Wednesday, January 18, 2017

Quantitative Macroeconomic Modeling with Structural Vector Autoregressions

A terrific new book titled, Quantitative Macroeconomic Modeling with Structural Vector Autoregressions – An EViews Implementation, is now available for free downloading from the EViews site. The book is written by Sam Ouliaris, Adrian Pagan, and Jorge Restrepo.

The "blurb" about this important new book reads:
"Quantitative macroeconomic research is conducted in a number of ways. An important method has been the use of the technique known as Structural Vector Autoregressions (SVARs), which aims to gather information about dynamic processes in macroeconomic systems. This book sets out the theory underlying the SVAR methodology in a relatively simple way and discusses many of the problems that can arise when using the technique. It also proposes solutions that are relatively easy to implement using EViews 9.5. Its orientation is towards applied work and it does this by working with the data sets from some classic SVAR studies."
In my view, EViews is certainly the natural choice for this venture. As the authors note in their Preface:
"A choice had to be made about the computer package that would be used to perform the quantitative work and EViews was eventually selected because of its popularity amongst IMF staff and central bankers more generally."
Gareth Thomas (of EViews) has pointed out to me that: "much of the book is covered in the IMF's free online macroeconomic forecasting course.  The next iteration of which starts in February: "

I'm sure that this new resource will be very well received!

© 2017, David E. Giles

Tuesday, January 17, 2017

Royal Economic Society Webcasts on Econometrics

The Royal Economic Society has recently released videos of interviews with three leading econometricans, recorded during the Society's 2016 Meeting. These are: 

Webcasts of Special (Econometrics) Sessions at RES Meetings between 2011 and 2016 are also available for viewing - here.     
© 2017, David E. Giles

Friday, January 13, 2017

Vintage Years in Econometrics - The 1970's

Continuing on from my earlier posts about vintage years for econometrics in the 1930's, 1940's, 1950's, 1960's, here's my tasting guide for the 1970's.

Once again, let me note that "in econometrics, what constitutes quality and importance is partly a matter of taste - just like wine! So, not all of you will agree with the choices I've made in the following compilation."

Monday, January 9, 2017

Trading Models and Distributed Lags

Yesterday, I received an email from Robert Hillman.

Robert wrote:
"I’ve thoroughly enjoyed your recent posts and associated links on distributed lags. I’d like to throw in a slightly different perspective.
 To give you some brief background on myself: I did a PhD in econometrics 1993-1998 at Southampton University. ............ I now manage capital and am heavily influenced by my study of econometrics and in particular exploring the historical foundations of many things that today that look new and funky but are probably old but no less funky!
I wanted to draw attention to the fact that many finance practitioners have long used ‘models’ that in my view are robust and heuristic versions of nonlinear ADL models. I’m not sure this interpretation is as widely recognised as it could be."
With Robert's permission, you can access the full contents of what Robert had to say, here

Robert provides some interesting and useful insights into the connections between certain trading models and ARDL models, and I thought that these would be useful to readers of this blog.

Thanks, Robert!

© 2017, David E. Giles

Sunday, January 8, 2017

When is a Dummy Variable Not a Dummy Variable?

In econometrics we often use "dummy variables", to allow for changes in estimated coefficients when the data fall into one "regime" or another. An obvious example is when we use such variables to allow the different "seasons" in quarterly time-series data.

I've posted about dummy variables several times in the past - e.g., here

However, there's one important point that seems to come up from time to time in emails that I receive from readers of this blog. I thought that a few comments here might be helpful.

Saturday, January 7, 2017

Jagger's Theorem

Recently I watched (for the n'th time!) The Big Chill. If you're a fan of this movie, and its terrific sound-track, then this post will be even more meaningful to you.😊

And if you're reading this because you thought it might be about Mick Jagger, then you won't be disappointed!

Before we go any further, let me make it totally clear that I stole this post's title - I couldn't have made up anything that enticing no matter how hard I tried!

With that confession, let me state Jagger's Theorem, and then I'll explain what this is all about.

Jagger's Theorem:  "You can't always get what you want."

Friday, January 6, 2017

Explaining the Almon Distributed Lag Model

In an earlier post I discussed Shirley Almon's contribution to the estimation of Distributed Lag (DL) models, with her seminal paper in 1965.

That post drew quite a number of email requests for more information about the Almon estimator, and how it fits into the overall scheme of things. In addition, Almon's approach to modelling distributed lags has been used very effectively more recently in the estimation of the so-called MIDAS model. The MIDAS model (developed by Eric Ghysels and his colleagues - e.g., see Ghysels et al., 2004) is designed to handle regression analysis using data with different observation frequencies. The acronym, "MIDAS", stands for "Mixed-Data Sampling". The MIDAS model can be implemented in R, for instance (e.g., see here), as well as in EViews. (I discussed this in this earlier post.)

For these reasons I thought I'd put together this follow-up post by way of an introduction to the Almon DL model, and some of the advantages and pitfalls associated with using it.

Let's take a look.

Thursday, January 5, 2017

Reproducible Research in Statistics & Econometrics

The American Statistical Association has recently introduced reproducibility requirements for articles published in its flagship journal, The Journal of the American Statistical Association.

The following is extracted from p.17 of the July 2016 issue of Amstat News:

Coming from one of the most prestigious statistics journals, this is good news for everyone!

We could do with more of this in the econometrics journals, and in those economics journals that publish empirical studies. 

To that end, I again commend The Replication Network.

© 2017, David E. Giles

Saturday, December 31, 2016

New Year's Reading

New Year's resolution - read more Econometrics!
  • Bürgi, C., 2016. What do we lose when we average expectations? RPF Working Paper No. 2016-013, Department of Economics, George Washington University.
  • Cox, D.R., 2016. Some pioneers of modern statistical theory:A personal reflection. Biometrika, 103, 747-759
  • Golden, R.M., S.S. Henley, H. White, & T.M. Kashner, 2016. Generalized information matrix tests for detecting model misspecification. Econometrics, 4, 46; doi:10.3390/econometrics4040046.
  • Phillips, G.D.A. & Y. Xu, 2016. Almost unbiased variance estimation in simultaneous equations models. Working Paper No. E2016/10, Cardiff Business School, University of Cardiff. 
  • Siliverstovs, B., 2016. Short-term forecasting with mixed-frequency data: A MIDASSO approach. Applied Economics, 49, 1326-1343.
  • Vosseler, A. & E. Weber, 2016. Bayesian analysis of periodic unit roots in the presence of a break. Applied Economics, online.
Best wishes for 2017, and thanks for supporitng this blog!

© 2016, David E. Giles

Thursday, December 29, 2016

Why Not Join The Replication Network?

I've been a member of The Replication Network (TRN) for some time now, and I commend it to you.

I received the End-of-the-Year Update for the TRN today, and I'm taking the liberty of reproducing it below in its entirety in the hope that you may consider getting involved.

Here it is: