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The autoregressive (AR) model is a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.

1 vote

How do you model time series data with an autogressive model?

Simply create two lags ( predictor series) of your series and estimate a regression equation with an intercept. This will give you the three a's and the error variance
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Does Auto-correlation cause AR(p) model?

Yes .... If you have yt=XtB+ut where ut=ρut−1+et, et is iid and you clear fractions then you have a model of the form y(t)= γy(t−1) + X_{t}-[1-γ]X_{t-1} + e_t$, and $e_t$ is iid. where $w_t= …
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How to decide the optimal AR-model order?

Modern procedures would include the explicit identification and incorporation of determinsitic structure (pulses,level shifts,seasonal pulses and local time trends) into an equation that would also in …
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3 votes

Autocorrelation and auto regressive

Yes that is correct auto-correlation is a sequence of correlations taken at fixed intervals. If the auto-correlation decays while the partial correlation (conditional correlation ) cuts off after k va …
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1 vote
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When is an AR model appropriate for a data set?

Structural break is a symptom ... the cause may be a shift in the mean or trend or a shift in any other parameter or number of parameters. The cause may be due to a deterministic change in the error v …
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2 votes

What is the reason for not including an intercept term in AR and ARMA models?

To begin with in arima models the constant is mandatory if d=0 i.e.no differencing is in play. If d<>=0 then the constant is optional. If d<>=0 and a constant is in the model there is a steady state c …
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1 vote

Create auto regressive regressors in R (extract from auto.arima)

These 5 impute lags of the output series GIVEN that the output series is regularly and seasonally differenced. What you are looking for is an augmented data matrix of 0/1 values which can be pre-spec …
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0 votes

Poisson with an autoregressive term

Have you considered a Transfer Function between N and E and your other covariates which could encode changes in parameters over time , changes in error variance over time , any necessaery autoregressive
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Quantify volatility on a time series data

After you have accounted for the ARIMA structure AND any Pulses/Level Shifts/Seasonal Pulses/Local Time Trends AND any changes in parameters over time, one can test for non-constant variance via http: …
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2 votes

Time series: correcting the standard errors for autocorrelation

The acf is the ratio of the covariance to the variance. If you have pulses/level shifts/seasonal pulses and/or local time trends (my guess is YES!) then both the covariance and the variance are affect …
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1 vote

How to do Univariate Heteroscedasticity Test

This question was answered in 1988 http://www.unc.edu/~jbhill/tsay.pdf by R.Tsay and implemented in AUTOBOX in 1990. As of this date (today) no other forecasting/time series package has implemented hi …
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Why different output eviews 8 vs. eviews 9; how to interpret?

It appears to me that the ar coefficient (1.102) in "8" is not invertable as it exceeds 1.0 . It was estimated using conditional least squares. If you use maximimum liklehood (as they did in version …
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AR(1) forecasting

What you might do is to develop a theoretical (guessed !) prior probability distribution (frequency distribution) for possible values of Y(t) and create/simulate via Monte Carlo a family of N possible …
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2 votes
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Convolution with autoregressive filter

Identify an ARIMA filter while taking account (robust) any deterministic time trends, level shifts, seasonal pulses or 1 time anomalies. The idea here is transform the stationary X into a white-noise …
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4 votes

Difference between MA and AR

A finite AR model can be expressed as an MA model and vice-versa , If one has an ar(1) model with coefficient .333333333 then the models are (nearly) identical . Consider the case for an ar(1) with co …
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