Showing papers in "Stata Technical Bulletin in 2001"

B-splines and splines parameterized by their values at reference points on the x-axis

Abstract: A package of 3 programs is presented for generating a basis of splines in an X–variable, to be input to regression programs to fit spline models. The first, bspline, generates a basis of Schoenberg B–splines, which avoid the stability problems associated with plus–functions. The second, frencurv, generates a basis of reference splines, whose parameters in the regression model are simply values of the spline at reference points on the X–axis. The third, flexcurv, is an easy–to–use version of frencurv, which generates reference splines with sensibly–spaced knots. frencurv and flexcurv have the additional option of generating an incomplete basis of reference splines, with the reference spline for one reference point omitted. This incomplete basis can be completed by adding the standard constant vector to the design matrix, and can then be used to estimate differences between values of the spline at the remaining reference points and the value of the spline at the omitted reference point.

Tests for stationarity of a time series

Abstract: sts15 Tests for stationarity of a time series Christopher F. Baum, Boston College, baum@bc.edu Abstract: Implements the Elliott–Rothenberg–Stock (1996) DF-GLS test and the Kwiatkowski–Phillips–Schmidt–Shin (1992) KPSS tests for stationarity of a time series. The DF-GLS test is an improved version of the augmented Dickey–Fuller test. The KPSS test has a null hypothesis of stationarity and may be employed in conjunction with the DF-GLS test to detect long memory (fractional integration).

Stereotype ordinal regression

Abstract: There are a number of reasonable approaches to analysing an ordinal outcome variable. One common approach, known as the Proportional Odds (PO) Model, is implemented in Stata as ologit. If the assumptions of the PO model are not satisfied, an alternative is to treat the outcome as categorical, rather than ordinal, and use multinomial logistic regression (mlogit) in Stata. This insert describes an alternative form of ordinal regression model, the Stereotype Ordinal Regression (SOR) Model, which can be thought of as imposing ordering constraints on a multinomial model. The multinomial model provides the best possible fit to the data, at the cost of a large number of parameters which can be difficult to interpret. Stereotype regression aims to reduce the number of parameters by imposing constraints, without reducing the adequacy of the fit.

Tests for long memory in a time series

Abstract: Acknowledgments I acknowledge useful conversations with Serena Ng, James Stock, and Vince Wiggins. The KPSS code was adapted from John Barkoulas’ RATS code for that test. Thanks also to Richard Sperling for tracking down a discrepancy between published work and the dfgls output and alerting me to the Cheung and Lai estimates. Any remaining errors are my own. References Cheung, Y. W. and K.-S. Lai. 1995. Lag order and critical values of a modified Dickey–Fuller test. Oxford Bulletin of Economics and Statistics 57: 411–419. Elliott, G., T. J. Rothenberg, and J. H. Stock. 1996. Efficient tests for an autoregressive unit root. Econometrica 64: 813–836. Kwiatkowski, D., P. C. Phillips, P. Schmidt, and Y. Shin. 1992. Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics 54: 159–178. Lee, D. and P. Schmidt. 1996. On the power of the KPSS test of stationarity against fractionally-integrated alternatives. Journal of Econometrics 73: 285–302. Ng, S. and P. Perron. 1995. Unit root tests in ARMA models with data-dependent methods for the selection of the truncation lag. Journal of the American Statistical Association 90: 268–281. Schwert, G. W. 1989. Tests for unit roots: A Monte Carlo investigation. Journal of Business and Economic Statistics 7: 147–160. Stock, J. H. 1994. Unit roots, structural breaks and trends. In Handbook of Econometrics IV, ed. R. F. Engle and D. L. McFadden. Amsterdam: Elsevier.

Multivariate portmanteau (Q) test for white noise

Abstract: wntstmvq performs the multivariate Ljung–Box portmanteau (or Q) test for white noise in a set of time series. This test is a generalization of the univariate Ljung–Box portmanteau ( Q) test implemented in Stata as wntestq. The multivariate form of the test was proposed by Hosking (1980) and others. Hosking (1981) demonstrated the equivalence of the several forms in the literature. The test implemented here is that described in Johansen (1995, 22). It is often applied to the residuals of a multivariate regression, such as a VAR (vector autoregression).

Tests for stationarity of a time series: update

Abstract: dfgls did not handle missing initial values properly. That is, if the time series variable specified had initial values not excluded byif or in conditions, those values were improperly considered in the construction of the sample size. This would apply as well to the consideration of variables with time series operators, such as D.gdp, since those variables will have at least one missing observation at the outset. This has been corrected.