October Reading

Here's my latest, and final, list of suggested reading:

• Bellego, C. and L-D. Pape, 2019. Dealing with the log of zero in regression models. CREST Working Paper No. 2019-13.
• Castle, J. L., J. A. Doornik, and D. F. Hendry, 2018. Selecting a model for forecasting. Department of Economics, University of Oxford, Discussion Paper 861.
• Gorajek, A., 2019. The well-meaning economist. Reserve Bank of Australia, Research Discussion Paper RDP 2019-08.
• Güriş, B., 2019. A new nonlinear unit root test with Fourier function. Communications in Statistics - Simulation and Computation, 48, 3056-3062.
• Maudlin, T., 2019. The why of the world. Review of The Book of Why: The New Science of Cause and Effect, by J. Pearl and D. Mackenzie. Boston Review.
• Qian, W., C. A. Rolling, G. Cheng, and Y. Yang, 2019. On the forecast combination puzzle. Econometrics, 7, 39. 

© 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 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

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

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

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

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

In Praise of Two Giants of Econometrics

Two giants in our field, now deceased, are celebrated in recent Working Papers by Peter Phillips and Timo Teräsvirta.

Peter's paper is titled, "Tribute to T. W. Anderson", is in an issue of Econometric Theory that also includes ted's last published research paper.

Timo's paper, which will be appearing in The Journal of Pure and Applied Mathematics, "Sir Clive Granger's contributions to nonlinear time series and econometrics".

Both papers are essential reading, whether you have a particular interest in the history of econometrics; of if you are a younger researcher who wants to understand the building blocks of our discipline.

© 2017, David E. Giles

March Reading List

Here are some suggestions for your reading this month:

• Coble, D. & P. Picheira, 2017. Nowcasting building permits with Google trends. MPRA Paper No. 76514.
• Mullahy, J., 2017. Marginal effects in multivariate probit models. Empirical Economics, 52, 447-461.
• Pagan, A., 2017. Some consequences of using "measurement error shocks" when estimating time series models. CAMA Working Paper 12.2017, Cantre for Macroeconomic Analysis, Australian National University.
• Reed, W. R. & A. Smith, 2017. A time series paradox: Unit root tests perform poorly when data are cointegrated. Economics Letters, 151, 71-74.
• Zhang, L., 2017. Partial unit root and surplus-lag Granger causality testing: A Monte Carlo simulation study. Communications in Statistics - Theory and Methods, online.

© 2017, David E. Giles

December Reading List

Goodness me! November went by really quickly!

• Bagnato, L., L. De Capitani, & A. Punzo, 2016. Testing for serial independence: Beyond the portmanteau approach. American Statistician, in press.
• Aastveit, K.A., C. Foroni, & F. Ravazzolo, 2016. Density forecasts with MIDAS models. Journal of Applied Econometrics, in press.
• Chang, C-L. & M. McAleer, 2016. A simple test for causality in volatility. Discussion Paper TI 2016-094/III, Tinbergen Institute.
• Frank, J. & B. Klar, 2016. Methods to test for equality of two normal distributions. Statistical Methods and Applications, 25, 581-599.
• Gao, J., G. Pan, & Y. Yang, 2016. Estimation of structural breaks in large panels with cross-sectional dependence. Working Paper 12/16, Department of Econometrics and Business Statistics, Monash University.
• Skeels, C.L. & F. Windmeijer, 2016. On the Stock-Yogo tables. Discussion Paper 16/679, Department of Economics, Univesity of Bristol.

 
© 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

Recommended Reading for July

Now that the Canada Day and Independence Day celebrations are behind (some of) us, it's time for some serious reading at the cottage. Here are some suggestions for you:

• Farmer, R. E. A., 2015. The stock market crash really did cause the great recession. Oxford Bulletin of Economics and Statistics, 77, 617-633.
• Franses, P. H., R. Legerstee, and R. Paap, 2016. Estimating loss functions of experts. Applied Economics, in press.
• Hartigan, L., 2016. Alternative HAC covariance matrix estimators with improved finite sample properties. Mimeo., School of Economics, University of New South Wales.
• Harvey, D. I. and S. J. Leybourne, 2016. Improving the length of confidence sets for the date of a break in level and trend when the order of integration is unknown. Economics Letters, in press.
• Noguchi, K. and F. Marmalejo-Ramos, 2016. Assessing the equality of means using the overlap of range-preserving confidence intervals. American Statistician, in press.
• Studer, R. and R. Winkelmann, 2016. Econometric analysis of ratings - With an application to health and wellbeing. SOEP Papers 846.

