Who was Shirley Almon?

How often have you said to yourself, "I wonder what happened to Jane X"? (Substitute any person's name you wish.)

Personally, I've noticed a positive correlation between my age and the frequency of occurrence of this event, but we all know that correlation doesn't imply causality.

Every now and then, over the years, I've wondered what happened to Shirley Almon, of the "Almon Distributed Lag Model" fame. Of course I should have gone to the internet for assistance, but somehow, I never did this - until the other day.......

MIDAS Regression is Now in EViews

The acronym, "MIDAS", stands for several things. In the econometrics literature it refers to "Mixed-Data Sampling" regression analysis. The term was coined by Eric Ghysels a few years ago in relation to some of the novel work that he, his students, and colleagues have undertaken. See Ghysels et al. (2004).

Briefly, a MIDAS regression model allows us to "explain" a (time-series) variable that's measured at some frequency, as a function of current and lagged values of a variable that's measured at a higher frequency. So, for instance, we can have a dependent variable that's quarterly, and a regressor that's measured at a monthly, or daily, frequency.

There can be more than one high-frequency regressor. Of course, we can also include other regressors that are measured at the low (say, quarterly) frequency, as well as lagged values of the dependent variable itself. So, a MIDAS regression model is a very general type of autoregressive-distributed lag model, in which high-frequency data are used to help in the prediction of a low-frequency variable.

There's also another nice twist.......

March Reading List

Now is a good time to catch up on some Econometrics reading. Here are my suggestions for this month:

• Carrasco, M. and R. Kotchoni, 2016. Efficient estimation using the characteristic function. Econometric Theory, in press.
• Chambers, M. J., 2016. The estimation of continuous time models with mixed frequency data. Discussion Paper No. 777, Department of Economics, University of Essex.
• Cuaresma, J. C., M. Feldkircher, and F. Huber, 2016. Forecasting with global vector autoregressive models: A Bayesian approach. Journal of Applied Econometrics, in press.
• Hendry, D., 2016. Deciding between alternative approaches in macroeconomics. Discussion Paper No. 778, Department of Economics, University of Oxford.
• Reed, W. R., 2016. Univariate unit root tests perform poorly when data are cointegrated. Working Paper No. 1/2016, Department of Economics and Finance, University of Canterbury.

© 2016, David E. Giles