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Gael Margaret Martin

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. V. L. Martin & G. M. Martin & G. C. Lim, 2005. "Parametric pricing of higher order moments in S&P500 options," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 377-404.

    Mentioned in:

    1. Parametric pricing of higher order moments in S&P500 options (Journal of Applied Econometrics 2005) in ReplicationWiki ()
  2. Gael M. Martin, 2000. "US deficit sustainability: a new approach based on multiple endogenous breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 83-105.

    Mentioned in:

    1. US deficit sustainability: a new approach based on multiple endogenous breaks (Journal of Applied Econometrics 2000) in ReplicationWiki ()
  3. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.

    Mentioned in:

    1. Does the option market produce superior forecasts of noise-corrected volatility measures? (Journal of Applied Econometrics 2009) in ReplicationWiki ()

Working papers

  1. Gael M. Martin & Brendan P.M. McCabe & Worapree Maneesoonthorn & Christian P. Robert, 2014. "Approximate Bayesian Computation in State Space Models," Monash Econometrics and Business Statistics Working Papers 20/14, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Johan Dahlin & Mattias Villani & Thomas B. Schon, 2015. "Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods," Papers 1506.06975, arXiv.org, revised Jun 2017.
    2. Christian P. Robert, 2016. "Comment on: Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference," Journal of Financial Econometrics, Oxford University Press, vol. 14(2), pages 265-271.

  2. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2014. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Monash Econometrics and Business Statistics Working Papers 30/14, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Li, Xiafei & Liao, Yin & Lu, Xinjie & Ma, Feng, 2022. "An oil futures volatility forecast perspective on the selection of high-frequency jump tests," Energy Economics, Elsevier, vol. 116(C).
    2. Milan Ficura & Jiri Witzany, 2016. "Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(4), pages 278-301, August.
    3. Yuru Sun & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Gael M. Martin, 2023. "Optimal probabilistic forecasts for risk management," Papers 2303.01651, arXiv.org.
    4. Markus Bibinger & Christopher J. Neely & Lars Winkelmann, 2017. "Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book," Working Papers 2017-12, Federal Reserve Bank of St. Louis.
    5. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    6. Xinglin Yang & Ji Chen, 2021. "VIX term structure: The role of jump propagation risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 785-810, June.
    7. Patrick Leung & Catherine S. Forbes & Gael M Martin & Brendan McCabe, 2019. "Forecasting Observables with Particle Filters: Any Filter Will Do!," Monash Econometrics and Business Statistics Working Papers 22/19, Monash University, Department of Econometrics and Business Statistics.
    8. Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019. "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, vol. 35(2), pages 521-539.
    9. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    10. Gonzato, Luca & Sgarra, Carlo, 2021. "Self-exciting jumps in the oil market: Bayesian estimation and dynamic hedging," Energy Economics, Elsevier, vol. 99(C).
    11. Kwok, Simon, 2020. "Nonparametric Inference of Jump Autocorrelation," Working Papers 2020-09, University of Sydney, School of Economics, revised Jan 2021.
    12. Kaeck, Andreas & Rodrigues, Paulo & Seeger, Norman J., 2018. "Model Complexity and Out-of-Sample Performance: Evidence from S&P 500 Index Returns," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 1-29.

  3. K. Nadarajah & Gael M. Martin & D.S. Poskitt, 2014. "Issues in the Estimation of Mis-Specified Models of Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 18/14, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Kanchana Nadarajah & Gael M Martin & Donald S Poskitt, 2019. "Optimal Bias Correction of the Log-periodogram Estimator of the Fractional Parameter: A Jackknife Approach," Monash Econometrics and Business Statistics Working Papers 7/19, Monash University, Department of Econometrics and Business Statistics.

  4. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    2. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2013. "Higher-Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 25/13, Monash University, Department of Econometrics and Business Statistics.

  5. D.S. Poskitt & Simone D. Grose & Gael M. Martin, 2012. "Higher Order Improvements of the Sieve Bootstrap for Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 9/12, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Neil Kellard & Denise Osborn & Jerry Coakley & Simone D. Grose & Gael M. Martin & Donald S. Poskitt, 2015. "Bias Correction of Persistence Measures in Fractionally Integrated Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(5), pages 721-740, September.
    2. D.S. Poskitt & Gael M. Martin & Simone D. Grose, 2012. "Bias Reduction of Long Memory Parameter Estimators via the Pre-filtered Sieve Bootstrap," Monash Econometrics and Business Statistics Working Papers 8/12, Monash University, Department of Econometrics and Business Statistics.
    3. La Vecchia, Davide & Ronchetti, Elvezio, 2019. "Saddlepoint approximations for short and long memory time series: A frequency domain approach," Journal of Econometrics, Elsevier, vol. 213(2), pages 578-592.
    4. Masoud M. Nasari & Mohamedou Ould-Haye, 2022. "Confidence intervals with higher accuracy for short and long-memory linear processes," Statistical Papers, Springer, vol. 63(4), pages 1187-1220, August.
    5. Arteche, Josu, 2024. "Bootstrapping long memory time series: Application in low frequency estimators," Econometrics and Statistics, Elsevier, vol. 29(C), pages 1-15.

  6. Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Gael M. Martin & Brendan P.M. McCabe & Worapree Maneesoonthorn & Christian P. Robert, 2014. "Approximate Bayesian Computation in State Space Models," Monash Econometrics and Business Statistics Working Papers 20/14, Monash University, Department of Econometrics and Business Statistics.
    2. Gael M. Martin & Brendan P.M. McCabe & David T. Frazier & Worapree Maneesoonthorn & Christian P. Robert, 2016. "Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models," Monash Econometrics and Business Statistics Working Papers 09/16, Monash University, Department of Econometrics and Business Statistics.
    3. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    4. Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
    5. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    6. Patrick Leung & Catherine S. Forbes & Gael M Martin & Brendan McCabe, 2019. "Forecasting Observables with Particle Filters: Any Filter Will Do!," Monash Econometrics and Business Statistics Working Papers 22/19, Monash University, Department of Econometrics and Business Statistics.
    7. Zhang, Yi & Zhou, Long & Liu, Zhidong & Wu, Baoxiu, 2024. "Herding behaviour towards high order systematic risks and the contagion Effect—Evidence from BRICS stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    8. Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.
    9. Patrick Leung & Catherine S. Forbes & Gael M. Martin & Brendan McCabe, 2016. "Data-driven particle Filters for particle Markov Chain Monte Carlo," Monash Econometrics and Business Statistics Working Papers 17/16, Monash University, Department of Econometrics and Business Statistics.

