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There are many statistical packages that can be used to find structural breaks, including R, [17] GAUSS, and Stata, among others.For example, a list of R packages for time series data is summarized at the changepoint detection section of the Time Series Analysis Task View, [18] including both classical and Bayesian methods.
[1] [2] Given two completely unrelated but integrated (non-stationary) time series, the regression analysis of one on the other will tend to produce an apparently statistically significant relationship and thus a researcher might falsely believe to have found evidence of a true relationship between these variables.
In Gretl, the option --robust to several estimation commands (such as ols) in the context of a time-series dataset produces Newey–West standard errors. [ 16 ] In SAS , the Newey–West corrected standard errors can be obtained in PROC AUTOREG and PROC MODEL [ 17 ]
For many series, the period is known and a single seasonality term is sufficient. For example, for monthly data one would typically include either a seasonal AR 12 term or a seasonal MA 12 term. For Box–Jenkins models, one does not explicitly remove seasonality before fitting the model.
In time series analysis, the extension of these is to higher order spectra, for example the bispectrum and trispectrum. An alternative to the use of HOS and higher moments is to instead use L-moments , which are linear statistics (linear combinations of order statistics ), and thus more robust than HOS.
Cochran's test, [1] named after William G. Cochran, is a one-sided upper limit variance outlier statistical test .The C test is used to decide if a single estimate of a variance (or a standard deviation) is significantly larger than a group of variances (or standard deviations) with which the single estimate is supposed to be comparable.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
This is an important technique for all types of time series analysis, especially for seasonal adjustment. [2] It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behavior.