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

More Sandwiches, Anyone?

Consider this my Good Deed for the Day!

A re-tweet from a colleague whom I follow on Twitter brought an important paper to my attention. I thought I'd share it more widely.

The paper is titled, "Small-sample methods for cluster-robust variance estimation and hypothesis testing in fixed effect models", by James Pustejovski (@jepusto) and Beth Tipton (@stats-tipton). It appears in The Journal of Business and Economic Statistics.  

You can tell right away, from its title, that this paper is going to be a must-read for empirical economists. And note the words, "Small-sample" in the title - that sounds interesting.

 Here's a compilation of Beth's six tweets:

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

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."

Some Suggested Reading for October

For your enjoyment:

• Diebold, F. X. & M. Shin, 2016. Assessing point forecast accuracy by stochastic error distance. NBER Working Paper No.2516.
• Franses, P.H., 2016. Yet another look at MIDAS regression. Econometric Institute Report 2016-32.
• Hillier, G. & F. Martellosio, 2016. Exact properties of the maximum likelihood estimator in spatial autoregressive models. Discussion Paper DP 07/16, Department of Economics, University of Surrey.
• Li, L., M.J. Holmes, & B.S. Lee, 2016. The asymmetric relationship between executive earnings management and compensation: A panel threshold regression approach. Applied Economics, 48, 5525-5545. 
• Lütkepohl, H., A. Staszewska-Bystrova, & P. Winker, 2016. Calculating joint confidence bands for impulse response functions using highest density regions. MAGKS Joint Discussion Paper 16-2016.
• Segnon, M., R. Gupta, S. Bekiros, & M.E. Wohar, 2016. Forecasting U.S. GNP growth: The role of uncertainty. Working Paper 2016-67, Department of Economics, University of Pretoria.

© 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

April Reading

April 1 already - time to update your reading list. Here are some suggestions:

• Baek, Y. I., J. S. Cho, and P. C. B. Phillips, 2015. Testing linearity using power transforms of regressors. 2015-RWP79, Economic Research Institute, Yonsei University.
• Gol, A. and D. E. Giles, 2015. HotGimmer: Random information. Working paper.
• Herwartz, H., N. Klein, and C. Strumann, 2015. Modelling hospital admission and length of stay by means of generalized count data models. Journal of Applied Econometrics, in press.
• Kabaila, P., R. Mainzer, and D. Farchione, 2015. The impact of a Hausman pretest, applied to panel data, on the coverage probability of confidence intervals. Economics Letters, in press.
• Lyhagen, J. and S. Ekberg, 2015. Beating the VAR: Improving Swedish GDP forecasts using error and intercept corrections. Journal of Forecasting, in press.
• Nadarajah, S., 2015. On the computation of Gauss hypergeometric functions. The American Statistician, in press.\
• Zhang, X. and A. Ullah, 2015. Group model averaging for finite sample size. Working Paper, Department of Economics, University of California, Riverside.

© 2015, David E. Giles

Recent Reading

From my "Recently Read" list:

• Born, B. and J. Breitung, 2014. Testing for serial correlation in fixed-effects panel data models. Econometric Reviews, in press.
• Enders, W. and Lee. J., 2011. A unit root test using a Fourier series to approximate smooth breaks, Oxford Bulletin of Economics and Statistics, 74, 574-599.
• Götz, T. B. and A. W. Hecq, 2014. Testing for Granger causality in large mixed-frequency VARs. RM/14/028, Maastricht University, SBE, Department of Quantitative Economics.
• Kass, R. E., 2011. Statistical inference: The big picture. Statistical Science, 26, 1-9.
• Qian, J. and L. Su, 2014. Structural change estimation in time series regressions with endogenous variables. Economics Letters, in press.
• Wickens, M., 2014. How did we get to where we are now? Reflections on 50 years of macroeconomic and financial econometrics. Discussion Paper No. 14/17, Department of Economics and Related Studies, University of York.

© 2014, David E. Giles

October Reading

October already!