  7. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes & Simone Grose, 2010. "Probabilistic Forecasts of Volatility and its Risk Premia," Monash Econometrics and Business Statistics Working Papers 22/10, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Hong Wang & Catherine S. Forbes & Jean-Pierre Fenech & John Vaz, 2018. "The determinants of bank loan recovery rates in good times and bad - new evidence," Papers 1804.07022, arXiv.org.
    2. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes, 2017. "High-Frequency Jump Tests: Which Test Should We Use?," Papers 1708.09520, arXiv.org, revised Jan 2020.
    3. Worapree Maneesoonthorn & David T. Frazier & Gael M. Martin, 2024. "Probabilistic Predictions of Option Prices Using Multiple Sources of Data," Papers 2412.00658, arXiv.org.
    4. David Harris & Gael M. Martin & Indeewara Perera & Don S. Poskitt, 2017. "Construction and visualization of optimal confidence sets for frequentist distributional forecasts," Monash Econometrics and Business Statistics Working Papers 9/17, Monash University, Department of Econometrics and Business Statistics.
    5. Worapree Maneesoonthorn & Gael M Martin & Catherine S Forbes, 2018. "Dynamic price jumps: The performance of high frequency tests and measures, and the robustness of inference," Monash Econometrics and Business Statistics Working Papers 17/18, Monash University, Department of Econometrics and Business Statistics.
    6. Yuru Sun & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Gael M. Martin, 2023. "Optimal probabilistic forecasts for risk management," Papers 2303.01651, arXiv.org.
    7. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    8. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    9. Perera, Indeewara & Silvapulle, Mervyn J., 2021. "Bootstrap based probability forecasting in multiplicative error models," Journal of Econometrics, Elsevier, vol. 221(1), pages 1-24.
    10. Worapree Maneesoonthorn & Catherine S. Forbes & Gael M. Martin, 2013. "Inference on Self-Exciting Jumps in Prices and Volatility using High Frequency Measures," Monash Econometrics and Business Statistics Working Papers 28/13, Monash University, Department of Econometrics and Business Statistics.
    11. Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019. "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, vol. 35(2), pages 521-539.
    12. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    13. Hattori, Masazumi & Shim, Ilhyock & Sugihara, Yoshihiko, 2021. "Cross-stock market spillovers through variance risk premiums and equity flows," Journal of International Money and Finance, Elsevier, vol. 119(C).
    14. Perera, Indeewara & Koul, Hira L., 2017. "Fitting a two phase threshold multiplicative error model," Journal of Econometrics, Elsevier, vol. 197(2), pages 348-367.
    15. Hattori, Masazumi & Shim, Ilhyock & Sugihara, Yoshihiko, 2016. "Volatility Contagion across the Equity Markets of Developed and Emerging Market Economies," ADBI Working Papers 590, Asian Development Bank Institute.
    16. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes, 2017. "Dynamic asset price jumps and the performance of high frequency tests and measures," Monash Econometrics and Business Statistics Working Papers 14/17, Monash University, Department of Econometrics and Business Statistics.

  8. Brendan P.M. McCabe & Gael M. Martin & David Harris, 2009. "Optimal Probabilistic Forecasts for Counts," Monash Econometrics and Business Statistics Working Papers 7/09, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Barczy, M. & Ispány, M. & Pap, G., 2011. "Asymptotic behavior of unstable INAR(p) processes," Stochastic Processes and their Applications, Elsevier, vol. 121(3), pages 583-608, March.
    2. Dag Tjøstheim, 2012. "Some recent theory for autoregressive count time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 413-438, September.
    3. Yousung Park & Hee-Young Kim, 2012. "Diagnostic checks for integer-valued autoregressive models using expected residuals," Statistical Papers, Springer, vol. 53(4), pages 951-970, November.

  9. Gael M. Martin & Andrew Reidy & Jill Wright, 2007. "Does the Option Market Produce Superior Forecasts of Noise-Corrected Volatility Measures?," Monash Econometrics and Business Statistics Working Papers 5/07, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Viktor Todorov & Yang Zhang, 2022. "Information gains from using short‐dated options for measuring and forecasting volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 368-391, March.
    2. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes, 2017. "High-Frequency Jump Tests: Which Test Should We Use?," Papers 1708.09520, arXiv.org, revised Jan 2020.
    3. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    4. K. Nadarajah & Gael M. Martin & D.S. Poskitt, 2014. "Issues in the Estimation of Mis-Specified Models of Fractionally Integrated Processes," Monash Econometrics and Business Statistics Working Papers 18/14, Monash University, Department of Econometrics and Business Statistics.
    5. Le-Yu Chen & Jerzy Szroeter, 2009. "Hypothesis testing of multiple inequalities: the method of constraint chaining," CeMMAP working papers CWP13/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Baruník, Jozef & Hlínková, Michaela, 2016. "Revisiting the long memory dynamics of the implied–realized volatility relationship: New evidence from the wavelet regression," Economic Modelling, Elsevier, vol. 54(C), pages 503-514.
    7. Worapree Maneesoonthorn & Gael M Martin & Catherine S Forbes, 2018. "Dynamic price jumps: The performance of high frequency tests and measures, and the robustness of inference," Monash Econometrics and Business Statistics Working Papers 17/18, Monash University, Department of Econometrics and Business Statistics.
    8. Gonzalez-Perez, Maria T., 2015. "Model-free volatility indexes in the financial literature: A review," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 141-159.
    9. Taylor, Stephen J. & Yadav, Pradeep K. & Zhang, Yuanyuan, 2010. "The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 871-881, April.
    10. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    11. Barunik, Jozef & Barunikova, Michaela, 2015. "Revisiting the long memory dynamics of implied-realized volatility relation: A new evidence from wavelet band spectrum regression," FinMaP-Working Papers 43, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    12. Massimo Guidolin & Daniel L. Thornton, 2010. "Predictions of short-term rates and the expectations hypothesis," Working Papers 2010-013, Federal Reserve Bank of St. Louis.
    13. Jozef Barunik & Michaela Barunikova, 2012. "Revisiting the fractional cointegrating dynamics of implied-realized volatility relation with wavelet band spectrum regression," Papers 1208.4831, arXiv.org, revised Feb 2013.
    14. Bams, Dennis & Blanchard, Gildas & Lehnert, Thorsten, 2017. "Volatility measures and Value-at-Risk," International Journal of Forecasting, Elsevier, vol. 33(4), pages 848-863.
    15. Worapree Maneesoonthorn & Gael M. Martin & Catherine S. Forbes, 2017. "Dynamic asset price jumps and the performance of high frequency tests and measures," Monash Econometrics and Business Statistics Working Papers 14/17, Monash University, Department of Econometrics and Business Statistics.

  10. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Bu, Ruijun & McCabe, Brendan, 2008. "Model selection, estimation and forecasting in INAR(p) models: A likelihood-based Markov Chain approach," International Journal of Forecasting, Elsevier, vol. 24(1), pages 151-162.
    2. Ralph D. Snyder & Adrian Beaumont, 2007. "A Comparison of Methods for Forecasting Demand for Slow Moving Car Parts," Monash Econometrics and Business Statistics Working Papers 15/07, Monash University, Department of Econometrics and Business Statistics.

