Gael M. Martin

Emeritus Professor 

Monash University, Clayton, 3800, Melbourne, AUSTRALIA

Fellow of the Academy of Social Sciences Australia (FASSA)

B.A. (University of Melbourne)

A.Mus.A. (Piano, AMEB)

B.Ec. (Hons), M.Ec., Ph.D. (Monash University)

Email:  gael.martin@monash.edu

Research Interests

Bayesian Inference; Computational Statistics; Probabilistic Forecasting; Financial Econometrics; Time Series Analysis; Long Memory Time Series; Count Time Series

Recent and Forthcoming Publications

  1. Frazier, D.T., Loaiza-Maya, R., Martin, G.M. and Koo, B., 2024,  "Loss-Based Variational Bayes Prediction", In Press, Journal of Computational and Graphical Statistics. Working paper downloadable at: https://arxiv.org/pdf/2104.14054.pdf

  2. Martin, G.M., Frazier, D.T., Maneesoonthorn, W., Loaiza-Maya, R., Huber, F., Koop, G., Maheu, J., Nibbering, D. and Panagiotelis, A., 2024, "Bayesian Forecasting in Economics and Finance: A Modern Review", International Journal of Forecasting, 40(2), 811-839, https://doi.org/10.1016/j.ijforecast.2023.05.002

  3. Martin, G.M., Frazier, D.T. and Robert, C.P., 2024, "Computing Bayes: From Then 'Til Now". Statistical Science 39(1):  3-19, https://doi.org/10.1214/22-STS876

  4. Martin, G.M., Frazier, D.T. and Robert, C.P., 2024, "Approximating Bayes in the 21st Century". Statistical Science 39(1):  20-45, https://doi.org/10.1214/22-STS875

  5. Frazier, D.T., Loaiza-Maya, R. and Martin, G.M., 2023, "Variational Bayes in State Space Models: Inferential and Predictive Accuracy". Journal of Computational and Graphical Statistics,  32,  793-804.  https://www.tandfonline.com/doi/full/10.1080/10618600.2022.2134875 

  6. Henri Pesonen, Umberto Simola, Alvaro Kohn-Luque, Henri Vuollekoski, Xiaoran Lai, Arnoldo Frigessi, Samuel Kaski, David T. Frazier, Worapree Maneesoonthorn, Gael M. Martin and Jukka Corander, 2023, "ABC of the Future". International Statistical Review, 91, 243-268. https://onlinelibrary.wiley.com/doi/10.1111/insr.12522

  7. Martin, G.M., Loaiza-Maya, R., Maneesoonthorn, W., Frazier, D.T. and Ramirez Hassan, A., 2022, "Optimal Probabilistic Forecasts:  When do they Work? ". International Journal of Forecasting, 38(1), 384-406, https://doi.org/10.1016/j.ijforecast.2021.05.008

  8. Martin, G.M., 2021, "Forecasting Count Time Series"; Section 2.3.8 in "Forecasting: Theory and Practice", (an encyclopedic review paper with contributions from multiple authors). Forthcoming, International Journal of Forecasting. Working paper downloadable at: https://arxiv.org/abs/2012.03854

  9. Frazier, D.T. and Martin, G.M., 2021, "Foundations of Bayesian Forecasting" and "Implementation of Bayesian Forecasting"; Sections 2.4.1 and 2.4.2 in "Forecasting: Theory and Practice", (an encyclopedic review paper with contributions from multiple authors). Forthcoming, International Journal of Forecasting. Working paper downloadable at: https://arxiv.org/abs/2012.03854

  10. Loaiza-Maya, R., Martin, G.M. and Frazier, D.T., 2021, "Focused Bayesian Prediction", Journal of Applied Econometrics, https://onlinelibrary.wiley.com/doi/abs/10.1002/jae.2810   Recent presentation of the paper in the "One World Approximate Bayesian Computation (ABC) Seminar Series" at: https://www.youtube.com/watch?v=TkVzbrJIv6Q&feature=youtu.be

  11. Nadarajah, K, Martin, G.M. and Poskitt, D.S., 2020, "Optimal Bias-Correction in the Log Periodogram Estimation of the Fractional Parameter: A Jackknife Approach", Journal of Statistical Planning and Inference, 211, 41-79. Available at: https://www.sciencedirect.com/science/article/pii/S0378375820300483?dgcid=author

