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

July Reading

This month my reading list is a bit different from the usual one. I've taken a look back at past issues of Econometrica and Journal of Econometrics, and selected some important and interesting papers that happened to be published in July issues of those journals.

Here's what I came up with for you:

• Aigner, D., C. A. K. Lovell, & P. Schmidt, 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21-37.
• Chow, G. C., 1960. Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591-605.
• Davidson, R. & J. G. MacKinnon, 1984. Convenient specification tests for logit and probit models. Journal of Econometrics, 25, 241-262.
• Dickey, D. A. & W. A. Fuller, 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072.
• Granger, C. W. J. & P. Newbold,  1974. Spurious regressions in econometrics. Journal of Econometrics, 2, 111-120.
• Sargan, J. D., 1961. The maximum likelihood estimation of economic relationships with autoregressive residuals. Econometrica, 29, 414-426. 

© 2019, David E. Giles

More Tributes to Clive Granger

As a follow-up to my recent post, "Clive Granger Special Issue", I received an email from Eyüp Çetin (Editor of the European Journal of Pure and Applied Mathematics).

Eyüp kindly pointed out that "......... actually, we published the first special issue dedicated to his memory exactly on 27 May 2010, the first anniversary of his passing at https://www.ejpam.com/index.php/ejpam/issue/view/11 

We think this was the first special issue dedicated to his memory in the world. The Table of Contents may be found here https://www.ejpam.com/index.php/ejpam/issue/view/11/showToc .

Another remarkable point that we also published some personal and institutional tributes and some memorial stories for Sir Granger that never appeared elsewhere before at 

https://www.ejpam.com/index.php/ejpam/article/view/805 .

Some institutions such as Royal Statistical Society, Japan Statistical Society and University of Canterbury have sent their tributes to this special volume." 

© 2019, David E. Giles

Some April Reading for Econometricians

Here are my suggestions for this month:

• Hyndman, R. J., 2019. A brief history of forecasting competitions. Working Paper 03/19, Department of Econometrics and Business Statistics, Monash University.
• Kuffner, T. A. & S. G. Walker, 2019. Why are p-values controversial?. American Statistician, 73, 1-3.
• Sargan, J. D.,, 1958. The estimation of economic relationships using instrumental variables. Econometrica, 26, 393-415. (Read for free online.)  
• Sokal, A. D., 1996. Transgressing the boundaries: Towards a trasnformative hermeneutics of quantum gravity. Social Text, 46/47, 217-252.
• Zeng, G. & Zeng, E., 2019. On the relationship between multicollinearity and separation in logistic regression. Communications in Statistics - Simulation and Computation, published online.
• Zhang, X., S. Paul, & Y-G. Yang, 2019. Small sample bias correction or bias reduction? Communications in Statistics - Simulation and Computation, published online.

© 2019, David E. Giles

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

February Reading

Now that Groundhog Day is behind us, perhaps we can focus on catching up on our reading?

• Deboulets, L. D. D., 2018. A review on variable selection in regression. Econometrics, 6(4), 45.
• Efron, B. & C. Morris, 1977. Stein's paradox in statistics. Scientific American, 236(5), 119-127.
• Khan, W. M. & A. u I. Khan, 2018. Most stringent test of independence for time series. Communications in Statistics - Simulation and Computation, online.
• Pedroni, P., 2018. Panel cointegration techniques and open challenges. Forthcoming in Panel Data Econometrics, Vol. 1: Theory, Elsevier.
• Steel, M. F., J., 2018. Model averaging and its use in economics. MPRA Paper No. 90110.
• Tay, A. S. & K. F. Wallis, 2000. Density forecasting: A survey. Journal of Forecasting, 19, 235-254.

