Grants

ARC-grant funded projects:

[1] Selection of mixed strength moment restrictions and optimal inference (DP20; 2020-2022; $240,000), with Prosper Dovonon (Concordia University)

Summary: This project aims to develop model selection procedures in moment condition models that yields the best model for optimal inference even if the available moment restrictions have mixed identification strength. Expected outcomes include: efficiency bounds for parameters identified by moment condition models of mixed identification strength; and new entropy-based relevant moment selection criteria. Success in this undertaking will dramatically enlarge the pool of empirical work involving moment condition models.

[2] Identification power and instrument strength for causal effect in discrete outcome models (DP21; 2021-2023; $354,000), with Donald Poskitt (Monash University), Xueyan Zhao (Monash University), Eric Renault (University of Warwick) and Franck Windmeijer (University of Oxford)

Summary: This project aims to develop new econometric and statistical techniques to quantify causal effects in treatment models with discrete outcomes. Expected outcomes include a much-needed weak instrument test, a measure for identification strength in partial identification setting, and an instrument-covariate selection procedure for high dimensional discrete models based identification power. The benefits include advanced knowledge in econometrics and statistics, and enhanced tools for program evaluation and policy assessment in empirical causal analysis using observational data. The project falls into the category of smarter information use and is relevant to any national priority areas where policy interventions require assessment.

Other grants:

[3] Developing improved estimators for the stochastic frontier model (Ratio Research Institute Fellowship, $110,000), with Prof Magnus Soederberg (University of Southern Denmark, Denmark); and Prof Zangin Zeebari (Jönköping University, Sweden)

Summary: The project aims to develop improved estimators for the stochastic frontier model (used, for example, by regulators to determine relative efficiency of local monopolies). The project will involve both theoretical and applied work, including an application of the methodology proposed with Australian and international energy data.