  12. Maneesoonthorn, W., Martin, G.M. and Forbes, C.S., 2020, "High-Frequency Jump Tests: Which Test Should We Use?", Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2020.03.012 Extended working paper version available at: https://arxiv.org/abs/1708.09520

  13. Martin, G.M., Nadarajah, K. and Poskitt, D.S., 2019,"Issues in the Estimation of Mis-specified Models of Fractionally Integrated Processes", Journal of Econometrics, 215, 559-573. https://www.sciencedirect.com/science/article/abs/pii/S030440761930209X

  14. Frazier, D.T., Maneesoonthorn, W., Martin, G.M. and McCabe, B.P.M., 2019, "Approximate Bayesian Forecasting", International Journal of Forecasting, 35, 521–539. Matlab code for all numerical work in the paper is to be found in: ABF_MATLAB.zip and ABC_sim_file_INARMA.zip. Published paper is here: Published_IJF_paper_2019.pdf

  15. Martin, G.M., McCabe, B.P.M., Frazier, D.T., Maneesoonthorn, W. and Robert, C.P., 2019, "Auxiliary Likelihood-based Approximate Bayesian Computation in State Space Models", Journal of Computational and Graphical Statistics, 28, 508-522. https://www.tandfonline.com/doi/full/10.1080/10618600.2018.1552154

  16. Harris, D., Martin, G.M., Perera, I. and Poskitt, D.S., 2019, "Construction and Visualization of Confidence Sets for Frequentist Distributional Forecasts", Journal of Computational and Graphical Statistics, 28, 92-104. https://www.tandfonline.com/doi/full/10.1080/10618600.2018.1476252

  17. Frazier, D.T., Martin, G.M., Robert, C.P. and Rousseau, J., 2018, "Asymptotic Properties of Approximate Bayesian Computation", Biometrika, 105, 593–607. Available at https://academic.oup.com/biomet/article-abstract/105/3/593/5033963?redirectedFrom=fulltext

  18. Maneesoonthorn, W., Forbes, C.S. and Martin, G.M, 2017, "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures". Journal of Applied Econometrics, 32, 504-532. Available at: http://onlinelibrary.wiley.com/doi/10.1002/jae.2547/epdf. Supplementary appendix: Online_Appendix.pdf

  19. Poskitt, D.S., Martin, G.M. and Grose, S.D., 2017, "Bias Correction of Semiparametric Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap". Econometric Theory, 33, 578-609. Extended working paper version (with additional numerical results): http://arxiv.org/abs/1603.01897

  20. Poskitt, D.S., Grose, S.D. and Martin, G.M., 2015, "Higher-order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes". Journal of Econometrics, 188, 94-100. Available at: http://www.sciencedirect.com/science/article/pii/S0304407615001372.

  21. Grose, S.D, Martin, G.M. and Poskitt, D.S., 2015, "Bias Correction of Persistence Measures in Fractionally Integrated Models". Journal of Time Series Analysis, 36, 721-740. Available at: http://onlinelibrary.wiley.com.ezproxy.lib.monash.edu.au/doi/10.1111/jtsa.12116/pdf . Supplementary On-Line Appendix available at:http://onlinelibrary.wiley.com.ezproxy.lib.monash.edu.au/store/10.1111/jtsa.12116/asset/supinfo/jtsa12116-sup-0001-Supplementary.pdf?v=1&s=fff9307cb925a16b6bd72c1bb4c157ad1deef130

  22. Ng, J, Forbes, C.S., Martin, G.M, and McCabe, B.P.M., 2013, "Nonparametric Estimation of Forecast Distributions in Non-Gaussian State Space Models". International Journal of Forecasting, 29, 411-430. Available at: http://www.sciencedirect.com/science/article/pii/S0169207012001665

  23. Maneesoonthorn, W., Martin, G.M, Forbes, C.S. and Grose, S., 2012, "Probabilistic Forecasts of Volatility and its Risk Premia". Journal of Econometrics, 171, 217-236. Available on-line at:
    http://www.sciencedirect.com/science/article/pii/S0304407612001534