  11. Chris M Strickland & Gael Martin & Catherine S Forbes, 2006. "Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models," Monash Econometrics and Business Statistics Working Papers 22/06, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Brendan P.M. McCabe & Gael Martin & Keith Freeland, 2010. "A Quasi-locally Most powerful Test for Correlation in the conditional Variance of Positive Data," Monash Econometrics and Business Statistics Working Papers 2/10, Monash University, Department of Econometrics and Business Statistics.
    3. Gregor Kastner & Sylvia Fruhwirth-Schnatter, 2017. "Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models," Papers 1706.05280, arXiv.org.
    4. Stefano Grassi & Tommaso Proietti, 2010. "Characterizing economic trends by Bayesian stochastic model specification search," EERI Research Paper Series EERI_RP_2010_25, Economics and Econometrics Research Institute (EERI), Brussels.
    5. Jouchi Nakajima & Yasuhiro Omori, 2010. "Stochastic Volatility Model with Leverage and Asymmetrically Heavy-Tailed Error Using GH Skew Student's t-Distribution Models," CIRJE F-Series CIRJE-F-738, CIRJE, Faculty of Economics, University of Tokyo.
    6. Nakajima, Jouchi & Omori, Yasuhiro, 2012. "Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3690-3704.
    7. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    8. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    9. Kreuzer, Alexander & Czado, Claudia, 2021. "Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 130-150.
    10. Gregor Kastner & Sylvia Fruhwirth-Schnatter & Hedibert Freitas Lopes, 2016. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models," Papers 1602.08154, arXiv.org, revised Jul 2017.
    11. McCausland, William J., 2012. "The HESSIAN method: Highly efficient simulation smoothing, in a nutshell," Journal of Econometrics, Elsevier, vol. 168(2), pages 189-206.
    12. Tommaso, Proietti & Stefano, Grassi, 2010. "Bayesian stochastic model specification search for seasonal and calendar effects," MPRA Paper 27305, University Library of Munich, Germany.
    13. Strickland, Christopher & Burdett, Robert & Mengersen, Kerrie & Denham, Robert, 2014. "PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i06).
    14. Strickland, Chris M. & Turner, Ian. W. & Denham, Robert & Mengersen, Kerrie L., 2009. "Efficient Bayesian estimation of multivariate state space models," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4116-4125, October.

  12. B.P.M. McCabe & G.M. Martin & R.K. Freeland, 2004. "Testing for Dependence in Non-Gaussian Time Series Data," Monash Econometrics and Business Statistics Working Papers 13/04, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. McCabe, B.P.M. & Martin, G.M., 2005. "Bayesian predictions of low count time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 315-330.
    2. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.
    3. Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006. "Bayesian analysis of the stochastic conditional duration model," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2247-2267, May.
    4. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.

  13. Andrew D. Sanford & Gael M. Martin, 2003. "Simulation-Based Bayesian Estimation of Affine Term Structure Models," Monash Econometrics and Business Statistics Working Papers 15/03, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Andrew D. Sanford & Gael Martin, 2004. "Bayesian Analysis of Continuous Time Models of the Australian Short Rate," Monash Econometrics and Business Statistics Working Papers 11/04, Monash University, Department of Econometrics and Business Statistics.
    3. Yasuhiro Omori & Toshiaki Watanabe, 2007. "Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-Space Model with Correlated Errors," CARF F-Series CARF-F-104, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Juneja, Januj, 2017. "Invariance, observational equivalence, and identification: Some implications for the empirical performance of affine term structure models," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 292-305.
    5. Jang, Bong-Gyu & Yoon, Ji Hee, 2010. "Analytic valuation formulas for range notes and an affine term structure model with jump risks," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2132-2145, September.
    6. Richard Finlay & Mark Chambers, 2009. "A Term Structure Decomposition of the Australian Yield Curve," The Economic Record, The Economic Society of Australia, vol. 85(271), pages 383-400, December.
    7. Collin-Dufresne, Pierre & Goldstein, Robert S. & Jones, Christopher S., 2009. "Can interest rate volatility be extracted from the cross section of bond yields?," Journal of Financial Economics, Elsevier, vol. 94(1), pages 47-66, October.
    8. Peter Feldhütter, 2016. "Can Affine Models Match the Moments in Bond Yields?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-56, June.
    9. Andrew D. Sanford & Gael M. Martin, 2006. "Bayesian comparison of several continuous time models of the Australian short rate," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 46(2), pages 309-326, June.

  14. Chris M. Strickland & Catherine S. Forbes & Gael M. Martin, 2003. "Bayesian Analysis of the Stochastic Conditional Duration Model," Monash Econometrics and Business Statistics Working Papers 14/03, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Dinghai Xu & John Knight & Tony S. Wirjanto, 2011. "Asymmetric Stochastic Conditional Duration Model--A Mixture-of-Normal Approach," Journal of Financial Econometrics, Oxford University Press, vol. 9(3), pages 469-488, Summer.
    2. Bauwens, L. & Galli, F., 2009. "Efficient importance sampling for ML estimation of SCD models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1974-1992, April.
    3. Brendan P.M. McCabe & Gael Martin & Keith Freeland, 2010. "A Quasi-locally Most powerful Test for Correlation in the conditional Variance of Positive Data," Monash Econometrics and Business Statistics Working Papers 2/10, Monash University, Department of Econometrics and Business Statistics.
    4. Fok, D. & Paap, R. & Franses, Ph.H.B.F., 2002. "Modeling dynamic effects of promotion on interpurchase times," Econometric Institute Research Papers EI 2002-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Joshua Chan & Rodney Strachan, 2012. "Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods," CAMA Working Papers 2012-13, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Chris M Strickland & Gael Martin & Catherine S Forbes, 2006. "Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models," Monash Econometrics and Business Statistics Working Papers 22/06, Monash University, Department of Econometrics and Business Statistics.
    7. Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2016. "A Multiscale Stochastic Conditional Duration Model," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-28, December.
    8. Bayarri, M.J. & Castellanos, M.E. & Morales, J., 2006. "MCMC methods to approximate conditional predictive distributions," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 621-640, November.
    9. Adriana Bortoluzzo & Pedro Morettin & Clelia Toloi, 2010. "Time-varying autoregressive conditional duration model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 847-864.
    10. Strid, Ingvar, 2010. "Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2814-2835, November.
    11. Fabrizio Cipollini & Giampiero M. Gallo, 2009. "Automated Variable Selection in Vector Multiplicative Error Models," Econometrics Working Papers Archive wp2009_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    12. Roman Huptas, 2019. "Point forecasting of intraday volume using Bayesian autoregressive conditional volume models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(4), pages 293-310, July.
    13. Tomoki Toyabe & Teruo Nakatsuma, 2022. "Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information," JRFM, MDPI, vol. 15(10), pages 1-25, October.
    14. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.
    15. Trojan, Sebastian, 2014. "Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts," Economics Working Paper Series 1425, University of St. Gallen, School of Economics and Political Science.
    16. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    17. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    18. Patrick Leung & Catherine S. Forbes & Gael M Martin & Brendan McCabe, 2019. "Forecasting Observables with Particle Filters: Any Filter Will Do!," Monash Econometrics and Business Statistics Working Papers 22/19, Monash University, Department of Econometrics and Business Statistics.
    19. Chaya Weerasinghe & Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier, 2023. "ABC-based Forecasting in State Space Models," Monash Econometrics and Business Statistics Working Papers 12/23, Monash University, Department of Econometrics and Business Statistics.
    20. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    21. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    22. Patrick Leung & Catherine S. Forbes & Gael M. Martin & Brendan McCabe, 2016. "Data-driven particle Filters for particle Markov Chain Monte Carlo," Monash Econometrics and Business Statistics Working Papers 17/16, Monash University, Department of Econometrics and Business Statistics.
    23. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.
    24. Roman Huptas, 2014. "Bayesian Estimation and Prediction for ACD Models in the Analysis of Trade Durations from the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(4), pages 237-273, December.
    25. McCausland, William J., 2012. "The HESSIAN method: Highly efficient simulation smoothing, in a nutshell," Journal of Econometrics, Elsevier, vol. 168(2), pages 189-206.
    26. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.
    27. Strickland, Chris M. & Turner, Ian. W. & Denham, Robert & Mengersen, Kerrie L., 2009. "Efficient Bayesian estimation of multivariate state space models," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4116-4125, October.
    28. Bortoluzzo, Adriana B. & Morettin, Pedro A. & Toloi, Clelia M. C., 2008. "Time-Varying Autoregressive Conditional Duration Model," Insper Working Papers wpe_174, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