© 2019, David E. Giles

December Reading for Econometricians

My suggestions for papers to read during December:

• Askanazi, R., F. X. Diebold, F. Schorfheide, & M. Shin, 2018. On the comparison of interval forecasts. PIER Working Paper 18-013, Penn. Institute for Economic Research, University of Pennsylvania.
• Meintanis, S. G., J. Ngatchou-Wandji, & J. Allison, 2018. Testing for serial independence in vector autoregressive models, Statistical Papers, 59, 1379-1410.
• Milas, C., T. Panagiotidis, & T. Dergiades, 2018. Twitter versus traditional news media: Evidence for the sovereign bond markets. Mimeo., Management School, University of Liverpool.
• Ollech, D., 2018. Seasonal adjustment of daily time series. Discussion Paper No. 41/2018, Deutsche Bundesbank.
• Sickles, R. C., W. Song, & V. Zelenyuk, 2018. Econometric analysis of productivity: Theory and implementation in R. Working Paper WP08/2018, Centre for Efficiency and productivity Analysis, Department of Economics, University of Queensland.
• Skeels, C. L. & F. Windmeijer, 2018. On the Stock-Yogo tables. Econometrics, 6 (4), 44.

© 2018, David E. Giles

A New Canadian Macroeconomic Database

Anyone who's undertaken empirical macroeconomic research relating to Canada will know that there are some serious data challenges that have to be surmounted.

In particular, getting access to long-term, continuous, time series isn't as easy as you might expect.

Statistics Canada has been criticized frequently over the years by researchers who find that crucial economic series are suddenly "discontinued", or are re-defined in ways that make it extremely difficult to splice the pieces together into one meaningful time-series.

In recognition of these issues, a number of efforts have been made to provide Canadian economic data in forms that researchers need. These include, for instance, Boivin et al. (2010), Bedock and Stevanovic (2107), and Stephen Gordon's on-going "Project Link".

Thanks to Olivier Fortin-Gagnon, Maxime Leroux, Dalibor Stevanovic, &and Stéphane Suprenant we now have an impressive addition to the available long-term Canadian time-series data. Their 2018 working paper, "A Large Canadian Database for Macroeconomic Analysis", discusses their new database and illustrates its usefulness in a variety of ways.

Here's the abstract:

"This paper describes a large-scale Canadian macroeconomic database in monthly frequency. The dataset contains hundreds of Canadian and provincial economic indicators observed from 1981. It is designed to be updated regularly through (the) StatCan database and is publicly available. It relieves users to deal with data changes and methodological revisions. We show five useful features of the dataset for macroeconomic research. First, the factor structure explains a sizeable part of variation in Canadian and provincial aggregate series. Second, the dataset is useful to capture turning points of the Canadian business cycle. Third, the dataset has substantial predictive power when forecasting key macroeconomic indicators. Fourth, the panel can be used to construct measures of macroeconomic uncertainty. Fifth, the dataset can serve for structural analysis through the factor-augmented VAR model."

Note - these are monthly data! And they're freely available. Although the paper doesn't appear to provide the source for accessing the data, Dalibor kindly pointed out to me that there's a download link here, on his webpage. This link will give you the data in spreadsheet form, together with all of the necessary background information.

The only slight concern that I have about this resource - and I don't want to sound ungrateful - is the issue of the updating of the data over time. You'll note from the abstract that the database "...... is designed to be updated regularly through (the) StatCan database....". Given my comments (above) about some of the issues that we've all faced for a very long time when it comes to StatCan data, I  know that updating this new database on a regular basis is going to be a bit of a challenge.

Added 8 March 2019: I'm glad to learn that new update of the database is now available here.

However, let's not let this concern detract from the considerable benefits that we'll all derive from having access to this rich set of Canadian macroeconomic time-series.

Thanks, again, to the authors for constructing this database, and for making it freely available!

References

Bedock, N. & D. Stevanovic, 2017. An empirical study of credit shock transmission in a small open economy. Canadian Journal of Economics, 50, 541–570.

Boivin, J., M. Giannoni, & D. Stevanovic, 2010. Monetary transmission in a small open economy: more data, fewer puzzles. Technical report, Columbia Business School, Columbia University.