  24. McCabe, B.P.M., G.M. Martin and Harris, D.G., 2011, "Efficient Probabilistic Forecasts for Counts". Journal of the Royal Statistical Society (Series B), 73, 253-272. Available at http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2010.00762.x/pdf

  25. McCabe, B.P.M., G.M. Martin and Freeland, K., 2011, "A Quasi-Locally Most Powerful Test for Correlation in the Conditional Variance of Positive Data". Australian and New Zealand Journal of Statistics, 53, 43-62. Available at http://onlinelibrary.wiley.com/doi/10.1111/j.1467-842X.2010.00596.x/pdf

  26. Lahiri, K. and Martin, G.M., 2010, "Bayesian Forecasting in Economics: Editorial", Special Issue of International Journal of Forecasting, 26, 211-215. Available at http://www.elsevier.com/wps/find/journaldescription.cws_home/505555/description#description

  27. Martin, G.M., 2010, "The 21st Century Belongs to Bayes: Editorial Introduction", Review of Economic Analysis, 2, 137-138. Available at http://www.rofea.org/index.php/journal/article/viewFile/33/46

  28. Martin, G.M., Reidy, A. and J. Wright, 2009, "Does the Option Market Produce Superior Forecasts of Noise-Corrected Volatility Measures?" Journal of Applied Econometrics, 24, 77-104. Available at http://www3.interscience.wiley.com/cgi-bin/fulltext/121544039/PDFSTART

  29. Feigin, P.D., Gould, P., Martin, G.M. and R.D. Snyder, 2008, "Feasible Parameter Regions for Alternative Discrete State Space Models" Statistics and Probability Letters, 78, 2963-2970. Available at http://dx.doi.org/10.1016/j.spl.2008.05.021

  30. Strickland, C.M., Martin, G.M. and C.S. Forbes, 2008, "Parameterization and Efficient MCMC Estimation of Non-Gaussian State Space Models". Computational Statistics and Data Analysis, Special Issue on Statistical and Computational Methods in Finance, 52, 2911-2930. Available at http://www.sciencedirect.com/science/journal/01679473

  31. Forbes, C.S., G.M. Martin and J. Wright, 2007, "Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter", Econometric Reviews, Special Issue on Bayesian Dynamic Econometrics, 26, 387-418. Available at http://www.informaworld.com/smpp/content~content=a778186553~db=all~order=page

  32. Strickland, C.M., Forbes, C.S. and G.M. Martin, 2006, "Bayesian Analysis of the Stochastic Conditional Duration Model", Computational Statistics and Data Analysis, Special Issue on Statistical Signal Extraction and Filtering, 50, 2247-2267. Available at http://www.sciencedirect.com/science/journal/01679473

  33. Lim, G.C., Martin, G.M. and V.L. Martin, 2006, "Pricing Currency Options in the Presence of Time-Varying Volatility and Nonnormalities",  Journal of Multinational Financial Management, 16, 291-314. Available at http://www.sciencedirect.com/science/journal/1042444X

  34. McCabe, B.P.M. and G.M. Martin, 2005, "Bayesian Predictions of Low Count Time Series", International Journal of Forecasting, 21, 315-330. Available at  http://www.sciencedirect.com/science/journal/01692070

  35. Martin, G.M., Forbes, C.S. and V.L. Martin, 2005, "Implicit Bayesian Inference Using Option Prices", Journal of Time Series Analysis, 26, 437-462. Available at http://www.blackwell-synergy.com/rd.asp?code=JTSA&goto=journal

  36. McCabe, B.P.M., Martin, G.M. and A.R. Tremayne, 2005, "Assessing Persistence in Discrete Nonstationary Time Series Models", Journal of Time Series Analysis, 26, 305-317. Available at   http://www.blackwell-synergy.com/rd.asp?code=JTSA&goto=journal . Earlier version available as Working Paper 2003/16 at  http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2003/wp16-03.pdf

  37. Lim, G.C., Martin, G.M. and V.L. Martin, 2005, "Parametric Pricing of Higher Order Moments in S&P500 Options", Journal of Applied Econometrics, 20, 377-404. Available at http://www3.interscience.wiley.com/cgi-bin/abstract/109856225/ABSTRACT . Draft version downloadable as S&P500_2003