• Chauvel, C. and J. O'Quigley, 2014. Tests for comparing estimated survival functions. Biometrika, 101, 535-552. 
• Choi, I., 2014. Unit root tests for dependent and heterogeneous micropanels. Discussion Paper No. 2014-04, Research Institute for Market Economy, Sogang University.
• Cho, J. S. and H. White, 2014. Testing the equality of two positive-definite matrices with application to in formation matrix testing. Discussion Paper, School of Economics,Yonsei University.
• Hansen, B. E., 2013. Model averaging, asymptotic risk, and regressor groups. Quantitative Economics, in press.
• Miller, J. I., 2014. Simple robust tests for the specification of high-frequency predictors of a low-frequency series. Mimeo., Department of Economics, University of Missouri.
• Owen, A. B. and P. A. Roediger, 2014. The sign of the logistic regression coefficient. American Statistician, in press.
• Westfall, P. H., 2014. Kurtosis as peakedness, 1905-2014. R.I.P.. American Statistician, 68, 191-195.

© 2014, David E. Giles

March Madness in the Reading Department

It's time for the monthly round-up of recommended reading material.

• Gan, L. and J. Jiang, 1999. A test for global maximum. Journal of the American Statistical Association, 94, 847-854.
• Nowak-Lehmann, F., D. Herzer, S. Vollmer, and I. Martinez-Zarzosa, 2006. Problems in applying dynamic panel data models: Theoretical and empirical findings. Discussion Paper Nr. 140, IAI, Georg-August-Universität Göttingen.
• Olive, D. J., 2004. Does the MLE maximize the likelihood? Mimeo., Department of Mathematics, Southern Illinois University. 
• Pollock, D. S. G., 2014. Econometrics: An historical guide for the uninitiated. Working Paper No. 14/05, Department of Economics, University of Leicester.
• Terrell, G. R., 2002. The gradient statistic. Interface 2002: Computing Science and Statistics, Vol. 34.
• Wald, A., 1940. The fitting of straight lines if both variables are subject to error. Annals of Mathematical Statistics, 11, 284-300.

© 2014, David E. Giles

Vintage Years in Econometrics - The 1960's

Remember that saying - "if you can remember the 60's you probably weren't there"? Well, with that said, and continuing from my earlier posts about vintage years for econometrics in the 1930's, 1940's, and 1950's, here's my take on the 1960'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."

An Interesting New Book

Here's a new book that looks as if it will be interesting, and I'm looking forward to reading it myself: Panel Data Analysis Using Eviews, writen by I Gusti Ngurah Agung. Two other related books by this author have been published perviously - see here.

I'll give my opinion in more detail at a later date.

© 2014, David E. Giles

Reading for the New Year

Back to work, and back to reading:

• Basturk, N., C. Cakmakli, S. P. Ceyhan, and H. K. van Dijk, 2013. Historical developments in Bayesian econometrics after Cowles Foundation monographs 10,14. Discussion Paper 13-191/III, Tinbergen Institute.
• Bedrick, E. J., 2013. Two useful reformulations of the hazard ratio. American Statistician, in press.
• Nawata, K. and M. McAleer, 2013. The maximum number of parameters for the Hausman test when the estimators are from different sets of equations.  Discussion Paper 13-197/III, Tinbergen Institute.
• Shahbaz, M, S. Nasreen, C. H. Ling, and R. Sbia, 2013. Causality between trade openness and energy consumption: What causes what  high, middle and low income countries. MPRA Paper No. 50832. 
• Tibshirani, R., 2011. Regression shrinkage and selection via the lasso: A retrospective. Journal of the Royal Statistical Society, B, 73, 273-282.
• Zamani, H. and N. Ismail, 2014. Functional form for the zero-inflated generalized Poisson regression model. Communications in Statistics - Theory and Methods, in press.

© 2014, David E. Giles

Specification Testing for Panel Data Models

Recently, I received a query from Rolf Lyneborg Lund who asked for references to material on specification testing in the context of panel data models. After I responded to Rolf. it occurred to me that these references might also be of interest to others.

