Back to School Reading

Here we are - it's Labo(u)r Day weekend already in North America, and we all know what that means! It's back to school time.

You'll need a reading list, so here are some suggestions:

• Frances, Ph. H. B. F., 2019. Professional forecasters and January. Econometric Institute Research Papers EI2019-25, Erasmus University Rotterdam.
• Harvey, A. & R. Ito, 2019. Modeling time series when some observations are zero. Journal of Econometrics, in press.
• Leamer, E. E., 1978. Specification Searches: Ad Hoc Inference With Nonexperimental Data. Wiley, New York. (This is a legitimate free download.)
• MacKinnon, J. G., 2019. How cluster-robust inference is changing applied econometrics. Working Paper 1413, Economics Department, Queen's University.
• Steel, M. F. J., 2019. Model averaging and its use in economics. Mimeo., Department of Statistics, University of Warwick.
• Stigler, S. M., 1981. Gauss and the invention of least squares. Annals of Statistics, 9, 465-474. 

© 2019, David E. Giles

May Reading List

Here's a selection of suggested reading for this month:

• Athey, S. & G. W. Imbens, 2019. Machine learning methods economists should know about. Mimeo.
• Bhagwat, P. & E. Marchand, 2019. On a proper Bayes but inadmissible estimator. American Statistician, online.
• Canals, C. & A. Canals, 2019. When is n large enough? Looking for the right sample size to estimate proportions. Journal of Statistical Computation and Simulation, 89, 1887-1898.
• Cavaliere, G. & A. Rahbek, 2019. A primer on bootstrap testing of hypotheses in time series models: With an application to double autoregressive models. Discussion Paper 19-03, Department of Economics, University of Copenhagen.
• Chudik, A. & G. Geogiardis, 2019. Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables. Globalization Institute Working Paper 356, Federal Reserve Bank of Dallas.
• Reschenhofer, E., 2019. Heteroscedasticity-robust estimation of autocorrelation. Communications in Statistics - Simulation and Computation, 48, 1251-1263.

© 2019, David E. Giles

A World Beyond p < 0.05

The American Statistical Association has just published a special supplementary issue of The American Statistician, titled Statistical Inference in the 21st. Century: A World Beyond p < 0.05.

This entire issue is open-access. In addition to an excellent editorial, Moving to a World Beyond "p < 0.05" (by Ronald Wasserstein, Allen Schirm, and Nicole Lazar) it comprises 43 articles with such titles as:

• The p-Value Requires Context, Not a Threshold (by Rebecca Betensky)
• The False Positive Risk: A Proposal Concerning What to do About p-Values (by David Colquhoun)
• What Have we (Not) Learnt From Millions of Scientific Papers With P Values? (by John Ioannidis)
• Three Recommendations for Improving the Use of p-Values (by Daniel Benjamin and James Berger)

I'm sure that you get the idea of what this supplementary issue is largely about.

But look back at its title - Statistical Inference in the 21st. Century: A World Beyond p < 0.05. It's not simply full of criticisms. There's a heap of excellent, positive, and constructive material in there.

Highly recommended reading!

© 2019, David E. Giles

Controlling My Heating Bill Using Bayesian Model Averaging

Where we live, in rural Ontario, we're not connected to "natural gas". Our home furnace runs on propane, and a local supplier sends a tanker to refill our propane tanks on a regular basis during the colder months.

Earlier this month we had to make a decision regarding our contract with the propane retailer. Should we opt for a delivery price that can vary, up or down, throughout the coming fall and winter; or should we "lock in" at a fixed delivery price for the period from October to May of next year?

Now, I must confess that my knowledge of the propane industry is slight, to say the least. I decided that a basic analysis of the historical propane price data might provide some insights to assist in making this decision. It also occurred to me, after doing this, that the analysis that I went through might be of interest to readers, as a simple exercise in forecasting using Bayesian model averaging.

Here are the details...........

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 - just click on the numbers below.

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

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

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

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

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. Goméz, 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

Read Some Econometrics this Month!

There are no April Fool's tricks in the following list of suggestions. 😐

• Antoine, B. and E. Renault, 2017. On the relevance of weaker instruments. Econometric Reviews, online.
• Golan, A. and A. Ullah, 2017. Interval estimation: An information theoretic approach. Econometric Reviews, online.
• Hendry, D. F. and P. C. B. Phillips, 2017. John Denis Sargan at the London School of Economics. Cowles Foundation Discussion Paper No. 2082, Yale University.
• Ozonur, D., H. T. K. Akdur, & H. Bayrak, 2016. Comparisons of tests of distributional assumption in Poisson regression model. Communications in Statistics - Simulation and Computation, online.
• Vijayamohanan, P. N., 2017. How do you interpret your regression coefficients? MPRA Paper No. 76867.
• Witmer, J., 2017. Bayes and MCMC for undergraduates. American Statistician, online.