  38. Flynn, D.B., Grose S.D., Martin, G.M. and V.L. Martin, 2005, "Pricing Australian S&P200 Options: A Bayesian Approach Based on Generalized Distributional Forms". Australian & New Zealand Journal of Statistics, 47, 101-117. Available at   http://www.blackwell-synergy.com/rd.asp?code=anzs&goto=journal

  39. Sanford, A.D. and Martin, G.M., 2005, "Simulation-Based Bayesian Estimation of Affine Term Structure Models". Computational Statistics and Data Analysis, Special Issue on Computational Econometrics 2, 49, 527-554.  Available at  http://www.sciencedirect.com/science/journal/01679473

  40. Sanford, A.D. and Martin, G.M., 2006, "A Bayesian Comparison of Several Continuous Time Models of the Australian Short Rate", Accounting and Finance, 46, 309-326. Available at http://www.blackwell-synergy.com/toc/acfi/46/2

  41. Martin, G.M. , 2001, "Bayesian Analysis of a Fractional Cointegration Model", Econometric Reviews, 20, 217-234. (see http://www.dekker.com/servlet/product/productid/ETC/toc/)

  42. Martin, G.M. and V.L. Martin, 2000, "Bayesian Inference in the Triangular Cointegration Model Using a Jeffreys Prior", Communications in Statistics, Theory and Methods, 29, No. 8.,1759-1785.

  43. Martin, G.M., 2000, "US Deficit Sustainability: a New Approach Based on Multiple Endogenous Breaks", Journal of Applied Econometrics, 15, 83-105. Available at http://www3.interscience.wiley.com/

  44. Martin, G.M. and C.S. Forbes, 1999, "Using Simulation Methods for Bayesian Econometric Models: Inference, Development and Communication: Some Comments", Econometric Reviews, 18, No.1, 113-118.

  45. Lim, G.C., Lye, J., Martin, G.M. and V.L. Martin, 1998, "The Distribution of Exchange Rate Returns and the Pricing of Currency Options", Journal of International Economics, 45, 351-368. See http://www.elsevier.com/homepage/sae/econbase/inec/

Work in Progress, Under Submission or Under Revision



  1. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century", 2020 (joint with David Frazier and Christian P. Robert). Working paper downloadable at: : https://arxiv.org/abs/2004.06425


    2. Note that this paper has been broken up into two separate papers, both now published in Statistical Science. Martin, G.M., Frazier, D.T. and Robert, C.P., 2024, "Computing Bayes: From Then 'Til Now" and Martin, G.M., Frazier, D.T. and Robert, C.P., 2024, "Approximating Bayes in the 21st Century", as cited above. Paper 1. above presents a historical narrative about all computational developments since 1763; Paper 2. provides a detailed review of 21st century approximate methods.

    Statistical Society of Australia webinar: statsoc.org.au/forum-event-announcements/9271157. Related podcast: Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS): "Bayes' Theorem: The Past & the Future: acems.org.au/podcast/episode-32-bayes-theorem

  2. "The Impact of Sampling Variability on Estimated Combinations of Distributional Forecasts", 2022 (joint with.Ryan Zischke, David Frazier and Don Poskitt). Working paper downloadable at: https://arxiv.org/abs/2206.02376

  3. "Forecasting Observables in State Space Models: Does the Choice of Filter Matter?", 2020 (joint with Patrick Leung, Catherine Forbes and Brendan McCabe). Working paper version: Leung_et_al_Dec_2020.pdf

  4. "Solving the Forecast Combination Puzzle", 2023 (joint with David Frazier, Ryan Zischke and Don Poskitt). Working paper downloadable at: https://arxiv.org/abs/2308.05263

  5. "Optimal Probabilistic Forecasts for Risk Management", 2023 (joint with Yuru Sun, Ole Maneesoonthorn and Ruben Loaiza Maya). Working paper downloadable at: https://arxiv.org/abs/2303.01651

  6. "Estimation and Prediction in Mis-specified Fractionally Integrated Models with an Unknown Mean", 2023 (joint with Kanchana Nadarajah, Indee Perera and Don Poskitt)

