerVerlag, New York.