© 2017, David E. Giles

Vintage Years in Econometrics - The 1970's

Continuing on from my earlier posts about vintage years for econometrics in the 1930's, 1940's, 1950's, 1960's, here's my tasting guide for the 1970's.

Once again, let me note that "in econometrics, what constitutes quality and importance is partly a matter of taste - just like wine! So, not all of you will agree with the choices I've made in the following compilation."

Explaining the Almon Distributed Lag Model

In an earlier post I discussed Shirley Almon's contribution to the estimation of Distributed Lag (DL) models, with her seminal paper in 1965.

That post drew quite a number of email requests for more information about the Almon estimator, and how it fits into the overall scheme of things. In addition, Almon's approach to modelling distributed lags has been used very effectively more recently in the estimation of the so-called MIDAS model. The MIDAS model (developed by Eric Ghysels and his colleagues - e.g., see Ghysels et al., 2004) is designed to handle regression analysis using data with different observation frequencies. The acronym, "MIDAS", stands for "Mixed-Data Sampling". The MIDAS model can be implemented in R, for instance (e.g., see here), as well as in EViews. (I discussed this in this earlier post.)

For these reasons I thought I'd put together this follow-up post by way of an introduction to the Almon DL model, and some of the advantages and pitfalls associated with using it.

Let's take a look.

New Year's Reading

New Year's resolution - read more Econometrics!

• Bürgi, C., 2016. What do we lose when we average expectations? RPF Working Paper No. 2016-013, Department of Economics, George Washington University.
• Cox, D.R., 2016. Some pioneers of modern statistical theory:A personal reflection. Biometrika, 103, 747-759
• Golden, R.M., S.S. Henley, H. White, & T.M. Kashner, 2016. Generalized information matrix tests for detecting model misspecification. Econometrics, 4, 46; doi:10.3390/econometrics4040046.
• Phillips, G.D.A. & Y. Xu, 2016. Almost unbiased variance estimation in simultaneous equations models. Working Paper No. E2016/10, Cardiff Business School, University of Cardiff. 
• Siliverstovs, B., 2016. Short-term forecasting with mixed-frequency data: A MIDASSO approach. Applied Economics, 49, 1326-1343.
• Vosseler, A. & E. Weber, 2016. Bayesian analysis of periodic unit roots in the presence of a break. Applied Economics, online.

Best wishes for 2017, and thanks for supporitng this blog!

© 2016, David E. Giles

Stephen E. Fienberg, 1942-2016

The passing of Stephen Fienberg today is another huge loss for the statistics community. Carnegie Mellon University released this obituary this morning.

Steve was born and raised in Toronto, and completed his undergraduate training in mathematics and statistics at the University of Toronto before moving to Harvard University for his Ph.D.. His contributions to statistics, and to the promotion of statistical science, were immense.

As the CMU News noted:

"His many honors include the 1982 Committee of Presidents of Statistical Society President's Award for Outstanding Statistician Under the Age of 40; the 002 ASA Samuel S. Wilks Award for his distinguished career in statistics; the first Statistical Society of Canada's Lise Manchester Award in 2008 to recognize excellence in state-of-the-art statistical work on problems of public interest; the 2015 National Institute of Statistical Sciences Jerome Sacks Award for Cross-Disciplinary Research; the 2015 R.A. Fisher Lecture Award from the Committee of Presidents of Statistical Societies and the ISBA 2016 Zellner Medal. 

Fienberg published more than 500 technical papers, brief papers, editorials and discussions.  He edited 19 books, reports and other volumes and co-authored seven books, including 1999's "Who Counts? The Politics of Census-Taking in Contemporary America," which he called "one of his proudest achievements." " 

There at least three terrific interviews with Steve that we have to remind us of the breadth of his contributions:

• Interview With Steve Fienberg, by Sam Behseta and Aleksandra Slavkovic.
• A Conversation With Stephen E. Fienberg, by Miron Straf and Judith Tanur.
• "In most places, not only are statisticians not in control of Big Data efforts and data science, but sometimes they are totally excluded or at best, marginalised." An interview with Stephen Fienberg, in Statistics Views.

© 2016, David E. Giles

November Reading

You'll see that this month's reading list relates, in part, to my two recent posts about Ted Anderson and David Cox.

• Acharya, A., M. Blackwell, & M. Sen, 2015. Explaining causal findings without bias: Detecting and assessing direct effects. RWP15-194, Harvard Kennedy School.
• Anderson, T.W., 2005. Origins of the limited information maximum likelihood and two-stage least squares estimators. Journal of Econometrics, 127, 1-16.
• Anderson, T.W. & H. Rubin, 1949. Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, 20, 46-63.
• Cox, D.R., 1972. Regression models and life-tables (with discussion). Journal of the Royal Statistical Society B, 34, 187–220.
• Malsiner-Walli, G. & H. Wagner, 2011. Comparing spike and slab priors for Bayesian variable selection. Austrian Journal of Statistics, 40, 241-264.
• Psaradakis, Z. & M. Vavra, 2016. Portmanteau tests for linearity of stationary time series. Working Paper 1/2016, National Bank of Slovakia.