  15. Gael M. Martin & Catherine S. Forbes & Vance L. Martin, 2003. "Implicit Bayesian Inference Using Option Prices," Monash Econometrics and Business Statistics Working Papers 5/03, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Gradojevic Nikola, 2016. "Multi-criteria classification for pricing European options," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 123-139, April.
    2. Jeroen V.K. Rombouts & Lars Stentoft, 2009. "Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models," CREATES Research Papers 2009-07, Department of Economics and Business Economics, Aarhus University.
    3. G.C. Lim & G.M. Martin & V.L. Martin, 2002. "Parametric Pricing of Higher Order Moments in S&P500 Options," Monash Econometrics and Business Statistics Working Papers 1/02, Monash University, Department of Econometrics and Business Statistics.
    4. Lim, G.C. & Martin, G.M. & Martin, V.L., 2006. "Pricing currency options in the presence of time-varying volatility and non-normalities," Journal of Multinational Financial Management, Elsevier, vol. 16(3), pages 291-314, July.
    5. Worapree Maneesoonthorn & David T. Frazier & Gael M. Martin, 2024. "Probabilistic Predictions of Option Prices Using Multiple Sources of Data," Papers 2412.00658, arXiv.org.
    6. Catherine S. Forbes & Gael M. Martin & Jill Wright, 2007. "Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 387-418.
    7. Renée Fry-McKibbin & Vance Martin & Chrismin Tang, 2013. "Financial Contagion and Asset Pricing," CAMA Working Papers 2013-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Anthony D. Hall & Paul Kofman & Steve Manaster, 2001. "Migration of Price Discovery With Constrained Futures Markets," Research Paper Series 70, Quantitative Finance Research Centre, University of Technology, Sydney.
    9. C.S. Forbes & G.M. Martin & J. Wright, 2002. "Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices," Monash Econometrics and Business Statistics Working Papers 2/02, Monash University, Department of Econometrics and Business Statistics.
    10. Tak Kuen Siu, 2024. "Bayesian Lower and Upper Estimates for Ether Option Prices with Conditional Heteroscedasticity and Model Uncertainty," JRFM, MDPI, vol. 17(10), pages 1-32, September.
    11. Shu Wing Ho & Alan Lee & Alastair Marsden, 2011. "Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models," JRFM, MDPI, vol. 4(1), pages 1-23, December.
    12. Lisha Lin & Yaqiong Li & Rui Gao & Jianhong Wu, 2019. "The Numerical Simulation of Quanto Option Prices Using Bayesian Statistical Methods," Papers 1910.04075, arXiv.org.

  16. Catherine S. Forbes & Gael M. Martin & Jill Wright, 2003. "Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices: Application of a Bivariate Kalman Filter," Monash Econometrics and Business Statistics Working Papers 17/03, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. G.C. Lim & G.M. Martin & V.L. Martin, 2002. "Parametric Pricing of Higher Order Moments in S&P500 Options," Monash Econometrics and Business Statistics Working Papers 1/02, Monash University, Department of Econometrics and Business Statistics.
    2. Hanno Gottschalk & Elpida Nizami & Marius Schubert, 2016. "Option Pricing in Markets with Unknown Stochastic Dynamics," Papers 1602.04848, arXiv.org, revised Jan 2017.

  17. David B. Flynn & Simone D. Grose & Gael M. Martin & Vance L. Martin, 2003. "Pricing Australian S&P200 Options: A Bayesian Approach Based on Generalized Distributional Forms," Monash Econometrics and Business Statistics Working Papers 6/03, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Stamey, James & Gerlach, Richard, 2007. "Bayesian sample size determination for case-control studies with misclassification," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2982-2992, March.
    2. Shu Wing Ho & Alan Lee & Alastair Marsden, 2011. "Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models," JRFM, MDPI, vol. 4(1), pages 1-23, December.

  18. B.P.M. McCabe & G.M. Martin & A.R. Tremayne, 2003. "Persistence and Nonstationary Models," Monash Econometrics and Business Statistics Working Papers 16/03, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. B. P. M. McCabe & G. M. Martin & A. R. Tremayne, 2005. "Assessing Persistence In Discrete Nonstationary Time‐Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 305-317, March.

  19. G.C. Lim & G.M. Martin & V.L. Martin, 2002. "Parametric Pricing of Higher Order Moments in S&P500 Options," Monash Econometrics and Business Statistics Working Papers 1/02, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Arismendi, Juan & Genaro, Alan De, 2016. "A Monte Carlo multi-asset option pricing approximation for general stochastic processes," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 75-99.
    2. Monica Billio & Bertrand Maillet & Loriana Pelizzon, 2022. "A meta-measure of performance related to both investors and investments characteristics," Annals of Operations Research, Springer, vol. 313(2), pages 1405-1447, June.
    3. Vance Martin & G.C. Lim & Esfandiar Maasoumi, 2004. "Discounting The Equity Premium Puzzle," Econometric Society 2004 Australasian Meetings 331, Econometric Society.
    4. Martin, G.M. & Forbes, C.S. & Martin, V.L., 2000. "Implicit Bayesian Inference Using Option Prices," Monash Econometrics and Business Statistics Working Papers 5/00, Monash University, Department of Econometrics and Business Statistics.
    5. Maasoumi, Esfandiar & Lim, G.C. & Martin, Vance, 2006. "A reexamination of the equity-premium puzzle: A robust non-parametric approach," Departmental Working Papers 0604, Southern Methodist University, Department of Economics.
    6. Carol Alexander & Emese Lazar, 2006. "Normal mixture GARCH(1,1): applications to exchange rate modelling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 307-336, April.
    7. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    8. Lim, G.C. & Martin, G.M. & Martin, V.L., 2006. "Pricing currency options in the presence of time-varying volatility and non-normalities," Journal of Multinational Financial Management, Elsevier, vol. 16(3), pages 291-314, July.
    9. Gael M. Martin & Brendan P.M. McCabe & Worapree Maneesoonthorn & Christian P. Robert, 2014. "Approximate Bayesian Computation in State Space Models," Monash Econometrics and Business Statistics Working Papers 20/14, Monash University, Department of Econometrics and Business Statistics.
    10. Gael M. Martin & Andrew Reidy & Jill Wright, 2006. "Assessing the Impact of Market Microstructure Noise and Random Jumps on the Relative Forecasting Performance of Option-Implied and Returns-Based Volatility," Monash Econometrics and Business Statistics Working Papers 10/06, Monash University, Department of Econometrics and Business Statistics.
    11. Ryszard Kokoszczyński & Paweł Sakowski & Robert Ślepaczuk, 2010. "Which Option Pricing Model is the Best? High Frequency Data for Nikkei225 Index Options," Working Papers 2010-16, Faculty of Economic Sciences, University of Warsaw.
    12. Worapree Maneesoonthorn & David T. Frazier & Gael M. Martin, 2024. "Probabilistic Predictions of Option Prices Using Multiple Sources of Data," Papers 2412.00658, arXiv.org.
    13. Ángel León & Javier Mencía & Enrique Sentana, 2007. "Parametric properties of semi-nonparametric distributions, with applications to option valuation," Working Papers 0707, Banco de España.
    14. Cortés, Lina M. & Mora-Valencia, Andrés & Perote, Javier, 2020. "Retrieving the implicit risk neutral density of WTI options with a semi-nonparametric approach," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    15. Juan Arismendi, 2014. "A Multi-Asset Option Approximation for General Stochastic Processes," ICMA Centre Discussion Papers in Finance icma-dp2014-03, Henley Business School, University of Reading.
    16. Catherine S. Forbes & Gael M. Martin & Jill Wright, 2007. "Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 387-418.
    17. J. C. Arismendi & Marcel Prokopczuk, 2016. "A moment-based analytic approximation of the risk-neutral density of American options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 23(6), pages 409-444, November.
    18. Renée Fry-McKibbin & Vance Martin & Chrismin Tang, 2013. "Financial Contagion and Asset Pricing," CAMA Working Papers 2013-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    19. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    20. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    21. Nizar Riane & Claire David, 2024. "Generalized measure Black-Scholes equation: Towards option self-similar pricing," Papers 2404.05214, arXiv.org.
    22. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    23. Ryszard Kokoszczyński & Paweł Sakowski & Robert Ślepaczuk, 2017. "Which Option Pricing Model Is the Best? HF Data for Nikkei 225 Index Options," Central European Economic Journal, Sciendo, vol. 4(51), pages 18-39.
    24. Nizar Riane, 2023. "The inverse Black-Scholes problem in Radon measures space revisited: towards a new measure of market uncertainty," Papers 2303.16773, arXiv.org.
    25. Lina M. Cortés & Javier Perote & Andrés Mora-Valencia, 2017. "Implicit probability distribution for WTI options: The Black Scholes vs. the semi-nonparametric approach," Documentos de Trabajo de Valor Público 15923, Universidad EAFIT.
    26. Ryszard Kokoszczyński & Paweł Sakowski & Robert Ślepaczuk, 2010. "Midquotes or Transactional Data? The Comparison of Black Model on HF Data," Working Papers 2010-15, Faculty of Economic Sciences, University of Warsaw.

