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Seasonality can be treated by adding 23 seasonal dummies. Seasonal arma or seasonal differences are often deficient. The whole idea is that time series modelling requires evaluating alternative approa …

This is probably due to untreated deterministic (NOT STOCHASTIC) effects like level shifts or local time trends in the original series and is not representative of a recurring autoprojective (arima) p …

After receipt of the data (252 values starting at 1999/1) and analyzing the data as given an appropriate model is (1,1,0)(0,1,0)12 WITHOUT DRIFT with 2 pulse indicators and a deterministic error varia …

The whole idea is to efficiently characterize the original data with as few coefficients as possible. For alternative models we can compute the implied acf or the theoretical acf given the data. It i …

"Difference of autocorrelation" doesn't make sense. Did you mean "autocorrelation of difference" ? …

For each time series estimate an AR(2) model for all the 1000 values and then for different subsets e.g. 1-100 and 101-1000 ( break point 101) and perform a CHOW test for constancy of parameters (1960 …

For non-seasonal data if the ACF is dominant then the order of the AR model is the last significant PACF value ...reverse this logic to asses the order of the MA model If you tons of significant ACF a …

Think of the ACF as unconditional auto-regression coefficients reflecting the importance of any 1 AND ONLY 1 particular lag. Now think of the PACF as a set of conditional auto-regression coefficients …

Just saw your question ... Please look at how to generate random time series for a given one, including all trends? for my suggestion as to how to simulate a series given its DGF ( read model form ! ) …

The Ljung_Box test is a global/cumulative test for all estimated autocorrelations. It assumes that the error process is free of latent deterministic structure (pulses,level shifts,local time trends an …

A model with no lags or differences and no arima structure is often a NO-NO when you have time series data due to untreated auto-correlation . Time series analysis model identification enables more po …

interpreting the observed ACF and PACF an trying to match them to a candidate ARIMA model requires that the data under analysis has no 1) step/level shifts 2) deterministic time trends 3) pulses 4) …

Let us review the assumptions that are made when it is safe to visually OR computationally map the sample acf and pacf to a useful arima model. Firstly there must be no deterministic structure latent …

Power transforms suggested by the error variance being linked to the expected value can be useful Detrending or not and should I always take log first? and here When (and why) should you take the log …

This issue arises normally when you have data for a number of groups/panels and you wish to subsequently test the hypothesis of a common set of parameters across all groups/panels. AUTOBOX has an opti …