Wednesday, July 30, 2014

Summer Reading List

While I'm at the lake, fishing, this is your big chance to get on with some reading.

There won't be a quiz, but I know that you'll thank me for this later on:
  • Aberdie, A., S. Athey, G. W. Imbens, and J. Wooldridge, 2014. Finite population standard errors. Mimeo.
  • Boero, G., J. Smith, and K. F. Wallis, 2014. The measurement and characteristics of professional forecasters' uncertainty. Journal of Applied Econometrics, in press.
  • Liu, C-A., 2014. Distribution theory of the least squares averaging estimator. Journal of Econometrics, in press.
  • Manzan, S., 2014. Forecasting the distribution of economic variables in a data-rich environment. Journal of Business and Economic Statistics, in press.
  • Sanderson, E. and F. Windmeijer, 2014. A weak instrument F-test in linear IV models with multiple endogenous variables. Discussion Paper 14/644, Department of Economics, University of Bristol.
  • Yang, Z., 2014. A general method for third-order bias and variance corrections on a nonlinear estimator. Journal of Econometrics, in press.


© 2014, David E. Giles

Monday, July 21, 2014

More on Step-(Un)Wise Regression and Pre-Testing

I've been meaning to do a decent post on Pre-test Estimation for some time. It just hasn't happened!

The general issue of pre-testing came up in my recent post on Step-Wise Regression, (I prefer the term, "Step-Unwise Regression"). I want to add a few things to what I said there.

First, a reminder of this post from April 2013:


Sunday, July 20, 2014

Promoting Econometrics Through Econometrica

Regular readers of this blog will know that I have an interest (but negligible talent or authority) in the history of econometrics. Actually, the same applies to the history of the discipline of statistics. I find it difficult to appreciate where we are without knowing something about where we came from, and I try to convey this to my students.

Olav Bjerkholt (University of Oslo) has provided me with a lot of very valuable material and insights in recent months, and I've been delighted to have drawn on his contributions in previous posts (e.g., here, here, and here).

Olav wrote to me yesterday, as follows:

Friday, July 18, 2014

Step-wise Regression

Some time ago, Haynes Goddard emailed me suggesting that I post something about step-wise regression. He also put me on an interesting paper by Peter Flom and David Cassell.

Many statistical and econometrics packages include stepwise regression. I wish they didn't! Here's why.

Rejected Economists

If you're feeling "down" as a result of a recent rejection of your work by a nasty journal editor, you may find some comfort in this paper from The Journal of Economic Perspectives in 1994: "How Are the Mighty Fallen: Rejected Classic Articles by Leading Economists", by Joshua Gans and George Shepherd.

In it, you can read about the many famous economists - Nobel laureates included - who have struggled to get their work published. There are some great stories here.

In the process, you'll learn why the Tobit model is so-named - and it's not just for the obvious reason that comes to mind!

I promise that you'll feel much better after reading what Gans and Shepherd have to say.
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© 2014, David E. Giles

Thursday, July 17, 2014

Price Indices Based on Scanner Data

Among the many interesting presentations that I attended at the recent Annual Conference of the N.Z. Association of Economists was one by Frances Krsinich, from Statistics New Zealand.

The paper that Frances gave was titled "Price Indexes From Online Data Using the Fixed Effects Window Splice (FEWS) Method". Here's the abstract:

Demand Analysis, Henry Schultz and the Rediscovery of Slutsky

Empirical demand analysis played a central role in the early history of econometrics. For instance, studies relating to this topic laid the groundwork for our understanding of simultaneous equations systems, identification, and instrumental variables estimation.

Throughout the 1950's, 1960's, and 1970's, empirical demand analysis loomed large in the empirical econometrics literature. So, any new insights relating to the history of the theory of demand are of considerable interest to applied econometricians.

Olav Bjerkholt, of the Department of Economics at the University of Oslo, has recently released a paper titled, "Henry Schultz and the Rediscovery of Slutsky (1915)". Here's the abstract:

Wednesday, July 16, 2014

Riding With the Reverand

Last week, outside Peter Jackson's ("Lord of the Rings", "The Hobbit") Weka Studios in Wellington, New Zealand, I happened to spy..........


All aboard! Let's get this bus on the road.


© 2014, David E. Giles

The Econometrics of Temporal Aggregation - II - Causality Testing

In my recent post about my presentation at the recent conference of the N.Z. Association of Economists, I promised some follow-up posts of a more specific nature. I thought I'd begin with some brief comments about the effects of temporal aggregation on Granger causality.

By temporal aggregation, I'm referring to the situation where the economic activity takes place at some frequency (say daily), but our time-series data are recorded at only a lower frequency (say monthly). My use of the word "aggregation' tells you that we're "adding up" the data over time - so, at least implicitly, I'm thinking about flow data.

How does this sort of aggregation impact on Granger causality?

Friday, July 11, 2014

Finite-Sample Properties of the 2SLS Estimator

During a recent conversation with Bob Reed (U. Canterbury) I recalled an interesting experience that I had at the American Statistical Association Meeting in Houston, in 1980. I was sitting in a session listening to an author presenting a paper about the bias and MSE of certain simultaneous equations estimators. The results were based on a Monte Carlo experiment. However, something just didn't seem right.

I looked at the guy sitting next to me - I didn't know him, but he was also looking puzzled. Then, at the same time, we both said to each other, "But the first two moments of that estimator don't exist!" The next thing out of our mouths was, "Who's going to tell him?"

The guy next to me turned out to be Tom Fomby, and I believe he was the one who politely explained to the speaker that his results were nonsensical.

If (the sampling distribution of) an estimator doesn't have a well-defined mean then it's nonsensical to talk that estimator's bias. Equally, if it doesn't have a well-defined variance, then it makes no sense to talk about its MSE. In other words, the Monte Carlo simulation results were trying to measure something that didn't exist! 

So, what was going on here?