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.

• Stypka, O., P. Grabarczyk, R. Kawka, and M. Wagner, 2017. "Linear" fully modified OLS estimation of cointegrating polynomial regressions. Discussion Paper Nr. 77/2016, SFB 823. (H/T David Stern)

© 2017, David E. Giles

When Everything Old is New Again

Some ideas are so good that they keep re-appearing again and again. In other words, they stand the test of time, and prove to be useful in lots of different contexts – sometimes in situations that we couldn’t have imagined when the idea first came to light.

This certainly happens in econometrics, and here are just a few examples that come to mind.

Difference-in-Differences With Missing Data

This brief post is a "shout out" for  Irene Botusaru (Economics, Simon Fraser University) who gave a great seminar in our department yesterday.

The paper that she presented (co-authored with Federico Guitierrez), is titled "Difference-in- Differences When the Treatment Status is Observed in Only One Period". So, the title of this post is a bit of an abbreviation of what the paper is really about.

When we conduct DID analysis, we need to be able to classify information about the behaviour/characteristics of survey respondents into a 4-way matrix. Specifically we need to be able to observe the respondents before and after a "treatment"; and in each case we need to know which respondents were treated, and which ones were not.

Usually, a true panel of data, observed at two or more time-periods, facilitates this.

However, what if we simply have repeated cross-sections of data, taken at different time-periods? In this case we aren't necessarily observing exactly the same respondents when we look at the cross-sections for two different time-periods. Typically, in the cross-section after the treatment we'll know which respondents were treated and which ones weren't. However, there will be no way of partitioning the respondents in the pre-treatment cross-section  into "subsequently treated" and "not treated" groups.

Two of the four cells in the matrix of information that we need will be missing, so conventional DID can't be performed.

This is the problem that Irene and Federico consider.

A natural response is introduce some sort of proxy variable(s) to deal with the missing data, and of course this will introduce an estimation bias, even asymptotically. This paper basically takes this approach. The result is a GMM estimation strategy, together with a test that the underlying assumptions are satisfied.

This is a really nice paper - well motivated, technically solid, and with a nice empirical example and application. I urge you to take a look at it if DID is in your econometrics tool-kit (and even if it's not!)

I'm sure that Irene and Federico would appreciate hearing about situations where you've encountered this missing data problem, and how you've responded to it.

© 2016, David E. Giles

Some Weekend Reading

Just what you need - some more interesting reading!

• Al-Sadoon, M. M., 2013. Geometric and long run aspects of Granger causality. Mimeo., Universitat Pompeu Fabra. (Forthcoming in Journal of Econometrics.)
• Barnett, W. A. and I. Kalondo-Kanyama, 2013. Time-varying parameter in the almost ideal demand system and the Rotterdam model: Will the best specification please stand up? Working Paper 335, Econometric Research Southern Africa.
• Delgado, M. S. and C. F. Parmenter, 2013, Embarrassingly easy embarrassingly parallel processing in R. Journal of Applied Econometrics, early view, DOI: 10.1002/jae.2362 .
• Doko Tchatoka, H., 2013. On bootstrap validity for specification tests with weak instruments. Discussion Paper 2013-05, School of Economics and Finance, University of Tasmania.
• Fisher, L. A., H-S. Huh, and A. R. Pagan , 2013, Econometric issues when modelling with a mixture of I(1) and I(0) variables. NCER Working Paper Series, Working Paper #97.
• Pesaran, H. H. and Y. Shin, 1998. Generalized impulse response analysis in linear multivariate models. Economics Letters, 58, 17-29.
• Warr, R. L. and R. A. Erich, 2013. Should the interquartile range divided by the standard deviation be used to assess normality? American Statistician, online, 
  DOI:
  10.1080/00031305.2013.847385 .
• Zhang, X. and X. Shao, 2013, On a general class of long run variance estimators. Economics Letters, 120, 437-441.

© 2013, David E. Giles

Economics Nobel Prize, 2013

The waiting is over - the 2013 Nobel in Economics was announced this morning! Most deservedly, it has been awarded to Eugene F. Fama (U. Chicago), Lars Peter Hansen (U. Chicago), and Robert J. Shiller (Yale U.). The citation says: "For their empirical analysis of asset prices". 

For more details, see here.

It's really  nice to see the recognition of empirical research.

And let's not forget that Hansen gave us GMM estimation; and do you recall Shiller distributed lag models?

© 2013, David E. Giles

Francis Diebold on GMM

On his blog, No Hesitations, Francis Diebold has two recent posts about GMM estimation that students of econometrics, and practitioners, definitely should read.

The first of these posts is here, and the second follow-up post is here.

Enjoy!

© 2013, David E. Giles

What's Your Favourite Estimator?

It's interesting to dwell on the popularity of different estimators that econometricians use. Some estimators are "in vogue" for a period, and then give way to others as new developments come along. Different topics have captured the attention of theoreticians and practitioners alike at different times in history.

Here's a Google Ngram showing the extent to which some familiar estimators for simultaneous equations models have been mentioned in books since 1960:

Not too surprisingly, good old OLS just goes on and on:

I was going to include the GMM estimator in these plots, but this acronym has meanings other than the obvious one that comes to mind. So, the results would have been misleading. To be safe, let's use the full phrase Generalized Method of Moments and allow for case sensitivity:

Interestingly, the phrase appeared in some books before the publication of Hansen's classic 1982 paper.

© 2013, David E. Giles

Finite Sample Properties of GMM

In a comment on a post earlier today,  Stephen Gordon quite rightly questioned the use of GMM estimation with relatively small sample sizes. The GMM estimator is weakly consistent, the "t-test" statistics associated with the estimated parameters are asymptotically standard normal, and the J-test statistic is asymptotically chi-square distributed under the null. But what can be said in finite samples?

Of course, this question applies to almost all of the estimators that we use in practice - IV, MLE, GMM, etc. Indeed, lots of work has been done to explore the finite-sample properties of such estimators. For instance, consider my own work on bias corrections for MLEs (see here, here, and here). So, I'm more than sympathetic to the general point that Stephen made.

Estimating an Euler Equation Using GMM

In one of my grad. econometrics courses we cover Generalized Method of Moments (GMM) estimation. I thought that some readers might be interested in the material that I use for one of the associated lab. classes.

The lab. exercise involves estimating the Euler equation associated with the "Consumption-Based Asset-Pricing Model" (e.g., Campbell, 1993, 1996.) This is a great example for illustrating GMM estimation, because the Euler equation is a natural "moment equation".

The basic statement of the problem is given below, taken from the handout that accompanies the lab. class exercises: