The 14 reference contexts in paper Christopher F. Baum () “Tests for stationarity of a time series” / RePEc:tsj:stbull:y:2001:v:10:i:57:sts15

  1. Start
    6484
    Prefix
    Acknowledgment We are grateful to Kit Baum for alerting us to this problem. References Cox, N. J. and J. B. Wernow. 2000. dm80: Changing numeric variables to string.Stata Technical Bulletin56: 8–12. dm81Utility for time series data
    Exact
    Christopher F. Baum, Boston College, baum@bc.edu Vin
    Suffix
    ce Wiggins, Stata Corporation, vwiggins@stata.com Abstract:A program entitledtsmktimis described which makes the creation of time variables more convenient. Keywords:time series, calendar, time variables.
    (check this in PDF content)

  2. Start
    117746
    Prefix
    Reprinted inStata Technical Bulletin Reprints, vol. 4, pp. 165–170. Newson, R. 2000. snp15:somersd—Confidence limits for nonparametric statistics and their differences.Stata Technical Bulletin55: 47–55. sts15Tests for stationarity of a time series
    Exact
    Christopher F. Baum, Boston College, baum@bc.edu Abs
    Suffix
    tract:Implements the Elliott–Rothenberg–Stock (1996)DF-GLStest and the Kwiatkowski–Phillips–Schmidt–Shin (1992) KPSStests for stationarity of a time series. TheDF-GLStest is an improved version of the augmented Dickey–Fuller test.
    (check this in PDF content)

  3. Start
    128194
    Prefix
    H. 1994. Unit roots, structural breaks and trends. InHandbook of Econometrics IV, ed. R. F. Engle and D. L. McFadden. Amsterdam: Elsevier. sts16Tests for long memory in a time series
    Exact
    Christopher F. Baum, Boston College, baum@bc.edu Vin
    Suffix
    ce Wiggins, Stata Corporation, vwiggins@stata.com Abstract:Implements the Geweke/Porter-Hudak log periodogram estimator (1983), the Phillips modified log periodogram estimator (1999b) and the Robinson log periodogram estimator (1995) for the diagnosis of long memory, or fractional integration, in a time series.
    (check this in PDF content)

  4. Start
    133068
    Prefix
    The stochastic processytis both stationary and invertible if all roots of\b(L)and-(L)lie outside the unit circle andjdj<0.5. The process is nonstationary ford-0.5, as it possesses infinite variance; for example, see Granger and Joyeux (1980). Assuming thatd2[0;0:5),
    Exact
    Hosking (1981)
    Suffix
    showed that the autocorrelation function,-(-),ofanARFIMAprocess is proportional tok2d-1ask!1. Consequently, the autocorrelations of theARFIMAprocess decay hyperbolically to zero as k!1in contrast to the faster, geometric decay of a stationaryARMAprocess.
    (check this in PDF content)

  5. Start
    134474
    Prefix
    Thus, it may be predictable at long horizons. Long memory models originated in hydrology and have been widely applied in economics and finance. An excellent survey of long memory models is given by
    Exact
    Baillie (1996)
    Suffix
    There are two approaches to the estimation of anARFIMA(p;d;q)model: exact maximum likelihood estimation, as proposed by Sowell (1992), and semiparametric approaches, as described in this insert.
    (check this in PDF content)

  6. Start
    134621
    Prefix
    An excellent survey of long memory models is given by Baillie (1996). There are two approaches to the estimation of anARFIMA(p;d;q)model: exact maximum likelihood estimation, as proposed by
    Exact
    Sowell (1992)
    Suffix
    and semiparametric approaches, as described in this insert. Sowell’s approach requires specification of thepandqvalues, and estimation of the fullARFIMAmodel conditional on those choices. This involves all the attendant difficulties of choosing an appropriateARMAspecification, as well as a formidable computational task for each combination of pandqto be evaluated.
    (check this in PDF content)

  7. Start
    137569
    Prefix
    The statistic based upon that standard error has a standard normal distribution under the null. modlprcomputes a modified form of theGPHestimate of the long memory parameter,d, of a time series, proposed by
    Exact
    Phillips (1999a, 1999b)
    Suffix
    b). Phillips (1999a) points out that the prior literature on this semiparametric approach does not address the case ofd=1, or a unit root, in (3), despite the broad interest in determining whether a series exhibits unit-root behavior or long memory behavior, and his work showing that thebdestimate of (5) is inconsistent whend>1;withbdexhibiting asymptotic bias toward unity.
    (check this in PDF content)

