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

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

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

Clive Granger Special Issue

The recently published Volume 10, No. 1 issue of the European Journal of Pure and Applied Mathematics takes the form of a memorial issue for Clive Granger. You can find the Table of Contents here, and all of the articles can be downloaded freely.

This memorial issue is co-edited by Jennifer Castle and David Hendry. The contributed papers include ones that deal with Forecasting, Cointegration, Nonlinear Time Series, and Model Selection.

This is a fantastic collection of important survey-type papers that simply must read!

© 2019, David E. Giles

Reading Suggestions for June

Well, here we are - it's June already.

Here are my reading suggestions:

• Abadie, A., S. Athey, G. Imbens, & J. Wooldridge, 2017. When should you adjust standard errors for clustering? Mimeo.
• Berk, R., A. Buja, L. Brown, E. George, A. K. Kuchibhotla, W. Su, & L, Shazo, 2019. Assumption lean regression. American Statistician, in press.
• Ghosh, T., M. Ghosh, & T. Kubokawa, 2019. On the loss robustness of least-square estimators, American Statistician, in press.
• Gustafsson, O. & P. Stockhammar, 2019. Variance stabilizing filters. Communications in Statistics - Theory and Methods, in press.
• Kocherlakota, N. R., 2019. A near-exact finite sample theory for an instrumental variable estimator. Mimeo. (Hat-tip to Frank Diebold.)
• Panagiotelis, A., G. Anathasopoulos, R. J. Hyndman, B. Jiang, & F. Vahid, 2019. Macroeconomic forecasting for Australia using a large number of predictors. International Journal of Forecasting, 35, 613-633.

© 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

Forecasting After an Inverse Hyperbolic Sine Transformation

There are all sorts of good reasons why we sometimes transform the dependent variable (y) in a regression model before we start estimating. One example would be where we want to be able to reasonably assume that the model's error term is normally distributed. (This may be helpful for subsequent finite-sample inference.)

If the model has non-random regressors, and the error term is additive, then a normal error term implies that the dependent variable is also normally distributed. But it may be quite plain to us (even from simple visual observation) that the sample of data for the y variable really can't have been drawn from a normally distributed population. In that case, a functional transformation of y may be in order.

So, suppose that we estimate a model of the form

              f(y_i) = β₁ + β₂ x_i2 + β₃ x_i3 + .... + β_k x_ik + ε_i ;    ε_i ~ iid N[0 , σ²] .                         (1)


where f(.) is usually a 1-1 function, so that f^-1(.) is uniquely defined. Examples include f(y) = log(y), (where, throughout this post, log(a) will mean the natural logarithm of 'a'.); and f(y) = √(y) (if we restrict ourselves to the positive square root).

Having estimated the model, we may then want to generate forecasts of y itself, not of f(y). This is where the inverse transformation, f^-1(y), comes into play.

Forecasting From a Regression with a Square Root Dependent Variable

Back in 2013 I wrote a post that was titled, "Forecasting From Log-Linear Regressions". The basis for that post was the well-known result that if you estimate a linear regression model with the (natural) logarithm of y as the dependent variable, but you're actually interested in forecasting y itself, you don't just report the exponentials of the original forecasts. You need to add an adjustment that takes account of the connection between a Normal random variable and a log-Normal random variable, and the relationship between their means.

Today, I received a query from a blog-reader who asked how the results in that post would change if the dependent variable was the square root of y, but we wanted to forecast the y itself. I'm not sure why this particular transformation was of interest, but let's take a look at the question.

In this case we can exploit the relationship between a (standard) Normal distribution and a Chi-Square distribution in order to answer the question.

