Monday, July 31, 2017

My August Reading List

Here are some suggestions for you:
  • Calzolari, G., 2017. Econometrics exams and round numbers: Use or misuse of indirect estimation methods? Communications in Statistics - Simulation and Computation, in press.
  • Chakraborti, S., F. Jardim, & E. Epprecht, 2017. Higher order moments using the survival function: The alternative expectation formula. American Statistician, in press.
  • Clarke, J. A., 2017. Model averaging OLS and 2SLS: An application of the WALS procedure. Econometrics Working Paper EWP1701, Department of Economics, University of Victoria.
  • Hotelling, H., 1940. The teaching of statistics, Annals of Mathematical Statistics, 11, 457-470.
  • Knaeble, B. & S. Dutter, 2017. Reversals of least-square estimates and model-invariant estimation for directions of unique effects. American Statistician, 71, 97-105.
  • Megerdichian, A., 2017. Further results on interpreting coefficients in regressions with a logarithmic dependent variable. Journal of Econometric Methods, in press.

© 2017, David E. Giles

Wednesday, July 12, 2017

The Bandwidth for the KPSS Test

Recently, I received an email from a follower of this blog, who asked:
"May I know what is the difference between the bandwidth of Newey-West and Andrews for the KPSS test. It is because when I test the variable with Newey-West, it is I(2), but then I switch the bandwidth to Andrews, it becomes I(1)."
First of all, it's worth noting that the unit root and stationarity tests that we commonly use can be very sensitive to the way in which they're constructed and applied. An obvious example arises with the choice of the maximum lag length when we're using the Augmented Dickey-Fuller test. Another example would be the treatment of the drift and trend components when using that test, So, the situation that's mentioned in the email above is not unusual, in general terms.

Now, let's look at the specific question that's been raised here.

Saturday, July 1, 2017

Canada Day Reading List

I was tempted to offer you a list of 150 items, but I thought better of it!

  • Hamilton, J. D., 2017. Why you should never use the Hodrick-Prescott filter. Mimeo., Department of Economics, UC San Diego.
  • Jin, H. and S. Zhang, 2017. Spurious regression between long memory series due to mis-specified structural breaks. Communications in Statistics - Simulation and Computation, in press.
  • Kiviet, J. F., 2016. Testing the impossible: Identifying exclusion restrictions.Discussion Paper 2016/03, Amsterdam School of Economics, University of Economics.
  • Lenz, G. and A. Sahn, 2017. Achieving statistical significance with covariates. BITSS Preprint (H/T  Arthur Charpentier)
  • Sephton, P., 2017. Finite sample critical values of the generalized KPSS test. Computational Economics, 50, 161-172.
© 2017, David E. Giles

Monday, June 26, 2017

Recent Developments in Cointegration

Recently, I posted about a special issue of the journal, Econometrics, devoted to "Unit Roots and Structural Breaks".

Another recent special issue of that journal will be of equal interest to readers of this blog. Katerina Juselius has guest- edited an issue titles, "Recent Developments in Cointegration". The papers published so far in this issue are, of course, open-access. Check them out!

© 2017, David E. Giles

Sunday, June 25, 2017

Instrumental Variables & the Frisch-Waugh-Lovell Theorem

The so-called Frisch-Waugh-Lovell (FWL) Theorem is a standard result that we meet in pretty much any introductory grad. course in econometrics.

The theorem is so-named because (i) in the very fist volume of Econometrica Frisch and Waugh (1933) established it in the particular context of "de-trending" time-series data; and (ii) Lovell (1963) demonstrated that the same result establishes the equivalence of "seasonally adjusting" time-series data (in a particular way), and including seasonal dummy variables in an OLS regression model. (Also, see Lovell, 2008.)

We'll take a look at the statement of the FWL Theorem in a moment. First, though, it's important to note that it's purely an algebraic/geometric result. Although it arises in the context of regression analysis, it has no statistical content, per se.

What's not generally recognized, however, is that the FWL Theorem doesn't rely on the geometry of OLS. In fact, it relies on the geometry of the Instrumental Variables (IV) estimator - of which OLS is a special case, of course. (OLS is just IV in the just-identified case, with the regressors being used as their own instruments.)

Implicitly, this was shown in an old paper of mine (Giles, 1984) where I extended Lovell's analysis to the context of IV estimation. However, in that paper I didn't spell out the generality of the FWL-IV result.

Let's take a look at all of this.

Friday, June 23, 2017

Unit Roots & Structural Breaks

The open-access journal, Econometrics (of which I'm happy to be an Editorial Board member), has recently published a special issue on the topic of "Unit Roots and Structural Breaks". 

This issue is guest-edited by Pierre Perron, and it includes eight really terrific papers. You can find the special issue here.

© 2017, David E. Giles

Wednesday, June 7, 2017

Marc Bellemare on "How to Publish in Academic Journals"

If you don't follow Marc Bellemare's blog, you should do.

And if you read only one other blog post this week, it should be this one from Marc, titled, "How to Publish in Academic Journals". Read his slides that are linked in the post.

Great advice that is totally applicable to anyone doing research in econometrics - theory or applied.

© 2017, David E. Giles

Saturday, June 3, 2017

June Reading List

Here are some suggestions for you:
  • Ai, C. and E. C. Norton, 2003. Interaction terms in logit and probit models. Economics Letters, 80, 123-129.
  • Hirschberg, J. and J. Lye, 2017. Inverting the indirect - the ellipse and the Boomerang: Visualizing the confidence intervals of the structural coefficient from two-stage least squares. Journal of Econometrics, in press.
  • Kim, I. and S. Park, 2017. Likelihood ratio tests for multivariate normality. Communications in Statistics - Theory and Methods, in press.
  • Knotek, E. S. and S. Zaman, 2017. Financial nowcasts and their usefulness in macroeconomic forecasting. Working Paper 17-02, Federal Reserve Bank of Cleveland.
  • Marczak, M. and V. Gom├ęz, 2017. Monthly US business cycle indicators: A new multivariate approach based on a band-pass filter. Empirical Economics, 52, 1379-1408.
  • Sherwood, C. and D. W. Kwak, 2017. New insights into an old problem - enhancing student learning outcomes in an introductory statistics course. Applied Economics, in press.
© 2017, David E. Giles

Tuesday, May 23, 2017

Staying on Top of the Literature

Recently, 'Michael' placed the following comment on one of my posts:
"Thanks for sharing this interesting list of articles! I'm wondering, how do you go about finding these types of articles to read? Are you a subscriber to these publications/do you regularly check for new updates online? I'd like to start keeping more up to date with academic articles, but I'm not sure where to start." 
Well, that's a good question, Michael. And I'm sure that there are many undergraduate students and non-academics who wonder the same thing when it comes to keeping up with the latest developments in econometrics. (I've phrased it that way because I'm also sure that grad. students will be getting appropriate advice on this, and other matters from their supervisors.)

Let's take a step back in time first.


Friday, May 19, 2017

The EViews Blog on ARDL - Part 3

As I mentioned in this recent post, the EViews team had a third blog post on ARDL modelling up their sleeves. The said post appeared a few days ago, here.

It's a real gem! The flow-chart and the detailed application are fabulous - I wish I could have come up with this myself.

Read it, read it................

© 2017, David E. Giles