Wednesday, April 25, 2018

April Reading

Very belatedly, here is my list of suggested reading for April:
  • Biørn, E., 2017. Identification, instruments, omitted variables, and rudimentary models: Fallacies in the "experimental approach" to econometrics. Memorandum No. 13/2017, Department of Economics, Oslo University.
  • Chambers, M. J., and M. Kyriacou, 2018. Jackknife bias reduction in the presence of a near-unit root. Econometrics, 6, 11.
  • Derryberry, D., K. Aho, J. Edwards, and T. Peterson, 2018. Model selection and regression t-statistics. American Statistician, in press.
  • Mitchell, J., D. Robertson, and S. Wright, 2018. R2 bounds for predictive models: What univariate properties tell us about multivariate predictability. Journal of Business and Economic Statistics, in press. (Free download here.)
  • Parker, T., 2017. Finite-sample distributions of the Wald, likelihood ratio, and Lagrange multiplier test statistics in the classical linear model. Communications in Statistics - Theory and Methods, 46, 5195-5202.
  • Troster, V., 2018. Testing Granger-causality in quantiles. Econometric Reviews, 37, 850-866.

© 2018, David E. Giles

Monday, March 19, 2018

The (Undergraduate) (Econo) Metrics Game

In a comment on my recent post about the long-running Econometrics Game for graduate student teams, "BJH" kindly pointed out the existence of a counterpart for undergraduate econometrics students.

The "Metrics Game" is a two-day competition organised by OEconomica in association with the University of Chicago’s Department of Economics and the Becker Friedman Institute. 

The 2018 competition is the fourth in the series, and gets underway on 7 April at the University of Chicago.

It's great to see competitions of this type being made available for students at all levels of study.

© 2018, David E. Giles

Sunday, March 18, 2018

The Econometric Game, 2018

Readers of this blog will be familiar with The Econometric Game. You'll find my posts about the 2016 and 2017 Games here, and here the first of those posts links to ones about the Games from previous years.

The Econometric Game is a competition between teams of graduate students in econometrics. It's organised by the study association for Actuarial Science, Econometrics & Operational Research (VSAE) of the University of Amsterdam, and it has been a terrific success.

The Econometric Game has been held annually since 1999. This year, 30 teams have been chosen to compete in the Games, which will be held in Amsterdam from 11 to 13 of April. The theme for this year's competition is "Econometrics of Happiness".

The winners in both 2016 and 2017 were teams representing Harvard University. Let's see how they perform this year. I'll have some follow-up posts once the Game gets underway next month.

© 2018, David E. Giles

Wednesday, February 21, 2018

March Reading List

  • Annen, K. & S. Kosempel, 2018. Why aid-to-GDP ratios? Discussion Paper 2018-01, Department of Economics and Finance, University of Guelph.
  • Conover, W. J., A. J. Guerrero-Serrano, & V. G. Tercero-Gomez, 2018. An update on 'a comparative study of tests for homogeneity of variance'. Journal of Statistical Computation and Simulation, online.
  • Foroni, C., M. Marcellino, & D. Stevanović, 2018. Mixed frequency models with MA components. Discussion Paper  No. 02/2018, Deutsche Bundesbank.
  • Sen, A., 2018. Lagrange multiplier unit root test in the presence of a break in the innovation variance. Communications in Statistics - Theory and Methods, 47, 1580-1596.
  • Stewart, K. G., 2018. Suits' watermelon model: The missing simultaneous equations empirical example. Mimeo., Department of Economics, University of Victoria.
  • Weigt, T. & B. Wilfling, 2018. An approach to increasing forecast-combination accuracy through VAR error modeling. Paper 68/2018, Department of Economics, University of Münster.
© 2018, David E. Giles

Sunday, February 11, 2018

Recommended Reading for February

Here are some reading suggestions:
  • Bruns, S. B., Z. Csereklyei, & D. I. Stern, 2018. A multicointegration model of global climate change. Discussion Paper No. 336, Center for European, Governance and Economic Development Research, University of Goettingen.
  • Catania, L. & S. Grassi, 2017. Modelling crypto-currencies financial time-series. CEIS Tor Vegata, Research Paper Series, Vol. 15, Issue 8, No. 417.
  • Farbmacher, H., R. Guber, & J. Vikström, 2018. Increasing the credibility of the twin birth instrument. Journal of Applied Econometrics, online.
  • Liao, J. G. & A. Berg, 2018. Sharpening Jensen's inequality. American Statistician, online.
  • Reschenhofer, E., 2018. Heteroscedasticity-robust estimation of autocorrelation. Communications in Statistics - Simulation and Computation, online.

