"Mastering 'Metrics"

Mastering 'Metrics: The Path from Cause to Effect, by Joshua Angrist and Jörn-Steffen Pischke, is to be published by Princeton University Press later this month. This new book from the authors of Mostly Harmless Econometrics: An Empiricist's Companion is bound to be well received by students and researchers involved in applied empirical economics. My guess is that the biggest accolades will come from those whose interest is in empirical microeconomics.

You can download and preview the Introduction and Chapter 1.

Apparently the book focuses on:

"The five most valuable econometric methods, or what the authors call the Furious Five - random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences."

If this sounds interesting to you, then make sure that you take a look at Peter Dizikes' recent post, "How to Conduct Social Science Research", on the World Economic Forum website.

© 2014, David E. Giles

Advice on Publishing

I've put in a lot of time over the years as an Editor, Associate Editor, or Editorial Board member, for a number of economics and statistics journals, ranging from Journal of Econometrics and Econometric Theory, to Journal of International Trade & Economic Development.  I've also refereed more papers than care to think about. 

Students, rightly, are eager to get the scoop on how to get their work published in good journals. They often talk to me about this. My suggestion would be to read, and follow the advice given by Marc Bellemare in his talk, "How to Publish Academic Papers". 

Just do it!

(HT to David Stern for unwittingly making me aware of Marc's talk,)



© 2014, David E. Giles

More on Prediction From Log-Linear Regressions

My therapy sessions are actually going quite well. I'm down to just one meeting with Jane a week, now. Yes, there are still far too many log-linear regressions being bandied around, but I'm learning to cope with it!

Last year, in an attempt to be helpful to those poor souls I had a post about forecasting from models with a log-transformed dependent variable. I felt decidedly better after that, so I thought I follow up with another good deed.

Let's see if it helps some more:

Statistical Controls Are Great - Except When They're Not!

A blog post today, titled, How Race Discrimination in Law Enforcement Actually Works", caught my eye. Seemed like an important topic. The post, by Ezra Klein, appeared on Vox.

I'm not going to discuss it in any detail, but I think that some readers of this blog will enjoy reading it. Here are a few selected passages, to whet your collective appetite:

"You see it all the time in studies. "We controlled for..." And then the list starts. The longer the better." (Oh boy, can I associate with that. Think of all of those seminars you've sat through.......)

"The problem with controls is that it's often hard to tell the difference between a variable that's obscuring the thing you're studying and a variable that is the thing you're studying."

"The papers brag about their controls. They dismiss past research because it had too few controls." (How many seminars was that?)

"Statistical Controls Are Great - Except When They're Not!"

© 2014, David E. Giles

Here's Your Reading List!

As we count the year down, there's always time for more reading!

• Birg, L. and A. Goeddeke, 2014. Christmas economics - A sleigh ride. Discussion Paper No. 220, CEGE, University of Gottingen.
• Geraci, A., D. Fabbri, and C. Monfardini, 2014. Testing exogeneity of multinomial regressors in count data models: Does two stage residual inclusion work? Working Paper 14/03, Health, Econometrics and Data Group, University of York.
• Li, Y. and D. E. Giles, 2014. Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets. International Journal of Finance and Economics, in press.
• Ma, J. and M. Wohar, 2014. Expected returns and expected dividend growth: Time to rethink an established literature. Applied Economics, 46, 2462-2476. 
• Qin, D., 2014. Resurgence of instrument variable estimation and fallacy of endogeneity. Economics Discussion Papers No. 2014-42, Kiel Institute for the World Economy. 
• Romano, J. P. and M. Wolf, 2014. Resurrecting weighted least squares. Working Paper No. 172, Department of Economics, University of Zurich.
• Tchatoka, F.D., 2014. Specification tests with weak and invalid instruments. Working Paper No. 2014-05, School of Economics, University of Adelaide.

© 2014, David E. Giles

The A. R. Bergstrom Prize, 2015

© 2014, David E. Giles

Thanks for Downloading!

In an earlier post I mentioned a paper that I co-authored with Xiao Ling. The paper is "Bias reduction for the maximum likelihood estimator of the parameters of the generalized Rayleigh family of distributions. Communications in Statistics - Theory and Methods, 2014, 43, 1778-1792.

Over the period January to July 2014, this paper was downloaded 144 times from the journal's website. That made it the 6th most downloaded paper for that period - out of all papers downloaded from all volumes/issues of Communications in Statistics - Theory and Methods.

My guess is that some of you were responsible for this. Thanks!

