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?


Friday, December 15, 2017

Reading for the Holidays

Here are some suggestions for your Holiday reading:
  • Athey, S. and G. Imbens, 2016. The state of econometrics - Causality and policy evaluation. Mimeo., Graduate School of Business, Stanford University.
  • Cook, J. D., 2010. Testing a random number generator. Chapter 10, in T. Rily and A. Goucher (eds.), Beautiful Testing, O' Reilly Media, Sebastol, CA. 
  • Ivanov, V. and L. Kilian, 2005. A practitioner's guide to lag order selection for VAR impulse response analysis. Studies in Nonlinear Dynamics and Econometrics, 9, article 2.
  • Polanin, J. A., E. A. Hennessy, and E. E. Tanner-Smith, 2016. A review of meta-analysis packages in R. Journal of Educational and Behavioural Statistics, 42, 206-242.
  • Young, A., 2017. Consistency without inference: Instrumental variables in practical application. Mimeo.,  London School of Economics.
  • Zhang, L., 2017, Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study. Communications in Statistics - Theory and Methods, 46, 12317-12323.

© 2017, David E. Giles

Sunday, November 5, 2017

Econometrics Reading List for November

Some suggestions........

  • Garcia, J. and D. E. Ramirez, 2017. The successive raising estimator and its relation with the ridge estimator. Communications in Statistics - Theory and Methods, 46, 11123-11142.
  • Silva, I. R., 2017. On the correspondence between frequentist and Bayesian tests. Communications in Statistics - Theory and Methods, online.
  • Steel, M. F. J., 2017. Model averaging and its use in economics. MPRA Paper No. 81568.
  • Teräsvirta, T., 2017. Nonlinear models in macroeconometrics. CREATES Research Paper 2017-32.
  • Witmer, J., 2017. Bayes and MCMC for undergraduates. American Statistician, 71, 259-274.
  • Zimmerman, C., 2015. On the need for a replication journal. Federal Reserve Bank of St. Louis, Working Paper 2015-016A.
© 2017, David E. Giles

Sunday, October 22, 2017

Another Shout-Out for The Replication Network

Replication in empirical economics is vitally important, and I'm delighted to be a member of The Replication Network. I've mentioned this group in previous blog posts - for instance, here and here.

The list of members of TRN continues to grow - why not consider becoming a member your self? Here's the link that you need to do so. 

The TRN website includes some excellent guest blog posts, the latest of which is about a new journal dedicated to the replication of economic research. The post is by  Martina Grunow, the Managing Editor of the International Journal for Re-Views in Empirical Economics (IREE).

If you haven't checked out TRN, why not do so - and why not join?

© 2017, David E. Giles

Wednesday, October 4, 2017

Recommended Reading for October

  • Andor, N. & C. Parmeter, 2017. Pseudolikelihood estimation of the stochastic frontier model. Ruhr Economic Papers #693.
  • Chalak, K., 2017. Instrumental variables methods with heterogeneity and mismeasured instruments. Econometric Theory, 33, 69-104.
  • Kim, J. H. & I. Choi, 2017. Unit roots in economic and financial time series: A re-evaluation at the decision-based significance levels. Econometrics, 56 (3), 41.
  • Owen, A. B., 2017. Statistically efficient thinning of a Markov chain sampler. Journal of Computational and Graphical Statistics, 26, 738-744. 
  • Owen, P. D., 2017. Evaluating ingenious instruments for fundamental determinants of long-run economic growth and development. Econometrics, 5 (3), 38.
  • Richard, P., 2017. Robust heteroskedasticity-robust tests. Economics Letters, 159, 28-32.

© 2017, David E. Giles