This site is a part of the JavaScript E-labslearning objects for decision making. Other JavaScript in this series are categorized under different areas of applications in theMENUsection on this page.

This site provides the necessary tools for the identification, estimation, and forecasting based on autoregressive order one obtained from a given time series:

X(t +1) = F

_{0}+ F_{1}X(t) + e_{t},where e

_{t}is a White-Noise series. Note that, theStationary Condition: | F

_{1}| < 1is expressed as a null hypothesis H

_{0}and being tested.

Notice: As always, it is necessary to construct the graph and , compute statistics and check for both stationary in mean and variance, as well as the seasonality test. For many time-series one must perform, differencing; data transformation; and/or deasonalitization prior to using this JavaScript.

Enter your up-to-84, ordered values of your time series, row-wise and, then click the

Calculatebutton. Blank boxes are not included in the calculations but zeros are.In entering your data to move from cell to cell in the data-matrix use the

Tab keynot arrow or enter keys.

For Technical Details, Back to:

Decision Making in Economics and Finance

Kindly email your comments to:

Professor Hossein Arsham

The Copyright Statement: The fair use, according to the 1996 Fair Use Guidelines for Educational Multimedia, of materials presented on this Web site is permitted for non-commercial and classroom purposes only.

This site may be translated and/or mirrored intact (including these notices), on any server with public access. All files are available at http://home.ubalt.edu/ntsbarsh/Business-stat for mirroring.Kindly e-mail me your comments, suggestions, and concerns. Thank you.

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