Panel Unit Root Tests

Testing for unit roots in panel data is pretty standard stuff these days. Any decent econometrics package has everything set up to make life easy for practitioners who want to apply such tests. As usual, though, ease of application doesn't guarantee correct application of the tests, or the interpretation of the associated results.

Yet Another Reason for Avoiding the Linear Probability Model

Oh dear, here we go again. Hard on the heels of this post, as well an earlier one here, I'm moved to share even more misgivings about the Linear Probability Model (LPM). That's just a fancy name for a linear regression model in which the dependent variable is a binary dummy variable, and the coefficients are estimated by OLS.

Another Gripe About the Linear Probability Model

NOTE: This post was revised significantly on 15 February, 2019, as a result of correcting an error in my original EViews code. The code file and the Eviews workfile that are available elsewhere on separate pages of this blog were also revised. I would like to thank Frederico Belotti for drawing my attention to the earlier coding error.

So you're still thinking of using a Linear Probability Model (LPM) - also known in the business as good old OLS - to estimate a binary dependent variable model?

Well, I'm stunned!

Yes, yes, I've heard all of the "justifications" (excuses) for using the LPM, as opposed to using a Logit or Probit model. Here are a few of them:

Econometrics Beat on Twitter

Last week I finally caved in and joined Twitter!

My intention is to use it in tandem with this blog, but we'll see how that works out. I might get totally carried away.

I'll be tweeting @DEAGiles.

© 2012, David E. Giles

More About Spurious Regressions

Students of econometrics are familiar with the "spurious regression" problem that can arise with (non-stationary) time-series data. 

As was pointed out by Granger and Newbold (1974), the “levels” of many economic time-series are integrated (or nearly so), and if these data are used in a regression model then a high value for the coefficient of determination (R²) is likely to arise, even when the series are actually independent of each other. 

They also demonstrated that the associated regression residuals are likely to be positively autocorrelated, resulting a very low value for the Durbin-Watson (DW) statistic. There was a time when we tended to describe a “spurious regression” as one in which R² > DW. 

"Disappearing" Historical Data

It's the bane of my life - and probably that of every other economist who (tries to) work(s) with time-series data. Historical data that simply disappear.

Shazam! Now you see, it; now you don't!

You know the sort of thing I mean, I'm sure. Just when you think you have a nice long, consistent, series of economic data, the statistical agency in question suddenly stops recording it. They change the basis on which the data are gathered (usually for perfectly good reasons), and leave you with a big red DISCONTINUED descriptor.

Gee - thanks a lot!

Sometimes they even "pull" the historical series that you were really counting on, and just provide you with a pretty new series that started yesterday, and won't be of any use to anyone for ages.

Forecasting: Principles and Practice

Forecasting: Principles and Practice is the title of a new book by Rob Hyndman and George Athanasopoulos.

As Rob says on his webpage:

"The book is dif­fer­ent from other fore­cast­ing text­books in sev­eral ways.

• It is free and online, mak­ing it acces­si­ble to a wide audience.
• It is based around the fore­cast pack­age for R.
• It is con­tin­u­ously updated. You don’t have to wait until the next edi­tion for errors to be removed or new meth­ods to be dis­cussed. We will update the book frequently.
• There are dozens of real data exam­ples taken from our own con­sult­ing prac­tice. We have worked with hun­dreds of busi­nesses and orga­ni­za­tions help­ing them with fore­cast­ing issues, and this expe­ri­ence has con­tributed directly to many of the exam­ples given here, as well as guid­ing our gen­eral phi­los­o­phy of forecasting.
• We empha­sise graph­i­cal meth­ods more than most fore­cast­ers. We use graphs to explore the data, analyse the valid­ity of the mod­els fit­ted and present the fore­cast­ing results."

This looks really good!

© 2012, David E. Giles

It's Not Rocket Science!

In fact, it's pretty obvious that this isn't any sort of science.

I'm referring to this little gem, in a post from Eric Crampton in the Offsetting Behaviour blog, back in February.

And it seems that these pseuds. just won't go away - see Eric's post today.

@Students in my ECON 246 course: If there were a Darwin Award for sample surveying, this would win one, hands down!

© 2012, David E. Giles

Happy Birthday, "Your Better Life Index"

One year ago, the OECD released its Your Better Life Index. The Index sought to provide comparative data relating to well-being that go beyond the traditional economic output measures such as GDP.

I analyzed some of the YBLI data in a few posts last year - here, here, and here.

The YBLI has been re-released (updated) today. The OECD tells us:

"Some of the key takeaways from the new version of the Index include:

• No matter which countries people live in, they value the most some combination of  health, education and life satisfaction.
• Men and women who have used the Index value basically the same things.
• The wealthier you are, the more likely you are to make your voice heard in elections, but not by a huge margin.
• Men work more in the labour market and make more money than women, but women are better in other areas, they live longer, are better educated and in most places they are also happier.
• Inequality isn’t just about money, it affects other topics in Your Better Life Index."

BTW, if you have an interest in these issues, then the Freedom and Flourishing blog that Winton Bates writes should be on your reading list.

 

© 2012, David E. Giles

Log Transformations & Forecasting

I enjoyed reading the lead article in the latest issue of Empirical Economics, by Helmut Lütkepohl and Fang Xu. It assesses the quality of forecasts obtained from an ARIMA model that is estimated using the levels of the data in question, as opposed to forecasts that are generated from a model estimated from the logarithms of the data.