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

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

Econometrics Reading for November

In between raking leaves and dealing with some early snow, I've put together this list of suggested reading for you:

• Beckert, W., 2018. A note on specification testing in some structural regression models. Mimeo., Department of Economics, Mathematics and Statistics, Birkbeck College, University of London.
• Clarke, D., 2018. A convenient omitted bias formula for treatment effect models. Economics Letters, in press.
• Liu, Y. & Y. Rho, 2018. On the choice of instruments in mixed frequency specification tests. Mimeo., School of Business and Economics, Michigan Technological University.
• Lütkepohl, H., A. Staszewska-Bystrova, & P. Winker, 2018. Constructing joint confidence bands for impulse functions of VAR models - A review. Lodz Economic Working Paper 4/2018, Faculty of Economics and Sociology, University of Lodz.
• Richardson, A., T. van Florenstein Mulder, & T. Vehbi, 2018. Nowcasting New Zealand GDP using machine learning algorithms.
• Słoczyński, T., 2018. A general weighted average representation of the ordinary and two-stage least squares estimands. Mimeo., Department of Economics, Brandeis University.

© 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

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...........

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

March Reading List

• Annen, K. & S. Kosempel, 2018. Why aid-to-GDP ratios? Discussion Paper 2018-01, Department of Economics and Finance, University of Guelph.
• Conover, W. J., A. J. Guerrero-Serrano, & V. G. Tercero-Gomez, 2018. An update on 'a comparative study of tests for homogeneity of variance'. Journal of Statistical Computation and Simulation, online.
• Foroni, C., M. Marcellino, & D. Stevanović, 2018. Mixed frequency models with MA components. Discussion Paper  No. 02/2018, Deutsche Bundesbank.
• Sen, A., 2018. Lagrange multiplier unit root test in the presence of a break in the innovation variance. Communications in Statistics - Theory and Methods, 47, 1580-1596.
• Stewart, K. G., 2018. Suits' watermelon model: The missing simultaneous equations empirical example. Mimeo., Department of Economics, University of Victoria.
• Weigt, T. & B. Wilfling, 2018. An approach to increasing forecast-combination accuracy through VAR error modeling. Paper 68/2018, Department of Economics, University of Münster.

© 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 September

A little belatedly, here is my September reading list:

• Benjamin, D. J. et al., 2017. Redefine statistical significance. Pre-print.
• Jiang, B., G. Athanasopoulos, R. J. Hyndman, A. Panagiotelis, and F. Vahid, 2017. Macroeconomic forecasting for Australia using a large number of predictors. Working Paper 2/17, Department of Econometrics and Business Statistics, Monash University.
• Knaeble, D. and S. Dutter, 2017. Reversals of least-square estimates and model-invariant estimations for directions of unique effects. The American Statistician, 71, 97-105.
• Moiseev, N. A., 2017. Forecasting time series of economic processes by model averaging across data frames of various lengths. Journal of Statistical Computation and Simulation, 87, 3111-3131.
• Stewart, K. G., 2017. Normalized CES supply systems: Replication of Klump, McAdam and Willman (2007). Journal of Applied Econometrics, in press.
• Tsai, A. C., M. Liou, M. Simak, and P. E. Cheng, 2017. On hyperbolic transformations to normality. Computational Statistics and Data Analysis, 115, 250-266,

© 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

Here's What I've Been Reading

Here are some of the papers that I've been reading recently. Some of them may appeal to you, too:

• Bampinas, G., K. Ladopoulos, &T. Panagiotidis, 2017.  A note on the estimated GARCH coefficients from the S&P1500 universe. WP 17-09, Rimini Centre for Economic Analysis.
• Heberle, J. & C. Sattarhoff, 2017. A fast algorithm for the computation of HAC covariance matrix estimators. Econometrics, 5(1), 9; doi:10.3390/econometrics5010009.
• Kristensen, D. & B. Salanié, 2017. Higher-order properties of approximate estimators. Journal of Econometrics, online.
• Lei, J., M. G'Sell, A. Rinaldo, R. J. Tibshirani, & L. Wasserman, 2017.Distribution-free predictive inference for regression. Journal of the American Statistical Association, online.
• Tsagbey, S., M. De carvalho, & G. L. Page, 2017. All data are wrong, but some are useful? Advocating the need for data auditing. American Statistician, online. 
• Zhang, X. & C-A. Liu, 2017. Inference after model averaging in linear regression models. IEAS Working Paper No. 17-A005.

© 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