  20. C.S. Forbes & G.M. Martin & J. Wright, 2002. "Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices," Monash Econometrics and Business Statistics Working Papers 2/02, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2003. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatily Model," Working Papers 07/2003, University of Verona, Department of Economics.
    2. Nunzio Cappuccio & Diego Lubian & Davide Raggi, 2006. "Investigating asymmetry in US stock market indexes: evidence from a stochastic volatility model," Applied Financial Economics, Taylor & Francis Journals, vol. 16(6), pages 479-490.
    3. Silvia Centanni, 2011. "Computing option values by pricing kernel with a stochatic volatility model," Working Papers 05/2011, University of Verona, Department of Economics.

  21. Antonio, J. & Martin, G., 2001. "Spot Market Competition with Stranded Costs in the Spanish Electricity Industry," Papers 0106, Centro de Estudios Monetarios Y Financieros-.

    Cited by:

    1. Aitor Ciarreta & María Espinosa, 2010. "Market power in the Spanish electricity auction," Journal of Regulatory Economics, Springer, vol. 37(1), pages 42-69, February.
    2. Josep Pijoan-Mas, 2006. "Precautionary Savings or Working Longer Hours?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 9(2), pages 326-352, April.

  22. Martin, G.M., 1998. "U.S. Deficit Sustainability: A New Approach Based on Multiple Endogenous Breaks," Monash Econometrics and Business Statistics Working Papers 1/98, Monash University, Department of Econometrics and Business Statistics.

    Cited by:

