AR(1) Model

Autoregressive Time Series Modeling

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

Professor Hossein Arsham   

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) = F0 + F1X(t) + et,

where et is a White-Noise series. Note that, the

Stationary Condition: | F1| < 1

is expressed as a null hypothesis H0 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 Calculate button. 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 key not arrow or enter keys.



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X(t)
 
Mean X(t)
Variance X(t)
Autorrelation
Its Standard Error
Parameter F1
Its Standard Error
Parameter F0
Its Standard Error
H0: Stationary Condition: | F1| < 1

 

White Noise Analysis and Diagnostic Tools
Mean Variance
Mean:
The first half 		Mean:
The second half
Variance:
The first-half 		Variance:
The second half
First order serial-correlation Second order serial-correlation
First partial
serial-correlation		 Second partial
serial-correlation
Durbin-Watson statistic Mean absolute noise
Normality Condition
ith White Noise
ith Step Ahead Forecast



For Technical Details, Back to:
Decision Making in Economics and Finance

Kindly email your comments to:
Professor Hossein Arsham

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Kindly e-mail me your comments, suggestions, and concerns. Thank you.

Professor Hossein Arsham   

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