44
Stata Technical Bulletin
STB-57
Saved Results
gphudak saves in e():
|
e(N.powers) |
number of powers (scalar) | |
|
modlpr saves in e(): |
e(N.powers) |
number of powers (scalar) |
|
roblpr saves the following scalars in |
r(): | |
|
r(N) |
number of observations | |
|
r(rob) |
e estimate | |
|
r(se) |
estimated standard error of d | |
|
r(t) |
s statistic | |
|
r(p) |
р-value of s statistic |
If more than one power is specified in roblpr, the saved results pertain to the last power used.
Acknowledgments
The first author acknowledges John Barkoulas’ original exposition of the ARFIMA model, and thanks Peter Phillips for
clarifying comments on his working papers. Any remaining errors are the authors’ responsibility.
References
Baillie, R. 1996. Long memory processes and fractional integration in econometrics. Journal of Econometrics 73: 5-59.
Geweke, J. and S. Porter-Hudak. 1983. The estimation and application of long memory time series models. Journal of Time Series Analysis 4:
221-238.
Granger, C. W. J. and R. Joyeux. 1980. An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis
1: 15-39.
Hosking, J. R. M. 1981. Fractional differencing. Biometrika 68: 165-176.
Phillips, P. C. B. 1999a. Discrete Fourier transforms of fractional processes. Unpublished working paper No. 1243, Cowles Foundation for Research
in Economics, Yale University. http://eowles.econ.yale.edu∕P∕cd∕dl2a∕dl243.pdf
——. 1999b. Unit root log periodogram regression. Unpublished working paper No. 1244, Cowles Foundation for Research in Economics, Yale
University. http : //eowles. econ. yale. edu∕P∕cd∕dl2a∕dl244.pdf
Robinson, P. M. 1995. Log-periodogram regression of time series with long range dependence. Annals of Statistics 23: 1048-1072.
Sowell, F. 1992. Maximum likelihood estimation of stationary univariate fractionally-integrated time-series models, Journal of Econometrics 53: 165-188.
sts17 Compactingtimeseriesdata
Christopher F. Baum, Boston College, [email protected]
Abstract: tscoIlap provides the ability to compact data of monthly, quarterly or half-yearly frequencies to a lower frequency
by one or more methods (e.g., average, sum, last value per period, and so on).
Keywords: time series, data frequency, collapse.
Syntax
tscollap clist, to(freq) [generate (freqvar) ]
where clist is either
[(stat)] varlist [ [(stat)] ... ]
or
[(stat) target war = varname [target-aar = varname ... ] [ [(stat) ∙ ∙ ∙ ]]
or any combination of the varlist or target war forms, and stat is one of
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