© 2016, David E. Giles

Econometrics Reading List for June

Here's some suggested reading for the coming month:

• Backhouse, R. and B. Cherrier, 2016. 'It's  computerization, stupid!' The spread of computers and the changing roles of theoretical and applied economics.
• Castle, J. L., M. P. Clements, and D. F. Hendry, 2016. An overview of forecasting facing breaks. Discussion Paper No. 779, Department of Economics, University of Oxford.
• Constantini. M. and A. Sen, 2016. A simple testing procedure for unit root and model specification. Computational Statistics and Data Analysis, 102, 37-54.
• Lutkepohl, H., A. Staszewska-Bystrova, and P. Winker, 2016. Calculating joint confidence bands for impulse response functions using highest density regions. SFB 649 Discussion Paper 2016-017.
• Valadkhani, A., R. Smyth, and B. Mahoney, 2016. Asymmetric causality between Australian inbound and outbound migration tourism flows. Applied Economics, in press.
• Webel, K., 2016. A data-driven selection of an appropriate seasonal adjustment approach. Discussion Paper No. 07/2016, Deutsche Bundesbank.

© 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

End-of-Year Reading

Wishing all readers a very special holiday season!

• Agiakloglou, C., and C. Agiropoulos, 2016. The balance between size and power in testing for linear association for two stationary AR(1) processes. Applied Economics Letters, 23, 230-234.
• Allen, D., M. McAleer, S. Peiris, and A. K. Singh, 2015. Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies. Discussion Paper No. 15-125/III, Tinbergen Institute.
• Basu, D., 2015. Asymptotic bias of OLS in the presence of reverse causality. Working Paper 2015-18, Department of Economics, University of Massachusetts, Amherst.
• Giles, D. E., 2005. Testing for a Santa Claus effect in growth cycles. Economics Letters, 87, 421-426.
• Kim, J., and I Choi, 2015. Unit roots in economic and financial time series: A re-evaluation based on enlightened judgement. MPRA Paper No. 68411.
• Triacca, U., 2015. A pitfall in using the characterization of Granger non-causality in vector autoregressive models. Econometrics, 3, 233-239.       

© 2015, 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

Cointegration & Granger Causality

Today, I had a query from a reader of this blog regarding cointegration and Granger causality. 

Essentially, the email said:

"I tested two economic time-series and found them to be cointegrated. However, when I then tested for Granger  causality, there wasn't any. Am I doing something wrong?"

First of all, the facts:

• If two time series, X and Y, are cointegrated, there must exist Granger causality either from X to Y, or from Y to X, both in both directions.
• The presence of Granger causality in either or both directions between X and Y does not necessarily imply that the series will be cointegrated.

Now, what about the question that was raised?

Truthfully, not enough information has been supplied for anyone to give a definitive answer.

1. What is the sample size? Even if applied properly, tests for Granger non-causality have only asymptotic validity (unless you bootstrap the test).
2. How confident are you that the series are both I(1), and that you should be testing for cointegration in the first place?
3. What is the frequency of the data, and have they been seasonally adjusted? This can affect the unit root tests, cointegration test, and Granger causality test.
4. How did you test for cointegration - the Engle-Granger 2-step approach, or via Johansen's methodology?
5. How did you test for Granger non-causality? Did you use a modified Wald test, as in the Toda-Yamamoto approach?
6. Are there any structural breaks in either of the time-series? These ail likely any or all of the tests that you have performed.
7. Are you sure that you correctly specified the VAR model used for the causality testing, and the VAR model on which Johansen's tests are based (if you used his methodology to test for cointegration)?

The answers to some or all of these questions will contain the key to why you obtained an apparently illogical result.

Theoretical results in econometrics rely on assumptions/conditions that have to be satisfied. If they're not, then don't be surprised by the empirical results that you obtain.

© 2015, David E. Giles

July Reading

Now that the (Northern) summer is here, you should have plenty of time for reading. Here are some recommendations:

• Ahelegbey, D. F., 2015. The econometrics of networks: A review. Working Paper 13/WP/2015, Department of Economics, University of Venice.
• Camba-Mendez, G., G. Kapetanios, F. Papailias, and M. R. Weale, 2015. An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for Euro area economies. Working Paper No. 1773, European Central Bank.
• Cho, J. S., T-H. Kim, and Y. Shin, 2015. Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics, 188, 281-300.
• De Luca, G., J. R. Magnus, and F. Peracchi, 2015. On the ambiguous consequences of omitting variables. EIEF Working Paper 05/15.
• Gozgor, G., 2015. Causal relation between economic growth and domestic credit in the economic globalization: Evidence from the Hatemi-J's test. Journal of International Trade and Economic Development,  24, 395-408.
• Panhans, M. T. and J. D. Singleton, 2015. The empirical economist's toolkit: From models to methods. Working Paper 2015-03, Center for the History of Political Economy.
• Sanderson. E and F. Windmeijer, 2015. A weak instrument F-test in linear IV models with multiple endogenous variables. Discussion Paper 15/644, Department of Economics, University of Bristol.

© 2015, David E. Giles