Going beyond the standard Hausman test for random versus fixed effects, here are some general references that may be helpful:

• Baltagi, B. H., 1998. Panel data methods. In A. Ullah and D. E. A. Giles (eds.), Handbook of Applied Economic Statistics, Marcel Dekker, New York.
• Baltagi, B. H., 1999. Specification tests in panel data models using artificial regressions. Annales d'Économie et des Statistiques, 55-56, 277-298.
• Lee, Y-J., 2005. Specification testing for functional forms in dynamic panel data models.
• Metcalf, G. E., 1996. Specification testing in panel data with instrumental variables. Journal of Econometrics, 71, 291-307.
• Park, H. M., 2011. Practical guides to panel data modeling: A step by step analysis using stata. Public Management and Policy Analysis Program, Graduate School of International Relations, International University of Japan.

In addition, there's quite an extensive literature on testing for unit roots and cointegration in panel data models. I won't attempt to summarize this literature here, but a useful, recent, summary is provided by:

• Chen, M-Y., 2013. Panel unit root and cointegration tests. Mimeo., Department of Finance, National Chung Hsing University.

© 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

Some Recent, and Transparently Applicable, Results in Time-Series Econometrics

I think most of us would agree that when new techniques are introduced in econometrics, it's often a bit of a challenge to see exactly what would be involved in applying them. Someone comes up with a new estimator or test, and it's often a while before it gets incorporated into our favourite econometrics package, or until someone puts together an expository piece that illustrates, in simple terms, how to put the theory into practice.

In part, that's why applied econometrics "lags behind" econometric theory. Another reason is that a lot of practitioners aren't interested in reading the latest theoretical paper themselves.

Fair enough!

In any event, it's always refreshing when new inferential procedures are introduced into the literature in a way that exhibits a decent degree of "transparency" with respect to their actual application. For those of you who like you keep up with recent developments in time-series econometrics, here are some good examples of recent papers that (in my view) score well on the "transparency index":

Last Week's Reading

There are some great econometrics papers out there, just waiting to be read. I need more hours in the day!

Some of the papers I enjoyed reading last week were:

• Barsoum, F. and S. Stankiewicz, 2013. Forecasting GDP growth using mixed-frequency models with switching regimes. Department of Economics, University of Konstanz, Working Paper 2013-10.
• Castle, J. L., M. P. Clements, and D. F. Hendry, 2013. Forecasting by factors, by variables, by both or neither? Journal of Econometrics, in press.
• Chiu, C. W., B. Eraker, A. T. Foerster, T. B. Kim, and H. D. Seoane, 2012. Estimating VAR's sampled at mixed or irregular spaced frequencies: A Bayesian approach. Federal Reserve Bank of Kansas City, Research Working Paper 11-11 (revised, December 2012).
• Dufour, J-M. and J. Wilde, 2013. Weak identification in probit models with endogenous covariates.
• Lim, H. K., J. Song, and B. C. Jung, 2013. Score tests for zero-inflation and overdispersion in two-level count data. Computational Statistics and Data Analysis, 61, 67-82.
• Millimet, D. L. and I. K. McDonough, 2013. Dynamic panel data models with irregular spacing: With applications to early childhood development. IZA Discussion Paper 7359.
• Pesaran, H. H., A. Pick, and M. Pranovich, 2013. Optimal forecasts in the presence of structural breaks. Journal of Econometrics, in press.

© 2013, David E. Giles

Granger Causality Testing With Panel Data

Some of my previous posts on testing for Granger causality (for example, here, here, and here) have drawn quite a lot of interest. That being the case, I'm sure that readers of this blog will enjoy reading a new paper by two of my colleagues, and a former graduate student of theirs.

The paper, by Weichun Chen, Judith Clark, and Nilanjana Roy is titled "Health and Wealth: Short Panel Granger Causality Tests for Developing Countries". Here's the abstract of their paper: