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

June Reading List

• Andrews, I. and T. B. Armstrong, 2015. Unbiased instrumental variables estimation under known first-stage sign. Cowles Foundation Discussion Paper No. 1984R, Yale University,
• Bajari, P., D. Nekipelov, S. P. Ryan, and M. Yang. 2015. Demand estimation with machine learning and model combination. NBER Working Paper 20955.
• Chambers, M. J., 2015. A jackknife correction to a test for cointegration rank. Econometrics, 3, 355-375.
• Mazeu, J. H. G., E. Ruiz and H. Veiga, 2015. Model uncertainty and the forecast accuracy of ARMA models: A survey. UC3M Working Paper 15-08, Statistics and Econometrics, Universidad Carlos III de Madrid. 
• Paldam, M., 2015. Meta-analysis in a nutshell: Techniques and general findings. Economics, 9, 2015-11.
• 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