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Venables, W. N. and B. D. Ripley (2001). S Programming. Springer-Verlag, New York. edu/DOS/S. 1 Introduction Time series data may be stored, manipulated and visualized in a variety of ways in S-PLUS1 . This chapter discusses the basics of working with financial time series data in the form of S-PLUS “timeSeries” objects. It begins with a discussion of the specification of “timeSeries” and “timeDate” objects in S-PLUS and gives examples of how to specify common “timeDate” sequences for financial time series.
Length of others (21) Use traceback() to see the call stack In S+FinMetrics, the formula is extended to support autoregressive specification, moving average specification, distributed lags and polynomial distributed lags for many modeling functions. These formulas will be illustrated in the appropriate chapters. values or simply fitted for extracting model fitted values, predict for out of sample prediction, etc. mod, ask=T) Make a plot selection (or 0 to exit): 1: plot: All 2: plot: Residuals vs Fitted Values 3: plot: Sqrt of abs(Residuals) vs Fitted Values 4: plot: Response vs Fitted Values 5: plot: Normal QQplot of Residuals 6: plot: r-f spread plot 7: plot: Cook’s Distances Selection: In addition to the above generic functions, S+FinMetrics defines three new generic functions for working with model objects: vcov for extracting 14 1.
13 29281200 ... double(sum(x[, "Volume"])) nrowx = numRows(x) 4 This method was suggested by Steve McKinney. 34 2. 59 ... 59 Disaggregating Time Series Consider the problem of creating a daily “timeSeries” of inflation adjusted (real) prices on Microsoft stock over the period January 2, 1991 through January 2, 2001. g. the consumer price level (CPI). dat. The CPI data, however, is only available monthly. dat) [1] Nov 2001 and represents the average overall price level during the month but is recorded at the end of the month.