© 2018, David E. Giles

Saturday, February 10, 2018

Economic Goodness-of-Fit

What do we mean by a "significant result" in econometrics?

The distinction between "statistical significance" and "economic significance" has received a good deal of attention in the literature. And rightly so.

Think about the estimated coefficients in a regression model, for example. Putting aside the important issue of the choice of a significance level when considering statistical significance, we all know that results that are significant in the latter sense may or may not be 'significant' when their economic impact is considered.

Marc Bellemare provided a great discussion of this in his blog a while back.

Here, I want to draw attention to a somewhat related issue - distinguishing between the statistical and economic overall goodness-of-fit of an economic model.

Thursday, February 8, 2018

ASA Symposium on Statistical Inference - Recorded Sessions

In October of last year, the American Statistical Association held a two-day Symposium on Statistical Inference in Bethesda, MD.

The symposium was sub-titled, Scientific Method for the 21st. Century: A World Beyond p < 0.05. That gives you some idea of what it was about.

The ASA has now released video recordings of several of the sessions at the symposium, and you can find them here.

The video sessions include:

"Why Is Eliminating P-Values So Hard? Reflections on Science and Statistics." (Steve Goodman)

"What Have We (Not) Learnt from Millions of Scientific Papers with P-Values?" (John Ioannidis)

"Understanding the Needs for Statistical Evidence of Decision-Makers in Medicine." (Madhu Mazumdar, Keren Osman, & Elizabeth Garrett-Mayer) 

"Statisticians: Sex Symbols, Liars, Both, or Neither?" (Christie Aschwanden, Laura Helmuth, & Aviva Hope Rutkin) 

"The Radical Prescription for Change." (Andrew Gelman, Marcia McNutt, & Xiao-Li Meng)

Closing Session: “Take the Mic”

The videos are stimulating and timely. I hope that you enjoy them.

© 2018, David E. Giles

Saturday, February 3, 2018

Bayesian Econometrics Slides

Over the years, I included material on Bayesian Econometrics in various courses that I taught - especially at the grad. level. I retired from teaching last year, and I thought that some of you might be interested in the slides that I used when I taught a Bayesian Econometrics topic for the last time.

I hope that you find them useful.

1. General Background
2. Constructing Prior Distributions
3. Properties of Bayes Estimators and Tests
4. Bayesian Inference for the Linear Regression Model
5. Bayesian Computation
6. More Bayesian Computation 
7. Acceptance-Rejection Sampling
8. The Metropolis-Hastings Algorithm
9. Model Selection - Theory
10. Model Selection - Applications
11. Consumption Function Case Study
© 2018, David E. Giles

Tuesday, January 2, 2018

Econometrics Reading for the New Year

Another year, and lots of exciting reading!
  • Davidson, R. & V. Zinde-Walsh, 2017. Advances in specification testing. Canadian Journal of Economics, online.
  • Dias, G. F. & G. Kapetanios, 2018. Estimation and forecasting in vector autoregressive moving average models for rich datasets. Journal of Econometrics, 202, 75-91.  
  • González-Estrada, E. & J. A. Villaseñor, 2017. An R package for testing goodness of fit: goft. Journal of Statistical Computation and Simulation, 88, 726-751.
  • Hajria, R. B., S. Khardani, & H. Raïssi, 2017. Testing the lag length of vector autoregressive models:  A power comparison between portmanteau and Lagrange multiplier tests. Working Paper 2017-03, Escuela de Negocios y EconomÍa. Pontificia Universidad Católica de ValaparaÍso.
  • McNown, R., C. Y. Sam, & S. K. Goh, 2018. Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50, 1509-1521.
  • Pesaran, M. H. & R. P. Smith, 2017. Posterior means and precisions of the coefficients in linear models with highly collinear regressors. Working Paper BCAM 1707, Birkbeck, University of London.
  • Yavuz, F. V. & M. D. Ward, 2017. Fostering undergraduate data science. American Statistician, online. 

© 2018, David E. Giles

Monday, January 1, 2018

Interpolating Statistical Tables

We've all experienced it. You go to use a statistical table - Standard Normal, Student-t, F, Chi Square - and the line that you need simply isn't there in the table. That's to say the table simply isn't detailed enough for our purposes.

One question that always comes up when students are first being introduced to such tables is:
"Do I just interpolate linearly between the nearest entries on either side of the desired value?"
Not that these exact words are used, typically. For instance, a student might ask if they should take the average of the two closest values. How should you respond?