    1. Gary Koop & Roberto Leon-Gonzalez & Rodney Strachan, 2011. "Bayesian Inference in the Time Varying Cointegration Model," Working Papers 1121, University of Strathclyde Business School, Department of Economics.
    2. Mark J. Holmes & Theodore Panagiotidis & Jesus Otero, 2008. "Are EU budgets stationary?," Discussion Paper Series 2008_07, Department of Economics, University of Macedonia, revised Sep 2008.
    3. Oscar Bajo-Rubio & Carmen Díaz-Roldán & Vicente Esteve, 2010. "Government deficit sustainability, and monetary versus fiscal dominance: The case of Spain, 1850-2000," Working Papers 10-04, Asociación Española de Economía y Finanzas Internacionales.
    4. Gollagari Ramakrishna & Berhanu Asefa Gizaw & Ch. Paramaiah & Robinson Joseph & Sania Khan, 2023. "Import Tariff Reduction and Fiscal Sustainability: A Macro-Econometric Modelling for Ethiopia," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    5. Jochmann, Markus & Koop, Gary, 2011. "Regime-Switching Cointegration," SIRE Discussion Papers 2011-60, Scottish Institute for Research in Economics (SIRE).
    6. Oscar Bajo-Rubio & Carmen Díaz-Roldán & Vicente Esteve, 2010. "On the Sustainability of Government Deficits: Some Long-Term Evidence for Spain, 1850–2000," Journal of Applied Economics, Taylor & Francis Journals, vol. 13(2), pages 263-281, November.
    7. Aviral Kumar Tiwari, 2012. "Debt Sustainability in India: Empirical Evidence Estimating Time-Varying Parameters," Economics Bulletin, AccessEcon, vol. 32(2), pages 1133-1141.
    8. Gordon L. Brady & Cosimo Magazzino, 2018. "Sustainability and comovement of government debt in EMU Countries: A panel data analysis," Southern Economic Journal, John Wiley & Sons, vol. 85(1), pages 189-202, July.
    9. Mark J. Holmes & Jesús Otero & Theodore Panagiotidis, 2009. "Are Eu Budget Deficits Stationary?," Working Paper series 17_09, Rimini Centre for Economic Analysis.
    10. Ahmad Zubaidi Baharumshah & Evan Lau, 2005. "Regime Changes And The Sustainability Of Fiscal Imbalance In East Asian Countries," Macroeconomics 0504001, University Library of Munich, Germany.
    11. SILVESTRINI, Andrea, 2010. "Testing fiscal sustainability in Poland: a Bayesian analysis of cointegration," LIDAM Reprints CORE 2220, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. Stephen Marks, 2004. "Fiscal sustainability and solvency: theory and recent experience in Indonesia," Bulletin of Indonesian Economic Studies, Taylor & Francis Journals, vol. 40(2), pages 227-242.
    13. Magazzino, Cosimo & Brady, Gordon L. & Forte, Francesco, 2019. "A panel data analysis of the fiscal sustainability of G-7 countries," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    14. Abderrahim Chibi & Sidi Mohamed Chekouri & Mohamed Benbouziane, 2015. "Assessing Fiscal Sustainability in Algeria: a Nonlinear Approach," Working Papers 962, Economic Research Forum, revised Oct 2015.
    15. Regina Escario & Mar�a Dolores Gadea & Marcela Sabat�, 2009. "Government Solvency or just Pseudo-Sustainability? a Long-Run Multicointegration Approach for Spain," Documentos de Trabajo dt2009-07, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    16. Tilak Abeysinghe & Ananda Jayawickrama, 2013. "A segmented trend model to assess fiscal sustainability: The US experience 1929–2009," Empirical Economics, Springer, vol. 44(3), pages 1129-1141, June.
    17. Tsong, Ching-Chuan & Wu, Chien-Wei & Chiu, Hsien-Hung & Lee, Cheng-Feng, 2013. "Covariate unit root tests under structural change and asymmetric STAR dynamics," Economic Modelling, Elsevier, vol. 33(C), pages 101-112.
    18. Joakim Westerlund & Silika Prohl, 2010. "Panel cointegration tests of the sustainability hypothesis in rich OECD countries," Applied Economics, Taylor & Francis Journals, vol. 42(11), pages 1355-1364.
    19. Koop, G. & Strachan, R.W. & van Dijk, H.K. & Villani, M., 2005. "Bayesian approaches to cointegratrion," Econometric Institute Research Papers EI 2005-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    20. Rathnayake, Anuruddhi Shanika K, 2020. "Sustainability of the fiscal imbalance and public debt under fiscal policy asymmetries in Sri Lanka," Journal of Asian Economics, Elsevier, vol. 66(C).
    21. Jochmann, Markus & Koop, Gary & Strachan, Rodney W., 2010. "Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks," International Journal of Forecasting, Elsevier, vol. 26(2), pages 326-347, April.
    22. Thanh Dat Nguyen & Sandy Suardi & Chew Lian Chua, 2017. "The Behavior Of U.S. Public Debt And Deficits During The Global Financial Crisis," Contemporary Economic Policy, Western Economic Association International, vol. 35(1), pages 201-215, January.
    23. Oscar Bajo-Rubio & Carmen Diaz-Roldan & Vicente Esteve, 2008. "US deficit sustainability revisited: a multiple structural change approach," Applied Economics, Taylor & Francis Journals, vol. 40(12), pages 1609-1613.
    24. Bogdan Dima & Oana Lobonţ & Cristina Nicolescu, 2009. "The Fiscal Revenues And Public Expenditures: Is Their Evolution Sustenable? The Romanian Case," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 1(11), pages 1-42.
    25. Ananda Jayawickrama & Tilak Abeysinghe, 2006. "Sustainability of Fiscal Deficits : The US Experience 1929-2004," Governance Working Papers 21924, East Asian Bureau of Economic Research.
    26. Gael Martin, 2001. "Bayesian Analysis Of A Fractional Cointegration Model," Econometric Reviews, Taylor & Francis Journals, vol. 20(2), pages 217-234.
    27. Oscar Bajo-Rubio & Carmen Díaz-Roldán & Vicente Esteve, 2003. "Is the Budget Deficit Sustainable when Fiscal Policy is nonlinear? The Case of Spain, 1961-2001," Economic Working Papers at Centro de Estudios Andaluces E2003/32, Centro de Estudios Andaluces.
    28. Hauzenberger Niko & Huber Florian & Pfarrhofer Michael & Zörner Thomas O., 2021. "Stochastic model specification in Markov switching vector error correction models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    29. Fincke Bettina & Greiner Alfred, 2011. "Debt Sustainability in Selected Euro Area Countries: Empirical Evidence Estimating Time-Varying Parameters," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-23, May.
    30. Philip Arestis & Andrea Cipollini & Bassam Fattouh, 2002. "Threshold Effects in the U.S. Budget Deficit," Economics Working Paper Archive wp_358, Levy Economics Institute.
    31. Süleyman Bolat & Aviral Kumar Tiwari & Mihai Mutascu, 2014. "The behaviour of US and UK public debt: further evidence based on time varying parameters," Working Papers halshs-01107962, HAL.
    32. Mr. Evan C Tanner & Issouf Samaké, 2006. "Probabilistic Sustainability of Public Debt: A Vector Autoregression Approach for Brazil, Mexico, and Turkey," IMF Working Papers 2006/295, International Monetary Fund.
    33. Anita Rath & Arpit Sachan, 2022. "Emerging Issues in Fiscal Sustainability in India: A Study of Central Government Finances, 1979–1980 to 2018–2019," South Asian Journal of Macroeconomics and Public Finance, , vol. 11(1), pages 39-68, June.
    34. Hatzinikolaou, Dimitris & Simos, Theodore, 2011. "A new test for deficit sustainability and its application to US data," MPRA Paper 45393, University Library of Munich, Germany, revised 17 Jan 2012.
    35. António Afonso & João Tovar Jalles, 2011. "A Longer-run Perspective on Fiscal Sustainability," Working Papers Department of Economics 2011/17, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    36. Abderrahim Chibi & Sidi Mohamed Chekouri & Mohamed Benbouziane, 2019. "The dynamics of fiscal policy in Algeria: sustainability and structural change," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-27, December.
    37. António Afonso, 2005. "Fiscal Sustainability: The Unpleasant European Case," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 61(1), pages 19-44, March.
    38. Vasco Gabriel & Pataaree Sangduan, 2009. "Assessing Fiscal Sustainability Subject to Policy Changes: a Markov Switching Cointegration Approach," School of Economics Discussion Papers 0309, School of Economics, University of Surrey.
    39. Ahmad Zubaidi Baharumshah & Evan Lau, 2010. "Mean Reversion Of The Fiscal Conduct In 24 Developing Countries," Manchester School, University of Manchester, vol. 78(4), pages 302-325, July.
    40. Ata Ozkaya, 2013. "The Effects of Debt Intolerance and Public Debt Sustainability on Credit Ratings: Evidence From European Economies," Working Papers 011, Bahcesehir University, Betam.
    41. Amir Kia, 2005. "Sustainability of the Fiscal Process in Developing Countries- Egypt, Iran and Turkey: A Multicointegration Approach – revised version: Fiscal Sustainability in Emerging Countries: Evidence from Iran a," Carleton Economic Papers 05-08, Carleton University, Department of Economics, revised Nov 2008.
    42. António Afonso & João Tovar Jalles, 2012. "Revisiting fiscal sustainability: panel cointegration and structural breaks in OECD countries," Working Papers Department of Economics 2012/29, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    43. Dimitris Hatzinikolaou, 2016. "A "litmus test" of Deficit Sustainability: The Case of the Greek Budget Deficit," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 65-73, August.
