Professor in Graduate School

Information theoretic econometric methods, behavioral networks.

University of Kentucky, B.S. Agricultural Economics 1948

Iowa State University, M.S. Economics and Statistics 1949

Iowa State University, Ph.D. 1952 Economics and Statistics 1952

FELLOW

Fellow of the Econometric Society

Fellow of the Journal of Econometrics

Fellow of the American Agricultural Economics Association

**BOOKS:**Judge, G. and Mittelhammer, R., (2012), An Information Theoretic Approach to Econometrics, Cambridge University Press

Mittelhammer, R., G. Judge and D. Miller (2000), Econometric Foundations, Cambridge University Press, 756 pages plus CDROM.

Hill, C, W. Griffiths, and G. Judge (2000), Undergraduate Econometrics, 2nd edition. New York: Wiley and Sons, 402 pages.

Hill, C, W. Griffiths, and G. Judge (1997), Undergraduate Econometrics, New York: Wiley and Sons, 366 pages (revised second edition - July 2000).

Golan, A., G. G. Judge, and D. Miller (1996), Maximum Entropy Econometrics: Robust Estimation with Limited Data, New York: John Wiley & Sons, 307 pages.

Hill, R. C., W. E. Griffiths, and G. G. Judge (Dec. 1997), Undergraduate Economics, New York, John Wiley & Sons.

Griffiths, W. E., R. C. Hill, and G. G. Judge (1993), Learning and Practicing Econometrics, New York: John Wiley & Sons, 866 pages.

Judge, G. G., R. C. Hill, W. E. Griffiths, H. Lütkepohl, and T.C. Lee (1988), Introduction to the Theory and Practice of Econometrics, (2nd ed.), New York: John Wiley & Sons, 1024 pages. Chinese Edition 1993.

Judge, G. G., and T. A. Yancey (1986), Improved Methods of Inference in Econometrics, Amsterdam: North-Holland Publishing Co., 291 pages.

Judge, G. G., W. E. Griffiths, R. C. Hill, H. Lütkepohl, and T.C. Lee (1985), The Theory and Practice of Econometrics, (2nd ed.), New York: John Wiley & Sons, 1050 pages.

Judge, G. G. (Ed.) (1984), Pre-Test and Stein-Rule Estimators: Some New Results, Amsterdam: North-Holland Publishing Co., 239 pages.

Judge, G. G., R. C. Hill, W. E. Griffiths, H. Lütkepohl, and T.C. Lee (1982), Introduction to the Theory and Practice of Econometrics, New York: John Wiley & Sons, 839 pages. Chinese Edition 1987.

Judge, G. G., W. E. Griffiths, R. C. Hill, and T.C. Lee (1980), The Theory and Practice of Econometrics, New York: John Wiley & Sons, 793 pages.

Judge, G. G., and M. E. Bock (1978), The Statistical Implications of Pre-test and Stein-rule Estimators in Econometrics, Amsterdam: North-Holland Publishing Co., 340 pages. Second Edition 1982

Judge, G. G., R. H. Day, S. R. Johnson, and G. C. Rausser (1977), Agricultural Economics Literature Review (Quantitative Economics), St. Paul, MN: University of Minnesota Press, 500 pages.

Judge, G. G., and T. Takayama (1973), Studies in Economic Planning Over Space and Time, Amsterdam: North-Holland Publishing Co., 727 pages. Reissued as a two-volume paperback set (Vol. 1, 416 pp., Vol 2, 301 pp.), 1976

Takayama, T., and G. G. Judge (1971), Spatial and Temporal Price and Allocation Models, Amsterdam: North-Holland Publishing Co., 528 pages. Two volume edition 1976

Lee, T.C., G. G. Judge, and A. Zellner (1970), Estimating the Parameters of the Markov Probability Model from Aggregate Time Series Data, (1st ed.), Amsterdam: North-Holland Publishing Co., 254 pages. Second Edition 1977. Russian Edition 1977.

**PAPERS**:Villa-Boas, S., Q. Fu and G. Judge, 2015, Is Benford’s Law a Universal Behavioral Law, Econometrics, 3, 698-708.

Squartini, T., S.E. Seri-Giacomi, D. Garlaschelli, and G. Judge, 2015. Information Recovery In Behavioral Networks. PLoS One, 10(5): e0125077.