  8. Start
    137597
    Prefix
    The statistic based upon that standard error has a standard normal distribution under the null. modlprcomputes a modified form of theGPHestimate of the long memory parameter,d, of a time series, proposed by Phillips (1999a, 1999b).
    Exact
    Phillips (1999a)
    Suffix
    points out that the prior literature on this semiparametric approach does not address the case ofd=1, or a unit root, in (3), despite the broad interest in determining whether a series exhibits unit-root behavior or long memory behavior, and his work showing that thebdestimate of (5) is inconsistent whend>1;withbdexhibiting asymptotic bias toward unity.
    (check this in PDF content)

  9. Start
    139656
    Prefix
    proves that, with appropriate assumptions on the distribution of-t;the distribution of~dfollows p m d~-d !N 0; -2 24 (7) in distribution, so~dhas the same limiting distribution atd=1 as does theGPHestimator in the stationary case so~dis consistent for values ofdaround unity. A semiparametric test statistic for a unit root against a fractional alternative is then based upon the statistic
    Exact
    (Phillips 1999a, 10)
    Suffix
    zd= p m d~-1 -=24 (8) with critical values from the standard normal distribution. This test is consistent against bothd<1andd>1fractional alternatives. roblprcomputes the Robinson (1995) multivariate semiparametric estimate of the long memory (fractional integration) parameters,d(g),ofasetofGtime series,y(g),g=1;GwithG-1.
    (check this in PDF content)

  10. Start
    139857
    Prefix
    A semiparametric test statistic for a unit root against a fractional alternative is then based upon the statistic (Phillips 1999a, 10) zd= p m d~-1 -=24 (8) with critical values from the standard normal distribution. This test is consistent against bothd<1andd>1fractional alternatives. roblprcomputes the
    Exact
    Robinson (1995)
    Suffix
    multivariate semiparametric estimate of the long memory (fractional integration) parameters,d(g),ofasetofGtime series,y(g),g=1;GwithG-1. When applied to a set of time series, thed(g)parameter for each series is estimated from a single log-periodogram regression which allows the intercept and slope to differ for each series.
    (check this in PDF content)

  11. Start
    140724
    Prefix
    The estimator also allows for the removal of one or more initial ordinates and for the averaging of the periodogram over adjacent frequencies. The rationale for using non-default values of either of these options is presented in
    Exact
    Robinson (1995)
    Suffix
    Robinson (1995) proposes an alternative log-periodogram regression estimator which he claims provides “modestly superior asymptotic efficiency to-d(0)”, (-d(0)being the Geweke and Porter-Hudak estimator) Robinson (1995, 1052).
    (check this in PDF content)

  12. Start
    140740
    Prefix
    The estimator also allows for the removal of one or more initial ordinates and for the averaging of the periodogram over adjacent frequencies. The rationale for using non-default values of either of these options is presented in Robinson (1995).
    Exact
    Robinson (1995)
    Suffix
    proposes an alternative log-periodogram regression estimator which he claims provides “modestly superior asymptotic efficiency to-d(0)”, (-d(0)being the Geweke and Porter-Hudak estimator) Robinson (1995, 1052).
    (check this in PDF content)

  13. Start
    140952
    Prefix
    Robinson (1995) proposes an alternative log-periodogram regression estimator which he claims provides “modestly superior asymptotic efficiency to-d(0)”, (-d(0)being the Geweke and Porter-Hudak estimator)
    Exact
    Robinson (1995, 1052)
    Suffix
    . Robinson’s formulation of the log-periodogram regression also allows for the formulation of a multivariate model, providing justification for tests that different time series share a common differencing parameter.
    (check this in PDF content)

  14. Start
    142755
    Prefix
    The standard errors for the estimated parameters are derived from a pooled estimate of the variance in the multivariate case, so that their interval estimates differ from those of their univariate counterparts. Modifications to this derivation when the frequency-averaging (j) or omission of initial frequencies (l) options are selected may be found in
    Exact
    Robinson (1995)
    Suffix
    Examples Data from Terence Mills’Econometric Analysis of Financial Time SeriesonUK FTAAll Share stock returns (ftaret)and dividends (ftadiv) are analyzed. .usehttp://fmwww.bc.edu/ec-p/data/Mills2d/fta.dta .tsset timevariable:month,1965m1to1995m12 .gphudakftaret,power(0.50.60.7) GPHestimateoffractionaldifferencingparameter ---------------------------------------------------------------------------
    (check this in PDF content)