Some Recommended Econometrics Reading for March

This month I am suggesting some overview/survey papers relating to a variety of important topics in econometrics:

• Bruns, S. B. & D. I. Stern, 2019. Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models. Empirical Economics, 56, 797-830.
• Casini, A. & P. Perron, 2018. Structural breaks in time series. Forthcoming in Oxford Research Encyclopedia in Economics and Finance. 
• Hendry, D. F. & K. Juselius, 1999. Explaining cointegration analysis: Pat I. Mimeo., Nuffield College, University of Oxford.
• Hendry, D. F. & K. Juselius, 2000. Explaining cointegration analysis: Part II. Mimeo., Nuffield College, University of Oxford.
• Horowitz, J., 2018. Bootstrap methods in econometrics. Cemmap Working Paper CWP53/18. 
• Marmer, V., 2017. Econometrics with weak instruments: Consequences, detection, and solutions. Mimeo., Vancouver School of Economics, University of British Columbia.

© 2019, David E. Giles

Bradley Efron and the Bootstrap

Econometricians make extensive use of various forms of "The Bootstrap", thanks to Bradley (Brad) Efron's pioneering work.

I've posted about the history of the bootstrap previously - e.g., here, and here.

You probably know by now that Brad was awarded The International Prize in Statistics last November - this was only the second time that this prize has been awarded. It's difficult to think of a more deserving recipient.

If you want to read an excellent account of Brad's work, and how the bootstrap came to be, I recommend the 2003 piece by Susan Holmes, Carl Morris, and Rob Tibshirani.

There are some fascinating snippets in this conversation/interview, including:

Efron: "One of the reasons I came to Stanford was because of its humor magazine. I wrote a humor column at Caltech, and I always wanted to write for a humor magazine. Stanford had a great humor magazine, The Chaparral. The first few months I was there, the editor literally went crazy and had to be hospitalized, and so I became editor. For one issue we did a parody of Playboy and it went a little too far. I was expelled from school, ..... I went away for 6 months and then I came back. That was by far the most famous I’ve ever been." 

 Referring to his seminal paper (Efron, 1979):

Tibshirani: "It was sent to the Annals. What kind of reception did it get?" 

Efron: "Rupert Miller was the editor of the Annals at the time. I submitted what was the Rietz lecture, and it got turned down. The associate editor, who will remain nameless, said it that didn’t have any theorems in it. So, I put some theorems in at the end and put a lot of pressure on Rupert, and he finally published it."

I guess there's still hope for the rest of us!

References

Efron, B., 1979. Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1-26.

Holmes, S., C. Morris, & R. Tibshirani, 2003. Bradley Efron: A conversation with good friends. Statistical Science, 18, 268-281.

© 2019, David E. Giles

New Year Reading Suggestions for 2019

With a new year upon us, it's time to keep up with new developments -

• Basu, D., 2018. Can we determine the direction of omitted variable bias of OLS estimators? Working Paper 2018-16, Department of Economics, University of Massachusetts, Amherst.
• Jiang, B., Y. Lu, & J. Y. Park, 2018. Testing for stationarity at high frequency. Working Paper 2018-9, Department of Economics, University of Sydney. 
• Psaradakis, Z. & M. Vavra, 2018. Normality tests for dependent data: Large-sample and bootstrap approaches. Communications in Statistics - Simulation and Computation, online.
• Spanos, A., 2018. Near-collinearity in linear regression revisited: The numerical vs. the statistical perspective. Communications in Statistics - Theory and Methods, online.
• Thorsrud, L. A., 2018. Words are the new numbers: A newsy coincident index of the business cycle. Journal of Business Economics and Statistics, online. (Working Paper version.)
• Zhang, J., 2018. The mean relative entropy: An invariant measure of estimation error. American Statistician, online.

