Canada Day Reading List

I was tempted to offer you a list of 150 items, but I thought better of it!

• Hamilton, J. D., 2017. Why you should never use the Hodrick-Prescott filter. Mimeo., Department of Economics, UC San Diego.

• Jin, H. and S. Zhang, 2017. Spurious regression between long memory series due to mis-specified structural breaks. Communications in Statistics - Simulation and Computation, in press.

• Kiviet, J. F., 2016. Testing the impossible: Identifying exclusion restrictions.Discussion Paper 2016/03, Amsterdam School of Economics, University of Economics.

• Lenz, G. and A. Sahn, 2017. Achieving statistical significance with covariates. BITSS Preprint (H/T  Arthur Charpentier)

• Sephton, P., 2017. Finite sample critical values of the generalized KPSS test. Computational Economics, 50, 161-172.

• Stypka, O., P. Grabarczyk, R. Kawka, and M. Wagner, 2017. "Linear" fully modified OLS estimation of cointegrating polynomial regressions. Discussion Paper Nr. 77/2016, SFB 823. (H/T David Stern)

© 2017, 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

Reading List for October

Some suggestions for the coming month:

• Franses, P. H., 2016. A note on the mean absolute scaled error. International Journal of Forecasting, 32, 20-22.
• Gorroochurn, P., 2015. On Galton's change from 'reversion' to 'regression'. American Statistician, in press.
• Holan, S. H., R. Lund, and G. Davis, 2010. The ARMA alphabet soup: A tour of ARMA model variants. Statistics Surveys, 4, 232-274.
• Liu, S., H. Wu, and W. Q. Meeker, 2015. Understanding and addressing the unbounded "likelihood" problem. American Statistician, 69, 191-200.
• McCracken, M. W. and S. Ng, 2015.  FRED-MD: A monthly database for macroeconomic research. Journal of Business and Economic Statistics, in press.
• Müller, U. K. and M. W. Watson, 2015. Low-frequency econometrics. NBER Working Paper No. 21564.

© 2015, David E. Giles

The H-P Filter and Unit Roots

The Hodrick-Prescott (H-P) filter is widely used for trend removal in economic time-series, and as a basis for business cycle analysis, etc. I've posted about the H-P filter before (e.g., here).

There's a widespread belief that application of the H-P filter will not only isolate the deterministic trend in a series, but it will also remove stochastic trends - i.e., unit roots. For instance, you'll often hear that if the H-P filter is applied to quarterly data, the filtered series will be stationary, even if the original series is integrated of order up to 4.

Is this really the case?

Let's take a look at two classic papers relating to this topic, and a very recent one that provides a bit of an upset.

Questions About Granger Causality Testing - The Fine Print

Judging by the number of hits, comments, and questions that I've had in relation to my various posts on testing for Granger (Non-) Causality, this seems to be a topic that a lot of followers find interesting. For instance, see the posts here, here, here, and especially here.

In the comments, and in a large number of related emails that I've received, several questions seem to recur, and I thought it would be worth addressing them in one place - right here, to be specific!

The following discussion relates to the (usual) case where there is the possibility that one or more of the time-series variables under consideration may be non-stationary, and some of the variables may be cointegrated. In such cases we have to be especially careful when we apply tests for Granger causality. The reasons for this, and for adopting a modified testing procedure, such as that proposed by Toda and Yamamoto (1995), or that of Dolado and Lütkepohl  (1996) and Saikkonen and Lütkepohl (1996), are laid out in this earlier post, and I won't repeat them here. I'll make the bold assumption that you've done your homework.

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":

Hot Topics in Econometrics

Last week, Takamitsu Kurita asked me "What do you think will be the big developments in Econometrics over the next decade". We were having a drink following his seminar, and I really didn't have a good answer. I think those of us present ducked the question by saying that, as econometricians, we know only too well the pitfalls associated with forecasting! But Taka's question was a good one, and it certainly deserved a better response than I had at the time.