- Inflation modeling
- Output gap estimation
- Bayesian model comparison
- Stochastic volatility models
Joshua Chan received his Ph.D. from the University of Queensland in 2010. His long-term research focuses on inflation modeling, output gap estimation, model comparison and nonlinear state space models.
His current research is supported by the Australian Research Council via a Discovery Early Career Researcher Award. The funded project aims at developing new nonlinear time-varying macroeconometric models with an emphasis on understanding the impact of uncertainty on business cycles.
His research has appeared in Journal of Econometrics, Journal of Business and Economic Statistics, and Journal of Applied Econometrics, among others.
Chan, J.C.C, Eisenstat, E. and Koop, G. (2016). Large Bayesian VARMAs, Journal of Econometrics, 192(2), 374-390.
Chan, J.C.C. (2016). The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to In ation Modeling. Journal of Business and Economic Statistics, forthcoming.
Kroese, D.P. and Chan, J.C.C. (2014). Statistical Modeling and Computation, Springer, New York. [Amazon]
Chan, J.C.C. (2013). Moving Average Stochastic Volatility Models with Application to Inflation Forecast, Journal of Econometrics, 176(2), 162-172.
Chan, J.C.C., Koop, G., Potter, S.M. (2013). A New Model of Trend Inflation, Journal of Business and Economic Statistics, 31(1), 94-106.
Chan, J.C.C., Koop, G., Leon-Gonzalez, R. and Strachan, R. (2012). Time Varying Dimension Models, Journal of Business and Economic Statistics, 30(3), 358-367.
Discovery Early Career Researcher Award, The Australian Research Council, “New approaches to estimating nonlinear time-varying macroeconometric models”, 2015- 2017