© 2019, David E. Giles

September Reading List

This month's list of recommended reading includes an old piece by Milton Friedman that you may find interesting:

• Broman, K. W. & K. H. Woo, 2017. Data organization in spreadsheets. American Statistician, 72, 2-10.
• Friedman, M., 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675-701.
• Goetz, T. & A. Hecq, 2018. Granger causality testing in mixed-frequency VARs with (possibly) cointegrated processes. MPRA Paper No. 87746.
• Güriş, B., 2018. A new nonlinear unit root test with Fourier function. Communications in Statistics - Simulation and Computation, in press.
• Honoré, B. E. & L. Hu, 2017. Poor (Wo)man's bootstrap. Econometrica, 85, 1277-1301. (Discussion paper version.)
• Peng, R. D., 2018. Advanced Statistical Computing. Electronic resource.

© 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

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

Reading for the Merry Month of May

While you're dancing around the Maypole (or whatever else it is that you get up to), my recommendations are:

• Claeskens, G., J. Magnus, A. Vasnev, and W. Wang, 2014. The forecast combination puzzle: A simple theoretical explanation. Tinbergen Institute Discussion Paper TI 2014 - 127/III. 
• de Jong, R. M. and M. Sakarya, 2013. The econometrics of the Hodrick-Prescott filter. Forthcoming in Review of Economics and Statistics.
• Honoré, B. E. and L. Hu, 2015. Poor (wo)man’s bootstrap. Working Paper 2015-01, Federal Reserve Bank of Chicago.
• King, M. L. and S. Sriananthakumar, 2015. Point optimal testing: A survey of the post 1987 literature. Working Paper 05/15, Department of Econometrics and Business Statistics, Monash University.
• Meintanis, S. G. and E. Tsionas, 2015. Approximately distribution-free diagnostic tests for regressions with survival data. Statistical Theory and Practice, 9, 479-488. 
• Piironen, J. and A. Vehtari, 2015. Comparison of Bayesian predictive methods for model selection. Mimeo.
• Yu, P., 2015. Consistency of the least squares estimator in threshold regression with endogeneity. Economics Letters, 131, 41-46.

© 2015, David E. Giles

March Reading List

Good grief! It's March already. You might enjoy:

Bajari, P., D. Nekipelov, S. P. Ryan, and M. Yang, 2015. Demand estimation with machine learning and model combination. NBER Working Paper No, 20955.

Baur, D. G. and D. T. Tran, 2014. The long-run relationship of gold and silver and the influence of bubbles and financial crises. Empirical Economics, 47, 1525-1541.

Efron, B., 2014. Estimation and accuracy after model selection. Journal of the American Statistical Association, 109, 991-1007.

Kennedy, P. E., 1995. Randomization tests in econometrics. Journal of Business and Economic Statistics, 13, 85-94.

Magnus, J. R., W. Wang, and X. Zhang, 2015. Weighted-average least squares prediction. Econometric Reviews, in press.

Osman, A. F. and M. L. King, 2015. A new approach to forecasting based on exponential smoothing with independent regressors. Working Paper 02/15, Department of Econometrics and Business Statistics, Monash University.

Perron, P. and Y. Yamamoto, 2015. Using OLS to estimate and test for structural change in models with endogenous regressors. Journal of Applied Econometrics, 30, 119-144.

© 2015, David E. Giles

Estimation & Accuracy After Model Selection

This was the title of Brad Efron's invited paper at the 2014 Joint Statistical Meetings in Boston this morning. It was a great presentation, with excellent discussants - Lan Wang, Lawrence Brown, and Soumendra Lahiri.

The paper and discussion are scheduled to appear in the September 2014 issue of JASA.

A lot of what Brad and his discussants had to say related to one of the main points in one of my recent posts. Namely, if you search for a model specification, then this affects all of your subsequent inferences - and usually in a rather complicated way. Typically, even after searching for a preferred model, we tend to "pretend" that we haven't done this, and that the model's form was known from the outset. Naughty! Naughty!

What Brad has done is to address the "pre-test" issue in a rather nice way. You won't be surprised to learn that bootstrapping features heavily in the methodology that he's developed. Using two examples - one non-parametric, and one parametric - he showed how to take account of model selection via Mallows' C_p statistic, and the lasso (respectively), when constructing regression confidence intervals.

