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Results tagged with autoregressive
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user 3382
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
0
votes
Accepted
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= …
0
votes
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 …
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 …
1
vote
Accepted
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 …
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 …
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 …
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 …
1
vote
Accepted
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: …
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 …
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 …
0
votes
Accepted
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 …
1
vote
Accepted
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 …
2
votes
Accepted
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 …
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 …