Research

Current Research Interests

Microeconometrics: Inference for Income Distribution, Inequality and Poverty      

Measures; Nonparametric Inference; Bayesian IV

Energy Economics: Modelling of Energy Markets, Price Gouging

Index Numbers: in particular Purchasing Power Parities

Productivity: Efficiency and Productivity Measurement

Bayesian and Simulation Based Econometrics: MCMC, Variational Bayes,  

Bayesian Large Macroeconometric Models, Maximum Simulated Likelihood

A* Publications

• Rao D.S. Prasada and G. Hajargasht (2016), Stochastic Approach to Computation of Purchasing Power Parities in the International Comparison Program (ICP), Journal of Econometrics, 191(2), 414–425         

• Griffiths W. and G. Hajargasht (2016) Some Models for Stochastic Frontiers with Endogenity, Journal of Econometrics, 190(2), 341-348

• Hajargasht G., Griffiths, W. , Brice, J., Rao, D.S. and Chotikapanich, D. (2012) Inference for Income Distributions using  Grouped Data, Journal of Business and Economic Statistics, 30, 563-75

     Income Distribution

• Griffiths, W., Chotikapanich, D., & Hajargasht, G. (2022). A note on inequality measures for mixtures of double Pareto–lognormal distributions. Australian Economic Papers, 61(2), 280-290. 

• Hajargasht G. and W. Griffiths (2020), Minimum Distance Estimation of Parametric Lorenz Curves, Econometric Reviews, 39(4), 344-361.

• Chotikapanich, D., Griffiths, W. E., Hajargasht, G., Karunarathne, W., & Rao, D. S. (2018). Using the GB2 Income Distribution. Econometrics, 6(2), 21.

• Griffiths, W. and G. Hajargasht (2015) On GMM Estimation of Distributions from Grouped Data, Economics Letters, 126, 122-126.

• Hajargasht G. and Griffiths, W. (2013) Pareto–Lognormal Distributions: Inequality, Poverty, and Estimation  from Grouped Income Data, Economic Modelling, 33, 593-604.

• Hajargasht G., Griffiths, W. , Brice, J., Rao, D.S. and Chotikapanich, D. (2012) Inference for Income Distributions using  Grouped Data, Journal of Business and Economic Statistics, 30, 563-75

• Hajargasht G. and W. Griffiths (2013), GMM Estimation of Mixtures from Grouped Data, Department of Economics - Working Papers Series 1148, The University of Melbourne.

• Chotikapanich D., W. Griffiths, G. Hajargasht and C. Xia, Inequality and Poverty in Africa: Regional Updates and Estimation of a Panel Of Income Distributions.

     Purchasing Power Parities (PPP)

• Hajargasht, G., & Rao, D. P. (2019). Multilateral index number systems for international price comparisons:properties, existence and uniqueness. Journal of Mathematical Economics, 83, 36-47.

• Hajargasht, R., Hill, R. J., Rao, D. P., & Shankar, S. (2018). Spatial Chaining in International Comparisons of Prices and Real Incomes (No. 2018-03). University of Graz, Department of Economics

• Hajargasht, G. and D.S. Prasada Rao and A. Valadkhani (2019), Reliability of Basic Heading PPPs, Economics Letters, 180, 102-107.

  • Rao D.S. Prasada and G. Hajargasht (2016), Stochastic Approach to Computation of Purchasing Power Parities in the International Comparison Program (ICP), Journal of Econometrics, 191(2), 414–425         

• Hajargasht, G. & D.S. Prasada Rao. (2010) “Stochastic Approach to Index Numbers for Multilateral Price Comparisons and their Standard Errors", Review of Income and Wealth, 56, S32-S58

      Productivity Measurement and Other Topics

• Neguyen J, A. Valadkhani and G. Hajargasht (2021), The Choice between Renewables and Non-renewables: Evidence from Electricity Generation in 29 Countries, Energy Journal, DOI: 10.5547/01956574.42.6.jngu.

• Hajargasht G. and W. Griffiths (2019), “Semiparametric Stochastic Frontiers with Correlated Effects”, Advances in Econometrics. Vol 40. Part B, 1-28.

• Hajargasht G. and W. Griffiths (2018), "Estimation and Testing of Stochastic Frontier Models using Variational Bayes", Journal of Productivity Analysis,    Code

• Griffiths W. and G. Hajargasht (2016) Some Models for Stochastic Frontiers with Endogenity, Journal of Econometrics, 190(2), 341-348

• Hajargasht G. (2015) Stochastic Frontiers with a Rayleigh Distribution, Journal of Productivity Analysis , 44,2, 199-208

• Hajargasht, G., Coelli, T and Rao, D.S. Prasada, (2008). "A Dual Measure of Economies of Scope", Economics Letters, Elsevier, vol. 100(2), pages 185-188

• Valadkhani A., J Nguyen, R Hajargasht (2019), Incorporating daily market uncertainty data into a conventional short-run dynamic model: the case of the black-market exchange rate in Iran, Applied Economics.

• Coelli T., G. Hajargasht and C.A. Lovell, Econometric Estimation of an Input Distance Function in a System of Equations," CEPA Working Papers Series WP012008, School of Economics, University of Queensland, Australia.

• Hajargasht, G. (2014), The Folded Normal Stochastic Frontier Model.  Code

• Hajargasht, G. and D.S. Prasada Rao, Nonparametric  Panel  Data  Models:  A  Penalized  Spline Approach.  

   Bayesian and Simulation Based Econometrics: 

• Hajargasht G., Approximation Properties of Variational Bayes for Vector Autoregressions, arXiv preprint arXiv:1903.00617

• Hajargasht G. and T. Wozniak,  Accurate Computation of Marginal Data Densities Using Variational Bayes, arXiv preprint arXiv:1805.10036

    Works in Progress:

 Flexible Estimation of Stochastic Frontiers (1):  A Bayesian Penalized Spline Approach.

 Flexible Estimation of Stochastic Frontiers (2): Panel Data Models.

 Flexible Estimation of Stochastic Frontiers (3): Time-Varying Panel Data Models.

 Variational Bayes Methods for Large Bayesian VARs

 Accurate Computation of Marginal Data Density using Variational Bayes

 A Minimum Distance and the Generalized EKS Approaches to Multilateral Comparisons of    Prices  and Real Incomes

 Panel Data Models with Endogeniety and Sample Selection

 Stochastic Frontiers Using Maximum Simulated Likelihood