  7. "Bootstrapping Weighted Combinations of Distributional Forecasts", 2023 (joint with Ryan Zischke, David Frazier and Don Poskitt)

  8. "Probabilistic Predictions of Option Prices Using Multiple Sources of Data" , 2023 (joint with Ole Maneesoonthorn, David Frazier and Ruben Loaiza Maya)

  9. "ABC-based Forecasting in Misspecified State Space Models", 2023 (joint with Chaya Weerasinghe, Ruben Loaiza Maya and David Frazier)



National Competitive Grants/Fellowships

  1. Australian Research Council Discovery Grant No. DP200101414, awarded for 2020 to 2022: "Loss-based Bayesian Prediction". Joint with Dr. David Frazier, Professor Rob Hyndman and Associate Professor Worapree (Ole) Maneesoonthorn (Universtiy of Melbourne).
  2. Australian Research Council Discovery Grant No. DP170100729, awarded for 2017 to 2019: "The Validation of Approximate Bayesian Computation: Theory and Practice". Joint with Dr. David Frazier, Professor Professor Christian P. Robert (University of Dauphine and CREST, Paris; University of Warwick) and Professor Eric Renault (Brown University).
  3. Australian Research Council Discovery Grant No. DP15010172, awarded for 2015 to 2017: "Approximate Bayesian Computation in State Space Models". Joint with Associate Professor Catherine Forbes, Professor Brendan McCabe (University of Liverpool) and Professor Christian P. Robert (University of Dauphine and CREST, Paris).
  4. Australian Research Council Discovery Grant No. DP120102344, awarded for 2012 to 2014: "Semi-Parametric Bootstrap-based Inference in Long Memory Models". Joint with Professor Don Poskitt.
  5. Australian Research Council Future Fellowship No. FT0991045, awarded for 2010 to 2013: "A Bayesian State Space Methodology for Forecasting Stock Market Volatility and Associated Time-varying Risk Premia".
  6. Australian Research Council Discovery Grant No. DP0985234, awarded for 2009, 2010 and 2011: "Non-parametric Estimation of Forecast Distributions in Non-Gaussian State Space Models". Joint with Dr. Catherine Forbes, Professor Mervyn Silvapulle and Professor Brendan McCabe (University of Liverpool)
  7. Australian Research Council Discovery Grant No. DP0664121, awarded for 2006, 2007 and 2008: "New Statistical Procedures for Analysing Dependence in Non-Gaussian Time Series Data". Joint with Associate Professor David Harris (University of Melbourne).
  8. Australian Research Council Discovery Grant No. DP0450257, awarded for 2004, 2005 and 2006: "New Approaches to the Analysis of Count Time Series". Joint with Associate Professor Ralph Snyder and Professor Rob Hyndman.
  9. Australian Research Council Discovery Grant No. DP0208333, awarded for 2002, 2003 and 2004: "Persistence in Economic Time Series: Interpretation, Measurement and Inference". Joint with Associate Professor David Harris (University of Melbourne).
  10. Large Australian Research Council  Grant No. A00103254 for 2001 and 2002: "Using Option Prices to Conduct Implicit Bayesian Analysis of Financial Returns".
  11. Large Australian Research Council Grant No. A79927170 for 1999 and 2000: "Multivariate Fractional Cointegration: Simulation-based Approaches to Testing and Estimation, with Applications to Exchange Rate Models". Joint with Dr. Nigel Wilkins.

 

Book Reviews

  1. Review of: Oxford Handbook of Bayesian Econometrics, 2011, edited by John Geweke, Gary Koop and Herman van Dijk. Published in: The Econometics Journal, 2012, 15, B11-B15.
  2. Review of: Bayesian Econometrics, 2003, by Gary Koop. Published in: Australian and New Zealand Journal of Statistics, 2004, 46, 512-514.
  3. Review of: Bayesian Economics Through Numerical Methods: a Guide to Econometrics and Decision Making with Prior Information, 1997, by J.H. Dorfman. Published in: Australian and New Zealand Journal of Statistics, 1999, 41, 120-122.

 

Recent Teaching

 

Current PhD Student