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Cheng Hsiao

Cheng Hsiao (born 28 July 1943^[1]) is a Chinese-American econometrician and statistician, a professor of economics at the University of Southern California. Hsiao is known for his works in time series analysis and panel data analysis.^[1][2]

Education and career[edit]

Hsiao was born in Chongqing and his family moved to Taiwan in 1950, where Hsiao received his primary and secondary education. He obtained his BA at the National Taiwan University in 1965. He went on to obtain a BPhil at University of Oxford in 1968, a MSc and a PhD, both in statistics, at Stanford University in 1970 and 1972, respectively. His PhD thesis, titled The Combined Use of Cross-Section and Time-Series Data in Econometric Analysis, was supervised by Theodore Wilbur Anderson and Takeshi Amemiya.^[1]

Hsiao was an assistant professor in economics at University of California, Berkeley from 1972 to 1977. He then moved to Canada, becoming an associated professor in economics at the University of Toronto between 1977 and 1980, and was a full professor from 1980 to 1985. He relocated to the US afterwards and stay at the University of Southern California since 1985 as a professor of economics.^[3]

Bibliography[edit]

• Intriligator, Michael D.; Bodkin, Ronald G.; Hsiao, Cheng (1996). Econometric models, techniques, and applications (2nd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 0-13-224775-5. OCLC 33404640.
• Hsiao, Cheng; Pesaran, M. Hashem; Lahiri, Kajal; Lee, Lung Fei, eds. (1999). Analysis of panels and limited dependent variable models : in honour of G.S. Maddala. Cambridge (U.K.): Cambridge University Press. ISBN 0-511-04012-1. OCLC 228069265.
• Hsiao, Cheng; Morimune, Kimio; Powell, James, eds. (2001). Nonlinear statistical modeling : proceedings of the thirteenth International Symposium in Economic Theory and Econometrics : essays in honor of Takeshi Amemiya. New York: Cambridge University Press. ISBN 0-521-66246-X. OCLC 44039814.
• Hsiao, Cheng (2022). Analysis of panel data (4th ed.). Cambridge: Cambridge University Press. ISBN 978-1-009-05774-5. OCLC 1296690708.

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