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The Breusch–Godfrey test is a test for autocorrelation in the errors in a regression model. It makes use of the residuals from the model being considered in a regression analysis, and a test statistic is derived from these. The null hypothesis is that there is no serial correlation of any order up to p. [3]
In SAS, SUR can be estimated using the syslin procedure. [14] In Stata, SUR can be estimated using the sureg and suest commands. [15] [16] [17] In Limdep, SUR can be estimated using the sure command [18] In Python, SUR can be estimated using the command SUR in the “linearmodels” package. [19] In gretl, SUR can be estimated using the system ...
[8] In Stata, one specifies the full regression, and then enters the command estat hettest followed by all independent variables. [9] [10] In SAS, Breusch–Pagan can be obtained using the Proc Model option. In Python, there is a method het_breuschpagan in statsmodels.stats.diagnostic (the statsmodels package) for Breusch–Pagan test. [11]
A Newey–West estimator is used in statistics and econometrics to provide an estimate of the covariance matrix of the parameters of a regression-type model where the standard assumptions of regression analysis do not apply. [1] It was devised by Whitney K. Newey and Kenneth D. West in 1987, although there are a number of later variants.
X-12-ARIMA can be used together with many statistical packages, such as SAS in its econometric and time series (ETS) package, R in its (seasonal) package, [6] Gretl or EViews which provides a graphical user interface for X-12-ARIMA, and NumXL which avails X-12-ARIMA functionality in Microsoft Excel. [7] There is also a version for MATLAB. [8]
A regression model is estimated to predict observed values of a variable based on other variables, and that model is then used to impute values in cases where the value of that variable is missing. In other words, available information for complete and incomplete cases is used to predict the value of a specific variable.
In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.
General numerical computing package with many extension modules. Syntax mostly compatible with MATLAB IGOR Pro: WaveMetrics 1986 1988 8.00 May 22, 2018: $995 (commercial) $225 upgrade, $499 (academic) $175 upgrade, $85 (student) Proprietary: interactive graphics, programmable, 2D/3D, used for science and engineering, large data sets. imc FAMOS