Econometrics Reading for the New Year

Another year, and lots of exciting reading!

• Davidson, R. & V. Zinde-Walsh, 2017. Advances in specification testing. Canadian Journal of Economics, online.
• Dias, G. F. & G. Kapetanios, 2018. Estimation and forecasting in vector autoregressive moving average models for rich datasets. Journal of Econometrics, 202, 75-91.  
• González-Estrada, E. & J. A. Villaseñor, 2017. An R package for testing goodness of fit: goft. Journal of Statistical Computation and Simulation, 88, 726-751.
• Hajria, R. B., S. Khardani, & H. Raïssi, 2017. Testing the lag length of vector autoregressive models:  A power comparison between portmanteau and Lagrange multiplier tests. Working Paper 2017-03, Escuela de Negocios y EconomÍa. Pontificia Universidad Católica de ValaparaÍso.
• McNown, R., C. Y. Sam, & S. K. Goh, 2018. Bootstrapping the autoregressive distributed lag test for cointegration. Applied Economics, 50, 1509-1521.
• Pesaran, M. H. & R. P. Smith, 2017. Posterior means and precisions of the coefficients in linear models with highly collinear regressors. Working Paper BCAM 1707, Birkbeck, University of London.
• Yavuz, F. V. & M. D. Ward, 2017. Fostering undergraduate data science. American Statistician, online. 

© 2018, David E. Giles

Interpolating Statistical Tables

We've all experienced it. You go to use a statistical table - Standard Normal, Student-t, F, Chi Square - and the line that you need simply isn't there in the table. That's to say the table simply isn't detailed enough for our purposes.

One question that always comes up when students are first being introduced to such tables is:

"Do I just interpolate linearly between the nearest entries on either side of the desired value?"

Not that these exact words are used, typically. For instance, a student might ask if they should take the average of the two closest values. How should you respond?