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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]
[1] [2] Both describe the degree to which two random variables or sets of random variables tend to deviate from their expected values in similar ways. If X and Y are two random variables, with means (expected values) μ X and μ Y and standard deviations σ X and σ Y , respectively, then their covariance and correlation are as follows:
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
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]
Intuitively, the Kendall correlation between two variables will be high when observations have a similar (or identical for a correlation of 1) rank (i.e. relative position label of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables, and low when observations have a dissimilar (or fully different for a ...
In EViews, this test is already done after a regression, at "View" → "Residual Diagnostics" → "Serial Correlation LM Test". In Julia, the BreuschGodfreyTest function is available in the HypothesisTests package. [10] In gretl, this test can be obtained via the modtest command, or under the "Test" → "Autocorrelation" menu entry in the GUI ...
Stata's proprietary file formats have changed over time, although not every Stata release includes a new dataset format. Every version of Stata can read all older dataset formats, and can write both the current and most recent previous dataset format, using the saveold command. [11]
MCA is performed by applying the CA algorithm to either an indicator matrix (also called complete disjunctive table – CDT) or a Burt table formed from these variables. [citation needed] An indicator matrix is an individuals × variables matrix, where the rows represent individuals and the columns are dummy variables representing categories of the variables. [1]