Judge, G., 2015. Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems. Econometrics, 3:91-100.

Miller, D. and G. Judge, 2015. Information Recovery in a Dynamic Statistical Markov Model. Econometrics, 3:187-198.

Cho, W. and G. Judge, 2015. An Information theoretic Approach to Network Tomography. Applied Econometric Letters, 22(1): 1-6.

Cho, W. and G. Judge, 2014. An Information Theoretic Approach to Network Tomography. Applied Econometric Letters, DOI: 10.1080/13504851.2013.86619

Martin, M.,A. Plastino,V. Vampo and G. Judge, 2014. A parametric Information Theory Model for Predictions in Time Series. Physica, A,405:63-69.

Lee, J., w. Cho and G. Judge, 2013. Generalizing Benfords Law: A Re-examination of Falsified Clinical Data. S. Fisher, Editor, Princeton University Press, 313-331.

Judge, G. and R. Mittelhammer, 2013. A Risk Superior Semiparametric Estimator for Overidentified Linear Models. Advances in Econometrics, 30:237-256.

Judge, G. and R Mittelhammer, 2013. A Minimum Mean Squared Semiparametric Combining Estimator. In Advances in Econometrics , 29:55-86.

Judge, G., 2013. Fellows Opinion Corner: Econometric Information Recovery. Journal of Econometrics,172:1-2.

Villas, S. and G. Judge, 2013. An Information Theoretic Approach to Understanding the Micro Foundations of Macro Economics, Theoretical Economic Letters. 3, 48-51.

Plastino, A. and G.Judge, 2012. On Extracting Probability Distribution Information from Time Series. Entropy, 14:1829-1841.

Judge, G. and R Mittelhammer, 2012. Information Theoretic Approach to Econometrics, Cambridge University Press, 232 pages.

Judge, G. and R . Mittelhammer, 2012. Implications of the Cressie-Read Family of Additive Divergences For Information Recovery. Entropy, 4:2427-2438.

R. Mittelhammer and G Judge. 2011. A family of empirical likelihood functions and estimators for the binary response model. Journal of Econometrics 164, 207–217.

Lee, J, W. Cho and G. Judge. 2011. Generalizing Benford’s Law: A re-examination of falsified clinical data. In S. Miller (ed.), Benford’s Law. Princeton University Press.

M. Grendar and G. Judge. 2010. Large deviations theory and econometric information recovery. In A. Ullah & D. Giles (eds.), Handbook of Empirical Economics and Finance. Chapman & Hall.

A. Gorban, P. Gorban and G. Judge. 2010. Entropy: The Markov order approach. Entropy 12, 1145–1193.

Lee, J and G. Judge. 2010. Stigler’s approach to recovering the distribution of first significant digits in a natural data set. Statistics and Probability Letters 80, 82–88.

M. Grendar and G. Judge. 2009. Empty set problem of maximum empirical likelihood methods. Electronic Journal of Statistics 3, 1542–1555.

Judge, G. and R.C. Mittelhammer. 2009. A minimum power divergence class of CDFs and estimators for the binary choice model. International Econometric Review 1, 1–17.

M. Grendar and G. Judge. 2009. Asymptotic equivalence of empirical likelihood and Bayesian MAP. Annals of Statistics 37, 2445–2457.

Judge, G. and R.C. Mittelhammer. 2009. Moment based estimation and inference: The Generalized Cressie-Read estimator. In B. Schipp & W. Kramer (eds.), Statistical Inference, Econometric Analysis and Matrix Algebra, pp. 163–179. Springer-Verlag.

Judge, G., & Mittelhammer, R.C., Robust Moment Based Estimation and Inference: The Generalized Cressie-Read Estimator. In Bernhard Schipp and Walter Kramer (Eds.), Statistical Inference, Econometric Analysis, and Matrix Algebra, Physica-Verlag, p. 163-178, 2009.

M. Grendar and G. Judge. 2008. Large deviation theory and empirical estimator choice. Econometric Reviews 27, 513–525.

Judge, G and W. Cho. 2008. Recovering voter choice from partial incomplete data. Journal of Data Sciences 6, 155–171.