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

February Reading

Here are some suggestions for your reading list this month:

• Aastveit, A., C. Foroni, and F. Ravazzolo, 2016. Density forecasts with midas models. Journal of Applied Econometrics, online.
• Chang, C-L. and M. McAleer, 2016.  The fiction of full BEKK. Tinbergen Institute Discussion Paper TI 2017-015/III.
• Chudik, A., G. Kapetanios, and M.H. Pesaran, 2016.  A one-covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models. Cambridge Working Paper Economics: 1667.
• Kleiber, C.. Structural change in (economic) time series WWZ Working Paper 2016/06, University of Basel.
• Romano, J. P. and M. Wolf, 2017. Resurrecting weighted least squares. Journal of Econometrics, 197, 1-19.
• Yamada, H., 2017. Several least squares problems related to the Hodrick-Prescott filtering. Communications in Statistics - Theory and Methods, online.

© 2016, David E. Giles

Here's Your Reading List!

As we count the year down, there's always time for more reading!

• Birg, L. and A. Goeddeke, 2014. Christmas economics - A sleigh ride. Discussion Paper No. 220, CEGE, University of Gottingen.
• Geraci, A., D. Fabbri, and C. Monfardini, 2014. Testing exogeneity of multinomial regressors in count data models: Does two stage residual inclusion work? Working Paper 14/03, Health, Econometrics and Data Group, University of York.
• Li, Y. and D. E. Giles, 2014. Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets. International Journal of Finance and Economics, in press.
• Ma, J. and M. Wohar, 2014. Expected returns and expected dividend growth: Time to rethink an established literature. Applied Economics, 46, 2462-2476. 
• Qin, D., 2014. Resurgence of instrument variable estimation and fallacy of endogeneity. Economics Discussion Papers No. 2014-42, Kiel Institute for the World Economy. 
• Romano, J. P. and M. Wolf, 2014. Resurrecting weighted least squares. Working Paper No. 172, Department of Economics, University of Zurich.
• Tchatoka, F.D., 2014. Specification tests with weak and invalid instruments. Working Paper No. 2014-05, School of Economics, University of Adelaide.

© 2014, David E. Giles

New Working Paper

Yanan Li (a former graduate student) and I have just released a new Working Paper. It's titled, "Modelling Volatility Spillover Effects Between Developed Stock Markets and Developing Asian Stock Markets".

If you're interested, you can download a copy of the paper from here.

ver Effects Between Developed Stock Markets and Asian Emerging Stock Marketsodelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets

© 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":