Bin Peng


  • 3 EAST 4.36

Accepting PhD Students


Personal profile

Research interests

Econometrics, Applied Economics

Education/Academic qualification

PhD Econometrics, Doctor of Philosophy, Monash University

Sep 2009May 2013

Applied Econometrics, Master of Economics, Monash University


Mathematicas, Bachelor of Science, Nanjing University


Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

Panel Data Mathematics
Data Model Mathematics
Data structures Engineering & Materials Science
Fixed Effects Mathematics
Varying Coefficient Model Mathematics
Categorical Mathematics
Varying Coefficients Mathematics
Estimator Mathematics

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Research Output 2015 2017

A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks

Feng, G., Gao, J., Peng, B. & Zhang, X. Jan 2017 In : Journal of Econometrics. 196, 1, p. 68-82

Research output: Contribution to journalArticle

Varying Coefficients
Fixed Effects
Panel Data
Data Model
Data structures
Panel Data
Data Model
Asymptotic distribution
Factor Structure

Productivity and Efficiency at Bank Holding Companies in the U.S.: A Time-varying Heterogeneity Approach

Feng, G., Peng, B. & Zhang, X. 1 Dec 2017 In : Journal of Productivity Analysis. 48, 2-3, p. 179-192

Research output: Contribution to journalArticle


Variable Selection for a Categorical Varying-Coefficient Model with Identifications for Determinants of Body Mass Index

Gao, J., Peng, B., Ren, Z. & Zhang, X. 2017 In : Annals of Applied Statistics. 11, 2, p. 1117-1145

Research output: Contribution to journalArticle

Open Access
Varying Coefficient Model
Variable Selection
Identification (control systems)

Inference on modelling cross-sectional dependence for a varying-coefficient model

Peng, B. Aug 2016 In : Economics Letters. 145, p. 1-5 5 p.

Research output: Contribution to journalArticle

Varying coefficient model
Asymptotic normality
Cross-sectional data