One important feature of his analysis involves "smoothing" the results to take account of the discontinuities that inherent in model selection. Although it wasn't mentioned in Brad's talk, these discontinuities are the source of some of the most important problems associated with pre-testing in general. For example, traditional pre-test estimators of regression coefficients (based, say, on a prior test of linear restrictions on those coefficients) are inadmissible under a range of standard loss functions. This inadmissibility is entirely due to the fact that these pre-test estimators are discontinuous functions of the random sample data.

All in all it was a great session, with some nice take-away quotes:

• "The discussants actually discussed my paper."
• "Simulations are hard to do."
• "Model averaging is perfectly easy to do, but model selection is not."

I took some comfort from the last two of these comments!

© 2014, David E. Giles

Summer Reading List

While I'm at the lake, fishing, this is your big chance to get on with some reading.

There won't be a quiz, but I know that you'll thank me for this later on:

• Aberdie, A., S. Athey, G. W. Imbens, and J. Wooldridge, 2014. Finite population standard errors. Mimeo.
• Boero, G., J. Smith, and K. F. Wallis, 2014. The measurement and characteristics of professional forecasters' uncertainty. Journal of Applied Econometrics, in press.
• Liu, C-A., 2014. Distribution theory of the least squares averaging estimator. Journal of Econometrics, in press.
• Manzan, S., 2014. Forecasting the distribution of economic variables in a data-rich environment. Journal of Business and Economic Statistics, in press.
• Sanderson, E. and F. Windmeijer, 2014. A weak instrument F-test in linear IV models with multiple endogenous variables. Discussion Paper 14/644, Department of Economics, University of Bristol.
• Yang, Z., 2014. A general method for third-order bias and variance corrections on a nonlinear estimator. Journal of Econometrics, in press.

© 2014, David E. Giles

The May Reading List

• Bjerkholt, O., 2013. Promoting econometrics through Econometrica 1933-39. Memorandum 28/2013, Department of Economics, University of Oslo.
• Gulesserian, S. G. and M. Kejriwal, 2014. On the power of bootstrap tests for stationarity: A Monte Carlo comparison. Empirical Economics, 46, 973-998.
• Lin, X. et al. (eds.), 2014. Past, Present, and Future of Statistical Science. Chapman and Hall/CRC Press.
• Medel, C. A., 2014. The typical spectral shape of an economic variable: A visual guide. Applied Economics Letters, in press.

In Past, Present, and Future of Statistical Science, I especially recommend:

1. Chapter 8: Bruce G. Lindsay, Developing a passion for statistics.
2. Chapter 19: Mary E. Thompson, Reflections on women in statistics in Canada.
3. Chapter 22: Donald A. S. Fraser, Why does statistics have two theories?
4. Chapter 27: T. W. Anderson, Serial correlation and the Durbin-Watson bounds.
5. Chapter 44: Larry A. Wasserman, Rise of the machines.
6. Chapter 52: Bradley Efron, Thirteen rules.

© 2014, David E. Giles

New Paper

 Another of my papers on analytic bias-correction has now been published. This one is with a former M.A. student, Xiao Ling.

The details are: Xiao Ling and David E. Giles, "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.

You can see the paper here.

© 2014, David E. Giles

An Interview With Bradley Efron

You've all heard about the bootstrap, and you all know that it was Bradley Efron (Statistics, Stanford) who came up with the idea. (If I'm wrong, you can check this earlier post.)

On Twitter yesterday, Joe Blitzstein (Statistics, Harvard; @stat110) drew attention to this great Youtube video interview in which Brad discusses what led him to develop the bootstrap.

I like his opening words when asked how it all came about:

"It's a story about having good colleagues..."

Well, it probably is, but I'd call that a pretty modest response!

Enjoy the video.


© 2014, David E. Giles

My Top 5 For 2013

Everyone seems to be doing it at this time of the year. So, here are the five most popular new posts on this blog in 2013:

1. Econometrics and "Big Data"
2. Ten Things for Applied Econometricians to Keep in Mind
3. ARDL Models - Part II - Bounds Tests
4. The Bootstrap - A Non-Technical Introduction
5. ARDL Models - Part I

Thanks for reading, and for your comments.

