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In Stata, this test is performed by the command estat bgodfrey. [7] [8] In SAS, the GODFREY option of the MODEL statement in PROC AUTOREG provides a version of this test. In Python Statsmodels, the acorr_breusch_godfrey function in the module statsmodels.stats.diagnostic [9] In EViews, this test is already done after a regression, at "View" → ...
RATS: robusterrors option is available in many of the regression and optimization commands (linreg, nlls, etc.). Stata: robust option applicable in many pseudo-likelihood based procedures. [19] Gretl: the option --robust to several estimation commands (such as ols) in the context of a cross-sectional dataset produces robust standard errors. [20]
The forerunner of RATS was a FORTRAN program called SPECTRE, written by economist Christopher A. Sims. [2] SPECTRE was designed to overcome some limitations of existing software that affected Sims' research in the 1970s, by providing spectral analysis and also the ability to run long unrestricted distributed lags. [3]
For example, in time series analysis, a plot of the sample autocorrelations versus (the time lags) is an autocorrelogram. If cross-correlation is plotted, the result is called a cross-correlogram . The correlogram is a commonly used tool for checking randomness in a data set .
Whereas Stata/MP allows for built-in parallel processing of certain commands, Stata/SE and Stata/BE are bottlenecked and limit usage to only one single core. [19] Stata/MP runs certain commands about 2.4 times faster, roughly 60% of theoretical maximum efficiency, when running parallel processes on four CPU cores compared to SE or BE versions. [19]
With any number of random variables in excess of 1, the variables can be stacked into a random vector whose i th element is the i th random variable. Then the variances and covariances can be placed in a covariance matrix, in which the (i, j) element is the covariance between the i th random variable and the j th one.
Although in the extreme cases of perfect rank correlation the two coefficients are both equal (being both +1 or both −1), this is not generally the case, and so values of the two coefficients cannot meaningfully be compared. [7] For example, for the three pairs (1, 1) (2, 3) (3, 2) Spearman's coefficient is 1/2, while Kendall's coefficient is ...
Suppose, for instance, that a number of people have been asked to rank a list of political concerns, from the most important to the least important. Kendall's W can be calculated from these data. If the test statistic W is 1, then all the survey respondents have been unanimous, and each respondent has assigned the same order to the list of ...