Showing posts with label Measurement error. Show all posts
Showing posts with label Measurement error. Show all posts

Tuesday, February 17, 2015

Non-Existent Instruments

Consider the following abstract for an econometrics paper:
"The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We argue that, in general, such instruments (weak or strong) cannot exist." 
This is, in fact, the abstract for a recent paper by Hall et al. (2014), and when I first read it I was definitely intrigued!

Recall that when we look for instruments we need to find variables that are, on the one hand, (asymptotically) uncorrelated with the errors of our regression model; but are, on the other hand, highly correlated (asymptotically) with the random regressors. The abstract, and the paper itself (of course) suggests that usually this objective is not achievable.

Why is this?

The difficulty arises if we view the error term in our regression equation as arising from various mis-specifications in the model. The authors argue that this interpretation is generally appropriate in econometric applications. Building on earlier work by Pratt and Schlaifer (1988), they show that in this case it's generally the situation that the error is a function of the very regressors that we're trying to "instrument". That being the case, legitimate instruments will be unattainable.

Food for thought!   


References

Hall, S. G., P. A. V. B. Swamy, and G. S. Tavlas, 2014. On the interpretation of instrumental variables in the presence of specification errors. Working Paper 14/19, Department of Economics, University of Leicester.

Pratt, J. W. and R. Schlaifer, 1988. On the Interpretation and observation of laws. Journal of Econometrics, 39, 23-52.


© 2015, David E. Giles

Friday, September 6, 2013

Some More Papers for Your "To Read" List


For better, or worse, here are some of the papers I've been reading lately:
  • Chambers, M. J., J. S. Ercolani, and A. M. R. Taylor, 2013. Testing for seasonal unit roots by frequency domain regression. Journal of Econometrics, in press. 
  • Chicu, M. and M. A. Masten, 2013. A specification test for discrete choice models. Economics Letters, in press. 
  • Hansen, P. R. and A. Lunde, 2013. Estimating the persistence and the autocorrelation function of a time series that is measured with error. Econometric Theory, in press.
  • Liu, Y., J. Liu, and F. Zhang, 2013. Bias analysis for misclassificaiton in a multicategorical exposure in a logistic regression model. Statistics and Probability Letters, in press.
  • Thornton, M., 2013, The aggregation of dynamic relationships caused by incomplete information. Journal of Econometrics, in press.
  • Wang, H. and S. Z. F. Zhou, 2013. Interval estimation by frequentist model averaging. Communications in Statistics - Theory and Methods, in press.   

© 2013, David E. Giles

Sunday, June 16, 2013

Vintage Years in Econometrics: The 1940's

A Fathers' Day "thank you" to our founding fathers..........

Following on from my earlier post about vintage years for econometrics in the 1930's, here's my take on the 1940's. This is a more challenging decade to assess, given the explosion of major contributions in the second decade of life for the new discipline.

As before, let me note that "in econometrics, what constitutes quality and importance is partly a matter of taste - just like wine! So, not all of you will agree with the choices I've made in the following compilation."

I've added a few "tasting notes" here and there, if I thought they were warranted.

Tuesday, April 30, 2013

Some Official Data Come With Standard Errors!

Without intending to, I seem to have been on a bit of a rant about data quality and reliability recently! For example, see here, here, and here.

This post is about a related topic that's bugged me for a long time. It's to do with the measures of uncertainty that some statistical agencies (e.g., Statistics Canada) correctly report with some of their survey-based statistics.

A good example of what I have in mind is the Labour Force Survey (LFS) from Statistics Canada.

Friday, June 1, 2012

Yet Another Reason for Avoiding the Linear Probability Model

Oh dear, here we go again. Hard on the heels of this post, as well an earlier one here, I'm moved to share even more misgivings about the Linear Probability Model (LPM). That's just a fancy name for a linear regression model in which the dependent variable is a binary dummy variable, and the coefficients are estimated by OLS.