Happy New Year!


© 2013, 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

So Much Good Reading........

Here are my latest reading suggestions:

• Choi, I., 2013. Panel Cointegration. Working Paper, Department of Economics, Sogang University, Korea.
• Davidson, R. and J. G. MacKinnon, 2013. Bootstrap tests for overdentification in linear regression models. Economics Department Working Paper No. 1318, Queen's University.
• Deng, A., 2013. Understanding spurious regression in financial econometrics. Journal of Financial Econometrics, in press.
• Feng, C., H. Wang, Y. Han, and Y. Xia, 2013. The mean value theorem and Taylor's expansion in statistics. The American Statistician, in press.
• Kiviet, J. F. and G. D. A. Phillips, 2013. Improved variance estimation of maximum likelihood estimation in stable first-order dynamic regression models. EGC Report No. 2012/06, Division of Economics, Nanyang Technical University.
• Lanne, M., M. Meitz, and P. Saikkonen, 2013. Testing for linear and nonlinear predicatability of stock returns. Journal of Financial Econometrics, 11, 682-705.

© 2013, David E. Giles

The Bootstrap - A Non-Technical Introduction

Computer-intensive methods have become essential to much of statistical analysis, and that includes econometrics. Think of Monte Carlo simulations, MCMC for Bayesian methods, maximum simulated likelihood, empirical likelihood methods, the jackknife, and (of course) the bootstrap.

Although we usually date the bootstrap from Bradley Efron's 1979 paper, as a resampling method it has its roots in earlier, related, contributions including those of Quenouille (1949, 1956).

The main purpose of this post is to draw readers' attention to the piece by Diaconis and Efron (1983) that appeared in Scientific American. It's written for a "general audience", which is nice, and it also provides an interesting snapshot of what was cutting-edge computing 30 years ago. The discussion paper version of the article (including typos) is available here.

As a final bonus, the examples include one from econometrics!


References

Diaconis and B. Efron, 1983. Computer intensive methods in statistics. Scientific American, 248, 116-132.

Efron, B., 1979. Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1-26.

Quenouille, M. H.,1949. Approximate tests of correlation in time series. Journal of the Royal

Statistical Society, Series B, 11, 18-44.

Quenouille, M. H.,1956. Notes on bias in estimation. Biometrika, 61, 353-360.

© 2013, David E. Giles

How Good is Your Random Number Generator?

Simulation methods, including Monte Carlo simulation and various forms of the bootstrap, are widely used by econometricians. We use these tools to learn about the sampling distributions of our estimators and tests, especially in situations where a purely analytic approach is technically difficult.

For example, sometimes we're able to appeal to standard asymptotic (large sample) results - such as the central limit theorems, and the laws of large numbers - to figure out how good our inferences will be if the sample size is very large. However, when it comes to the question of how good they are when the sample size is quite small, the answer may not be so easily established.

In addition, when we come up with a new theoretical result in econometrics, most of us take the precaution of also simulating the result - as check on its accuracy.

Monte Carlo and bootstrap methods rely critically on our ability to generate "pseudo"-random numbers that have the characteristics that we ascribe to them. How often have you actually checked  if the random number generators in your favourite econometrics package produce values that are "random", and follow the distribution that you've asked for? Probably not often enough!

I follow John Cook's blog, The Endeavour. A couple of years ago he had a nice post titled, "How to test a random number generator". In that post, he links to a chapter of the same title that he wrote for the book, Beautiful Testing (edited by Tim Riley and Adam Goucher).

John's chapter is a short, but very valuable read, and I recommend it strongly.

© 2012, David E. Giles

My "Must Read" List

I have to confess that the number of items on my list of papers that I really must read (very soon) is rather large. My excuse is the same as everyone else's - too many papers, too little time. However, here's a small selection of of some of the papers that I've added to that list recently: