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I would like to know how I can determine the appropriate amount of lags in Matlab or another statistical package. I'm getting confused with VAR models and ARMAX models all the time and I'm a little stuck to be honest. Introduction on ADL models at wikipedia: http://en.wikipedia.org/wiki/Distributed_lag

Example of my issue: I have one measure based on prices and one measure based on news. I expect a linear relationship between both but I expect the news based measure to be lagged to the price effect therefore I want to try to do a linear regression with the lagged news measures to the non-lagged price measure to check how the coherence (R-squared) changes. I expect the lag to be 3-5 days so that is what I want to check (also some autocorrelation could be present). I would like to do this without manually adding regressors (general-to-specific/specific-to-general) but with some model selection criteria for the number of lags if this is possible for the ADL(p,q). Still remains unclear to me how I should handle this.

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  • $\begingroup$ you might want to check out Forecasting with Dynamic Regression Models by Pankratz, this book is full of examples on ADL (p,q) or you could refer a chapter in the book on dynamic regression - Forecasting: Methods and Applications by Spyros G. Makridakis, Steven C. Wheelwright and Rob J Hyndman. Also, you might want to ask this type of question with a reproducible example. $\endgroup$ – forecaster Aug 12 '14 at 22:56
  • $\begingroup$ Thank you very much for your suggestion I'll look into the Pankratz book. I updated the question with an example of what I want to check. $\endgroup$ – BigChief Aug 13 '14 at 5:16
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You could use cross-correlation analysis (CCF) to identify leading and lagging variables and max lag between them.

When you have found this out you can build a model with lags P, P+1, P+2, ... until current time.

Parameter restrictions can be tested as usual with F-test which tests restricted model against full-model.

You perhaps have to first prewhiten variables before CCF-analysis, since autocorrelation structure can affect analysis.

In any case you have to test residuals that they are IID.

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  • $\begingroup$ Thanks for your answer!! Pankratz writes something about it but does not help me very much. So what you say is I should first check for autocorrelation (acquiring autocorrelation lag order with pacf/acf), then use prewhitening, and finally I could use mathworks.nl/help/signal/ref/xcorr.html (biased or unbiased) for the lags with the independant variable?? Could I use one of these for prewhitening? (want to fix this in stat package): naufraghi.googlecode.com/svn-history/r241/dottorato/drtoolbox/… github.com/casperkaae/MATLAB/blob/master/drtoolbox/prewhiten.m $\endgroup$ – BigChief Aug 13 '14 at 8:08
  • $\begingroup$ sorry only pacf right because we look in ADF only at AR & lags of variables and not at moving averages? $\endgroup$ – BigChief Aug 13 '14 at 8:21
  • $\begingroup$ sorry to bother you again but could you provide me with a paper or other source where your method is used?? I think your method is very well doable but I lack some reading material very badly (and sources) $\endgroup$ – BigChief Aug 13 '14 at 20:52
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    $\begingroup$ You must first identify an ARIMA or AR(p) model for the x variable and get white noise residuals. Then you filter y series by this same model and get "residuals" which probably are not white noise. Then you do bivariate cross-correlation analysis with "residuals" of x and y series. Model for x series can be identified by ACF/PACF. Here is link to this method: onlinecourses.science.psu.edu/stat510/node/75 $\endgroup$ – Analyst Aug 14 '14 at 4:56
  • $\begingroup$ @BigChief, perhaps you could give me upvote? :) $\endgroup$ – Analyst Aug 15 '14 at 5:23

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