Fortin-Gagnon, O., M. Leroux, D. Stevanovic, & S. Suprenant, 2018. A large Canadian database for macroeconomic analysis. CIRANO Working Paper 2018s-25.

Gordon, S., 2018. Project Link - Piecing together Canadian economic history. Département d'économique, Université Laval.

© 2018, David E. Giles

Essential Fall Reading

• Buono, D., G. Kapetanios, M. Marcellino, G. Mazzi, & F. Papailias, 2018. Big data econometrics - Now casting and early estimates. Working paper N. 82, Baffi Carefin Centre for Applied Research on International Markets, Banking, Finance, and Regulation, Bocconi University.
• Fair, R. C., 2018. Information content of DSGE forecasts. Mimeo
• Lewbel, A., 2018. The identification zoo - Meanings of Identification. Forthcoming, Journal of Economic Literature.
• Pretis, F., J. J. Reade, & G. Sucarrat, 2018. Automated general-to-specific (GETS) regression modeling and indicator saturation for outliers and structural breaks. Journal of Statistical Software, 86, 3.
• Woodruff, R. S., 1971. A simple method for approximating the variance of a complicated estimate. Journal of the American Statistical Association, 66, 411-414.
• Zhang, R. & N. H. Chan, 2018. Portmanteau-type tests for unit-root and cointegration. Journal of Econometrics, in press.

© 2018, 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

Recommended Reading

Here's my reading list for August:

• Ardia, D., K. Bluteau, & L. F. Hoogerheide, 2018. Methods for computing numerical standard errors: Review and application to value-at-risk estimation. Journal of Time Series Econometrics. Available online.
• Bauer, D. & A. Maynard, 2012. Persistence-robust surplus-lag Granger causality testing. Journal of Econometrics, 169. 293-300.
• David, H. A., 2009. A historical note on zero correlation and independence. American Statistician, 63, 185-186.
• Fisher, T. J. & M. W. Robbins, 2018. A cheap trick to improve the power of a conservative hypothesis tests. American Statistician. Available online.
• Granger, C. W. J., 2012. Useful conclusions from surprising results. Journal of Econometrics, 169, 142-146.
• Harville, D. A., 2014. The need for more emphasis on prediction: A 'nondenominational' model-based approach (with discussion). American Statistician, 68, 71-92.

© 2018, David E. Giles

Some Reading Suggestions for July

Some summertime reading:

• Chen, T., DeJuan, J., & R. Tian, 2018. Distributions of GDP across versions of  the Penn World Tables: A functional data analysis approach. Economics Letters, in press. 
• Clements, K.W., H. Liu, & Y. Tarverdi, 2018. Alcohol consumption, censorship and misjudgment. Applied Economics, online
• Jin, H., S. Zhang, J. Zhang,& H. Hao, 2018. Modified tests for change points in variance in the possible presence of mean breaks. Journal of Statistical Computation and Simulation, online
• Pata, U.K., 2018. The Feldstein Horioka puzzle in E7 countries: Evidence from panel cointegration and asymmetric causality analysis. Journal of International Trade and Economic Development, online.
• Sen, A., 2018. A simple unit root testing methodology that does not require knowledge regarding the presence of a break. Communications in Statistics - Simulation and Computation, 47, 871-889.
• Wright, T., M. Klein, &K. Wieczorek, 2018. A primer on visualizations for comparing populations, including the issue of overlapping confidence intervals. American Statistician, online.