© 2014, David E. Giles

The Rise of Bayesian Econometrics

A recent discussion paper by Basturk et al. (2014) provides us with (at least) two interesting pieces of material. First, they give a very nice overview of the origins of Bayesian inference in econometrics. This is a topic dear to my heart, given that my own Ph.D. dissertation was in Bayesian Econometrics; and I began that work in early 1973 - just two years after the appearance of Arnold Zellners' path-breaking book (Zellner, 1971).

Second, they provide an analysis of how the associated contributions have been clustered, in terms of the journals in which they have been published. The authors find, among other things, that: 

"Results indicate a cluster of journals with theoretical and applied papers, mainly consisting of Journal of Econometrics, Journal of Business and Economic Statistics, and Journal of Applied Econometrics which contains the large majority of high quality Bayesian econometrics papers."

A couple of the paper coming out of my dissertation certainly fitted into that group - Giles (1975) and Giles and Rayner (1979).

The authors round out their paper as follows:

"...with a list of subjects that are important challenges for twenty-first century Bayesian conometrics: Sampling methods suitable for use with big data and fast, parallelized and GPU, calculations, complex models which account for nonlinearities, analysis of implied model features such as risk and instability, incorporating model incompleteness, and a natural combination of economic modeling, forecasting and policy interventions."

So, there's lots more to be done!


References

Basturk, N., C. Cacmakli, S. P. Ceyhan, and H. K. van Dijk, 2014. On the rise of Bayesian econometrics after Cowles Foundation monographs 10 and 14. Tinbergen Institute Discussion Paper TI 2014-085/III.

Giles, D.E.A., 1975. Discriminating between autoregressive forms: A Monte Carlo comparison of Bayesian and ad hoc methods”, Journal of Econometrics, 3, 229-248.

Giles, D.E.A.and A.C. Rayner, 1979. The mean squared errors of the maximum likelihood and natural-conjugate Bayes regression estimators”, Journal of Econometrics, 11, 319-334.

Zellner, A., 1971. An Introduction to Bayesian Inference in Econometrics. Wiley, New York.

© 2014, David E. Giles

Orthogonal Regression: First Steps

When I'm introducing students in my introductory economic statistics course to the simple linear regression model, I like to point out to them that fitting the regression line so as to minimize the sum of squared residuals, in the vertical direction, is just one possibility.

They see, easily enough, that squaring the residuals deals with the positive and negative signs, and that this prevents obtaining a "visually silly" fit through the data. Mentioning that one could achieve this by working with the absolute values of the residuals provides the opportunity to mention robustness to outliers, and to link the discussion back to something they know already - the difference between the behaviours of the sample mean and the sample median, in this respect.

We also discuss the fact that measuring the residuals in the vertical ("y") direction is intuitively sensible, because the model is purporting to "explain" the y variable. Any explanatory failure should presumably be measured in this direction. However, I also note that there are other options - such as measuring the residuals in the horizontal ("x") direction.

Perhaps more importantly, I also mention "orthogonal residuals". I mention them. I don't go into any details. Frankly, there isn't time; and in any case this is usually the students' first exposure to regression analysis and they have enough to be dealing with. However, I've thought that we really should provide students with an introduction to orthogonal regression - just in the simple regression situation - once they've got basic least squares under their belts. 

The reason is that orthogonal regression comes up later on in econometrics in more complex forms, at least for some of these students; but typically they haven't seen the basics. Indeed, orthogonal regression is widely used (and misused - Carroll and Ruppert, 1966) to deal with certain errors-in-variables problems. For example, see Madansky (1959).

That got me thinking. Maybe what follows is a step towards filling this gap.

Cointegration - The Definitive Overview

Recently released, this discussion paper from Søren Johansen, will give you the definitive overview of cointegration that you've been waiting for.

Titiled simply, "Time Series: Cointegration", Johansen's paper has been prepared for inclusion in the 2nd. edition of The International Encyclopedia of the Social and Behavioural Sciences, 2014. In the space of just sixteen pages, you'll find pretty much everything you need or want to know about cointegration.

To get you started, here's the abstract:

"An overview of results for the cointegrated VAR model for nonstationary I(1) variables is given. The emphasis is on the analysis of the model and the tools for asymptotic inference. These include: formulation of criteria on the parameters, for the process to be nonstationary and I(1), formulation of hypotheses of interest on the rank, the cointegrating relations and the adjustment coefficients. A discussion of the asymptotic distribution results that are used for inference. The results are illustrated by a few examples. A number of extensions of the theory are pointed out."

Enjoy!

© 2014, David E. Giles