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06-03-2018 07:42 PM - last edited a month ago

Suppose one is attempting to run regression on the following two economic indicators:

Employment and Business Cycle

(please see graphic below)

Linear Regression won't work here because level of Business Cycle (say, from 0 to 100) includes both 'coming down' and 'going up.'

The regression coefficient for Business Cycle will always be not significant because Business Cycle values will include **offsetting** 'going up' and 'coming down' values.

Yet one knows that Business Cycle level is indeed important.

One would additionally need to include a **second** variable -- whether the cycle is 'going up' or 'coming down.' Change from Previous Period sounds appropriate.

Thus, Multiple Regression might be usable.

I'm curious on your take. How can one best go about working with this oscillator dilemma?

Thanks!

Nicholas Kormanik

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Posted in reply to NicholasKormanik

06-03-2018 07:53 PM

One way would be to include time, time**2, and time**3 as predictor variables.

Art, CEO, AnalystFinder.com

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Posted in reply to art297

06-03-2018 10:46 PM

Are those (time**2 and time**3) 'interaction variables'?

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Posted in reply to NicholasKormanik

06-04-2018 01:19 AM

My hunch is that an additional variable must be introduced. Employment and

Business Cycle are insufficient by themselves.

For instance, add in Change in Business Cycle.

Thus:

Y = Employment

X1 = Level of Business Cycle, 1-to-100

X2 = Change in Business Cycle from previous period

Regression Model:

Y = f(X1, X2)

This might solve the problem of, Is X1 heading up or down??

Business Cycle are insufficient by themselves.

For instance, add in Change in Business Cycle.

Thus:

Y = Employment

X1 = Level of Business Cycle, 1-to-100

X2 = Change in Business Cycle from previous period

Regression Model:

Y = f(X1, X2)

This might solve the problem of, Is X1 heading up or down??

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Posted in reply to NicholasKormanik

06-04-2018 01:37 AM

"Interaction Effect" - One variable depends on the level of another

variable.

Clearly that's the case at hand.

That raises the SAS question, should the "Interaction Effect" be explicitly

included?

Y = f(X1, X2, X1*X2)

variable.

Clearly that's the case at hand.

That raises the SAS question, should the "Interaction Effect" be explicitly

included?

Y = f(X1, X2, X1*X2)

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Posted in reply to NicholasKormanik

06-04-2018 08:39 AM

My guess would be no, they shouldn't be included. However, I'll leave that for the forum's statisticians to chime in.

Art, CEO, AnalystFinder.com

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Posted in reply to NicholasKormanik

06-03-2018 09:23 PM

Two common approaches are to add in variables for seasonality or or smoothing it out using moving averages.

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Posted in reply to NicholasKormanik

06-04-2018 08:57 AM

two ways:

1) try other model like PROC LOESS, PROC TRANREG .

2) time series analysis SAS/ETS, like PROC ARIMIA, PROC UCM .

Calling @Rick_SAS

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Posted in reply to Ksharp

06-04-2018 07:20 PM - edited 06-04-2018 09:19 PM

I'm using this as a general example of handling oscillators

(sinusoidal-shaped independent variables over time).

In the above case we have only one such variable, Business Cycle.

Suppose we have 1000 such variables.

Proc GLMSELECT permits us to check to see which of the variables appear most

significant - variable selection techniques.

But, as shown, 'oscillators' present special challenges. Not sure how to

handle it.

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Posted in reply to NicholasKormanik

06-04-2018 09:24 PM

This is time series and econometric modeling. Searching within those spaces will give you options. I work parallel to an econometrics team. I’ve never heard the term 'oscillator' it’s usually referenced as the business cycle or boom bust cycle.

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Posted in reply to NicholasKormanik

06-05-2018 08:39 AM

PROC GLMSELECT only can handle LINEAR relation, not non-linear .

I suggest you post it at SAS/ETS forum, there is someone could answer your question.

or try PROC PLS .