    44. Samuel S Jibao & Niek Schoeman & Ruthira Naraidoo, 2010. "Fiscal Regime Changes and the Sustainability of Fiscal Imbalance in South Africa: A Smooth Transition Error-Correction Approach," Working Papers 201023, University of Pretoria, Department of Economics.
    45. Ricciuti, Roberto, 2007. "The quest for a fiscal rule: Italy, 1861-1998," POLIS Working Papers 86, Institute of Public Policy and Public Choice - POLIS.
    46. Jean BOISSINOT & Clotilde L’ANGEVIN & Brieuc MONFORT, 2010. "Assessing Sustainability of Fiscal Policy in France: an I(2) Analysis," EcoMod2004 330600027, EcoMod.
    47. Paleologou, Suzanna-Maria, 2013. "Asymmetries in the revenue–expenditure nexus: A tale of three countries," Economic Modelling, Elsevier, vol. 30(C), pages 52-60.
    48. K. R. Shanmugam & P.S. Renjith, 2023. "Sustainability and Threshold Value of Public Debt of Centre and All State Governments in India," Working Papers 2023-240, Madras School of Economics,Chennai,India.
    49. James Payne & Hassan Mohammadi, 2006. "Are Adjustments in the U.S. Budget Deficit Asymmetric? Another Look at Sustainability," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 34(1), pages 15-22, March.
    50. Javier Biscarri & Fernando Gracia, 2004. "Stock market cycles and stock market development in Spain," Spanish Economic Review, Springer;Spanish Economic Association, vol. 6(2), pages 127-151, July.
    51. Kausik Chaudhuri & Bodhisattva Sengupta, 2009. "Revenue-Expenditure Nexus for Southern States: Some Policy Oriented Econometric Observations," Working Papers 2009-048, Madras School of Economics,Chennai,India.
    52. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    53. María Florencia Aráoz & Ana María Cerro & Osvaldo Meloni & Tatiana Soria Genta, 2009. "Empirical Evidence on Fiscal Policy Sustainability in Argentina," The IUP Journal of Monetary Economics, IUP Publications, vol. 0(3-4), pages 116-127, August.
    54. Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Is the US fiscal deficit sustainable?: A fractionally integrated approach," Journal of Economics and Business, Elsevier, vol. 56(6), pages 501-526.
    55. Shruti SHASTRI & A.K. GIRI & Geetilaxmi MOHAPATRA, 2017. "An empirical assessment of fiscal sustainability for selected South Asian economies," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(1(610), S), pages 163-178, Spring.
    56. Josep Lluís Carrion-I-Silvestre, 2016. "Fiscal Deficit Sustainability of the Spanish Regions," Regional Studies, Taylor & Francis Journals, vol. 50(10), pages 1702-1713, October.
    57. Ricardo Ferraz & Joaquim Miranda Sarmento & António Portugal Duarte, 2024. "The Sustainability of Portuguese Fiscal Policy in Democracy, 1974–2020," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(3), pages 749-772, September.
    58. Bayan Mohamad Alshaib & Abdullah Mohammad Ghazi Al khatib & Alina Cristina Nuta & Mohamad Hamra & Pradeep Mishra & Rajani Gautam & Sarfraz Hussain & Cristina Gabriela Zamfir, 2023. "Fiscal Sustainability and Its Implications for Economic Growth in Egypt: An Empirical Analysis," SAGE Open, , vol. 13(4), pages 21582440231, December.
    59. Paap, R. & van Dijk, H.K., 2002. "Bayes estimates of Markov trends in possibly cointegrated series: an application to US consumption and income," Econometric Institute Research Papers EI 2002-42, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    60. Ata Ozkaya, 2013. "Public Debt Stock Sustainability in Selected OECD Countries," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 13(1), pages 31-49.
    61. Saeid Mahdavi, 2010. "Fiscal Stringency and Fiscal Sustainability in the American States: Panel Evidence," Working Papers 0016, College of Business, University of Texas at San Antonio.
    62. Manuchehr Irandoust, 2018. "Government spending and revenues in Sweden 1722–2011: evidence from hidden cointegration," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(3), pages 543-557, August.
    63. James W. Saunoris, 2015. "The Dynamics of the Revenue–Expenditure Nexus," Public Finance Review, , vol. 43(1), pages 108-134, January.
    64. António Afonso & João Tovar Jalles, 2016. "The elusive character of fiscal sustainability," Applied Economics, Taylor & Francis Journals, vol. 48(28), pages 2651-2664, June.
    65. Ahmad Zubaidi Baharumshah & Evan Lau, 2002. "On the Sustainability of Current Account Deficits: Evidence from Four ASEAN Countries," Working Papers 0062, National University of Ireland Galway, Department of Economics, revised 2002.
    66. Villani, Mattias, 2005. "Bayesian Inference of General Linear Restrictions on the Cointegration Space," Working Paper Series 189, Sveriges Riksbank (Central Bank of Sweden).
    67. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    68. Luis Gil-Alana, 2009. "Government Expenditures and Revenues: Evidence of Fractional Cointegration in an Asymmetric Modeling," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(2), pages 143-155, May.
    69. Chen, Shyh-Wei & Wu, An-Chi, 2018. "Is there a bubble component in government debt? New international evidence," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 467-486.
    70. C.S. Forbes & G.M. Martin & J. Wright, 2002. "Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices," Monash Econometrics and Business Statistics Working Papers 2/02, Monash University, Department of Econometrics and Business Statistics.
    71. Perron, Pierre & Qu, Zhongjun, 2006. "Estimating restricted structural change models," Journal of Econometrics, Elsevier, vol. 134(2), pages 373-399, October.
    72. Miyazaki, Tomomi, 2014. "Fiscal reform and fiscal sustainability: Evidence from Australia and Sweden," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 141-151.
    73. Chen, Shyh-Wei, 2014. "Testing for fiscal sustainability: New evidence from the G-7 and some European countries," Economic Modelling, Elsevier, vol. 37(C), pages 1-15.
    74. Evan Lau & Ahmad Zubaidi Baharumshah, 2005. "Assessing The Mean Reversion Behavior Of Fiscal Policy: The Case Of Asian Countries," Macroeconomics 0504002, University Library of Munich, Germany.
    75. Ching-Chuan Tsong & Cheng-Feng Lee & Li-Ju Tsai & Te-Chung Hu, 2016. "The Fourier approximation and testing for the null of cointegration," Empirical Economics, Springer, vol. 51(3), pages 1085-1113, November.
    76. Mahsa Fathalizadeh, 2016. "Assessing the Iranian Fiscal Sustainability in Past and Future through Tax Side of the Economy," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 20(2), pages 187-201, Spring.
    77. Luis F. Martins & Paulo M. M. Rodrigues, 2022. "Tests for segmented cointegration: an application to US governments budgets," Empirical Economics, Springer, vol. 63(2), pages 567-600, August.
    78. Roberto Ricciuti, 2003. "Assessing Ricardian Equivalence," Journal of Economic Surveys, Wiley Blackwell, vol. 17(1), pages 55-78, February.
    79. Gebhard Kirchgässner & Silika Prohl, 2008. "Sustainability of Swiss Fiscal Policy," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 144(I), pages 57-83, March.
    80. Mahdavi, Saeid & Westerlund, Joakim, 2011. "Fiscal stringency and fiscal sustainability: Panel evidence from the American state and local governments," Journal of Policy Modeling, Elsevier, vol. 33(6), pages 953-969.
    81. James Payne & Hassan Mohammadi & Murat Cak, 2008. "Turkish budget deficit sustainability and the revenue-expenditure nexus," Applied Economics, Taylor & Francis Journals, vol. 40(7), pages 823-830.
    82. Andrea Cipollini & Bassam Fattouh & Kostas Mouratidis, 2009. "Fiscal Readjustments In The United States: A Nonlinear Time‐Series Analysis," Economic Inquiry, Western Economic Association International, vol. 47(1), pages 34-54, January.
    83. Juan Carlos Cuestas & Luis A. Gil-Alana & Laura Sauci, 2020. "Public finances in the EU-27: Are they sustainable?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(1), pages 181-204, February.
    84. Roberto Ricciuti, 2004. "Nonlinearity in testing for fiscal sustainability," Money Macro and Finance (MMF) Research Group Conference 2003 80, Money Macro and Finance Research Group.
    85. Michele Salvi & Christoph A. Schaltegger, 2023. "Tax more or spend less? Historical evidence from Switzerland’s federal budget plans," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(3), pages 678-705, June.
    86. Chew Lian Chua & Sarantis Tsiaplias, 2014. "A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank," Melbourne Institute Working Paper Series wp2014n27, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    87. Ho-Chuan Huang & Wan-hsiu Cheng, 2005. "Tests of the CAPM under structural changes," International Economic Journal, Taylor & Francis Journals, vol. 19(4), pages 523-541.
    88. Escario, Regina & Gadea, María Dolores & Sabaté, Marcela, 2012. "Multicointegration, seigniorage and fiscal sustainability. Spain 1857–2000," Journal of Policy Modeling, Elsevier, vol. 34(2), pages 270-283.