© 2016, David E. Giles

Choosing Between the Logit and Probit Models

I've had quite a bit say about Logit and Probit models, and the Linear Probability Model (LPM), in various posts in recent years. (For instance, see here.) I'm not going to bore you by going over old ground again.

However, an important question came up recently in the comments section of one of those posts. Essentially, the question was, "How can I choose between the Logit and Probit models in practice?"

I responded to that question by referring to a study by Chen and Tsurumi (2010), and I think it's worth elaborating on that response here, rather than leaving the answer buried in the comments of an old post.

So, let's take a look.

Graduate Econometrics Exam

Occasionally readers ask about the exams that I set in my graduate econometrics courses.

The elective graduate econometrics course that I taught this past semester was one titled "Themes in Econometrics". The topics that are covered vary from year to year. However, as the title suggests, the course focuses on broad themes that arise in econometrics. Examples might include maximum likelihood estimation and the associated testing strategies;instrumental variables/GMM estimation; simulation methods; nonparametric inference; and Bayesian inference.

This year most of the course was devoted to maximum likelihood, and Bayesian methods in econometrics.

The mid-term test covered the first of these two thematic topics, while the final exam was devoted largely to Bayesian inference.

You can find the mid-term test here. The final exam question paper is here; and the associated R code is here.

© 2016, David E. Giles

May Reading List

Here's my reading list for May:

• Hayakawa, K., 2016. Unit root tests for short panels with serially correlated errors. Communications in Statistics - Theory and Methods, in press.
• Hendry, D. F. and G. E. Mizon, 2016. Improving the teaching of econometrics. Discussion Paper 785, Department of Economics, University of Oxford.
• Hoeting, J. A., D. Madigan, A. E. Raftery, and C. T. Volinsky, 1999. Bayesian model averaging: A tutorial (with comments and rejoinder). Statistical Science, 14, 382-417. 
• Liu, J., D. J. Nordman, and W. Q. Meeker, 2016. The number of MCMC draws needed to compute Bayeian credible bounds. American Statistician, in press.
• Lu, X., L. Su, and H. White, 2016. Granger causality and structural causality in cross-section and panel data. Working Paper No, 04-2016, School of Economics, Singapore Management University.
• Nguimkeu, P., 2016.  An improved selection test between autoregressive and moving average disturbances in regression models. Journal of Time Series Econometrics, 8, 41-54.

© 2016, David E. Giles

March Reading List

Now is a good time to catch up on some Econometrics reading. Here are my suggestions for this month:

• Carrasco, M. and R. Kotchoni, 2016. Efficient estimation using the characteristic function. Econometric Theory, in press.
• Chambers, M. J., 2016. The estimation of continuous time models with mixed frequency data. Discussion Paper No. 777, Department of Economics, University of Essex.
• Cuaresma, J. C., M. Feldkircher, and F. Huber, 2016. Forecasting with global vector autoregressive models: A Bayesian approach. Journal of Applied Econometrics, in press.
• Hendry, D., 2016. Deciding between alternative approaches in macroeconomics. Discussion Paper No. 778, Department of Economics, University of Oxford.
• Reed, W. R., 2016. Univariate unit root tests perform poorly when data are cointegrated. Working Paper No. 1/2016, Department of Economics and Finance, University of Canterbury.

© 2016, David E. Giles

November Reading

Somewhat belatedly, here is some suggested reading for this month:

• Al-Sadoon, M. M., 2015. Testing subspace Granger causality. Barcelona GSE Working Paper Series, Working Paper nº 850.
• Droumaguet, M., A. Warne, & T. Wozniak, 2015. Granger causality and regime influence in Bayesian Markov-switching VAR's. Department of Economics, University of Melbourne. 
• Foroni, C., P. Guerin, & M. Marcellino, 2015. Using low frequency information for predicting high frequency variables. Working Paper 13/2015, Norges Bank.
• Hastie, T., R. Tibshirani, & J. Friedman, 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd. ed.). Springer, New York. (Legitimate download.) 
• Hesterberg, T. C., 2015. What teachers should know about the bootstrap: Resampling in the undergraduate statistics curriculum. American Statistician, in press. 
• Quineche. R. & G. Rodríguez, 2015. Data-dependent methods for the lag selection in unit root tests with structural change. Documento de Trabajo No. 404, Departmento de Economía, Pontificia Universidad Católica del Perú.

© 2015, David E. Giles