© 2018, David E. Giles

Suggested Reading for June

• Colussi, T., 2018. Social ties in academia: A friend is a treasure. Review of Economics and Statistics, 100, 45-50.
• Dette, H., K. Möllenhoff, S. Volgushev, & F. Bretz, 2018. Equivalence of regression curves. Journal of the American Statistical Association, online.
• Kumbhaker, S. C., C. F. Parmeter, & V. Zelenyuk, 2018. Stochastic frontier analysis: Foundations and advances. Working paper No. WP02/2018, Centre for Efficiency and productivity Analysis, School of Economics, University of Queensland.
• Morshed, A. A., E. Andreou, & O. Boldea, 2018. Structural break tests robust to regression misspecification. Econometrics, 6, 27.
• Zhang, L., 2018. Spurious regressions with high-order models: A reconsideration. Economics Letters, 168, 70-72.
• Zhao, S., A. Ullah, & X. Zhang, 2018. A class of model averaging estimators. Working Paper Series 18-11, Rimini Centre for Economic Analysis.

© 2018, David E. Giles

The Uniqueness of the Cointegrating Vector

Suppose that we have (only) two non-stationary time-series, X_1t and X_2t (t = 1, 2, 3, .....). More specifically, suppose that both of these series are integrated of order one (i.e., I(1)). Then there are two possibilities - either X₁ and X₂ are cointegrated, or they aren't.

You'll recall that if they are cointegrated, then there is a linear combination of X₁ and X₂ that is stationary. Let's write this linear combination as Z_t = (X_1t + αX_2t). (We can normalize the first "weight" to the value "one" without any loss of generality.) The vector whose elements are 1 and α is the so-called "cointegrating vector".

You may be aware that if such a vector exists, then it is unique.

Recently, I was asked for a simple proof of this uniqueness. Here goes.........

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

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

The Bandwidth for the KPSS Test

Recently, I received an email from a follower of this blog, who asked:

"May I know what is the difference between the bandwidth of Newey-West and Andrews for the KPSS test. It is because when I test the variable with Newey-West, it is I(2), but then I switch the bandwidth to Andrews, it becomes I(1)."

First of all, it's worth noting that the unit root and stationarity tests that we commonly use can be very sensitive to the way in which they're constructed and applied. An obvious example arises with the choice of the maximum lag length when we're using the Augmented Dickey-Fuller test. Another example would be the treatment of the drift and trend components when using that test, So, the situation that's mentioned in the email above is not unusual, in general terms.

Now, let's look at the specific question that's been raised here.

Canada Day Reading List

I was tempted to offer you a list of 150 items, but I thought better of it!

• Hamilton, J. D., 2017. Why you should never use the Hodrick-Prescott filter. Mimeo., Department of Economics, UC San Diego.

• Jin, H. and S. Zhang, 2017. Spurious regression between long memory series due to mis-specified structural breaks. Communications in Statistics - Simulation and Computation, in press.

• Kiviet, J. F., 2016. Testing the impossible: Identifying exclusion restrictions.Discussion Paper 2016/03, Amsterdam School of Economics, University of Economics.

• Lenz, G. and A. Sahn, 2017. Achieving statistical significance with covariates. BITSS Preprint (H/T  Arthur Charpentier)

• Sephton, P., 2017. Finite sample critical values of the generalized KPSS test. Computational Economics, 50, 161-172.

• Stypka, O., P. Grabarczyk, R. Kawka, and M. Wagner, 2017. "Linear" fully modified OLS estimation of cointegrating polynomial regressions. Discussion Paper Nr. 77/2016, SFB 823. (H/T David Stern)

© 2017, David E. Giles

Recent Developments in Cointegration

Recently, I posted about a special issue of the journal, Econometrics, devoted to "Unit Roots and Structural Breaks".

Another recent special issue of that journal will be of equal interest to readers of this blog. Katerina Juselius has guest- edited an issue titles, "Recent Developments in Cointegration". The papers published so far in this issue are, of course, open-access. Check them out!

© 2017, David E. Giles

Unit Roots & Structural Breaks

The open-access journal, Econometrics (of which I'm happy to be an Editorial Board member), has recently published a special issue on the topic of "Unit Roots and Structural Breaks". 

This issue is guest-edited by Pierre Perron, and it includes eight really terrific papers. You can find the special issue here.

© 2017, David E. Giles