Articles

  1. Martin, G., 2015. "A conceptual framework to support adaptation of farming systems – Development and application with Forage Rummy," Agricultural Systems, Elsevier, vol. 132(C), pages 52-61.

    Cited by:

    1. van Weeghel, H.J.E. & Bos, A.P. & Jansen, M.H. & Ursinus, W.W. & Groot Koerkamp, P.W.G., 2021. "Good animal welfare by design: An approach to incorporate animal capacities in engineering design," Agricultural Systems, Elsevier, vol. 191(C).
    2. Leen, Frederik & Van den Broeke, Alice & Aluwé, Marijke & Ludwig, Lauwers & Sam, Millet & Jef, Van Meensel, 2017. "Simulation Modelling To Provide Insights Into The Optimization Of Delivery Weights Of Finisher Pigs," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 261272, European Association of Agricultural Economists.
    3. Robert, Marion & Thomas, Alban & Bergez, Jacques Eric, 2016. "Processes of adpatation in farm decision-making models. A review," TSE Working Papers 16-731, Toulouse School of Economics (TSE).
    4. Ditzler, Lenora & Klerkx, Laurens & Chan-Dentoni, Jacqueline & Posthumus, Helena & Krupnik, Timothy J. & Ridaura, Santiago López & Andersson, Jens A. & Baudron, Frédéric & Groot, Jeroen C.J., 2018. "Affordances of agricultural systems analysis tools: A review and framework to enhance tool design and implementation," Agricultural Systems, Elsevier, vol. 164(C), pages 20-30.
    5. Guillaume Martin & Sandrine Allain & Jacques-Eric Bergez & Delphine Burger-Leenhardt & Julie Constantin & Michel Duru & Laurent Hazard & Camille Lacombe & Danièle Magda & Marie-Angélina Magne & Julie , 2018. "How to Address the Sustainability Transition of Farming Systems? A Conceptual Framework to Organize Research," Sustainability, MDPI, vol. 10(6), pages 1-20, June.
    6. Prost, Lorène & Reau, Raymond & Paravano, Laurette & Cerf, Marianne & Jeuffroy, Marie-Hélène, 2018. "Designing agricultural systems from invention to implementation: the contribution of agronomy. Lessons from a case study," Agricultural Systems, Elsevier, vol. 164(C), pages 122-132.
    7. Dolinska, Aleksandra & Hassenforder, Emeline & Loboguerrero, Ana Maria & Sultan, Benjamin & Bossuet, Jérôme & Cottenceau, Jeanne & Bonatti, Michelle & Hellin, Jon & Mekki, Insaf & Drogoul, Alexis & Va, 2023. "Co-production opportunities seized and missed in decision-support frameworks for climate-change adaptation in agriculture – How do we practice the “best practice”?," Agricultural Systems, Elsevier, vol. 212(C).
    8. Queyrel, Wilfried & Van Inghelandt, Bastien & Colas, Floriane & Cavan, Nicolas & Granger, Sylvie & Guyot, Bérénice & Reau, Raymond & Derrouch, Damien & Chauvel, Bruno & Maillot, Thibault & Colbach, Na, 2023. "Combining expert knowledge and models in participatory workshops with farmers to design sustainable weed management strategies," Agricultural Systems, Elsevier, vol. 208(C).
    9. Osinga, Sjoukje A. & Paudel, Dilli & Mouzakitis, Spiros A. & Athanasiadis, Ioannis N., 2022. "Big data in agriculture: Between opportunity and solution," Agricultural Systems, Elsevier, vol. 195(C).
    10. Colnago, P. & Rossing, W.A.H. & Dogliotti, S., 2021. "Closing sustainability gaps on family farms: Combining on-farm co-innovation and model-based explorations," Agricultural Systems, Elsevier, vol. 188(C).
    11. Boulestreau, Yann & Casagrande, Marion & Navarrete, Mireille, 2023. "A method to design coupled innovations for the agroecological transition. Implementation for soil health management in Provencal sheltered vegetable systems," Agricultural Systems, Elsevier, vol. 212(C).
    12. Robert-Jan Den Haan & Mascha C. Van der Voort, 2018. "On Evaluating Social Learning Outcomes of Serious Games to Collaboratively Address Sustainability Problems: A Literature Review," Sustainability, MDPI, vol. 10(12), pages 1-26, December.
    13. Dolinska, Aleksandra, 2017. "Bringing farmers into the game. Strengthening farmers' role in the innovation process through a simulation game, a case from Tunisia," Agricultural Systems, Elsevier, vol. 157(C), pages 129-139.

  2. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    See citations under working paper version above.
  3. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    See citations under working paper version above.
  4. Brendan P. M. McCabe & Gael M. Martin & David Harris, 2011. "Efficient probabilistic forecasts for counts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 253-272, March.

    Cited by:

    1. Tianqing Liu & Xiaohui Yuan, 2013. "Random rounded integer-valued autoregressive conditional heteroskedastic process," Statistical Papers, Springer, vol. 54(3), pages 645-683, August.
    2. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    3. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2019. "Evaluating Approximate Point Forecasting of Count Processes," Econometrics, MDPI, vol. 7(3), pages 1-28, July.
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Serge Darolles & Gaëlle Le Fol & Yang Lu & Ran Sun, 2019. "Bivariate integer-autoregressive process with an application to mutual fund flows," Post-Print hal-04582262, HAL.
    6. Bisaglia, Luisa & Canale, Antonio, 2016. "Bayesian nonparametric forecasting for INAR models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 70-78.
    7. David Harris & Gael M. Martin & Indeewara Perera & Don S. Poskitt, 2017. "Construction and visualization of optimal confidence sets for frequentist distributional forecasts," Monash Econometrics and Business Statistics Working Papers 9/17, Monash University, Department of Econometrics and Business Statistics.
    8. Wei Wei & Leonhard Held, 2014. "Calibration tests for count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 787-805, December.
    9. Dungey Mardi & Martin Vance L. & Tang Chrismin & Tremayne Andrew, 2020. "A threshold mixed count time series model: estimation and application," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-18, April.
    10. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    11. Yang Lu, 2021. "The predictive distributions of thinning‐based count processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 42-67, March.
    12. Giulia Carallo & Roberto Casarin & Christian P. Robert, 2020. "Generalized Poisson Difference Autoregressive Processes," Papers 2002.04470, arXiv.org.
    13. Yao Rao & David Harris & Brendan McCabe, 2022. "A semi‐parametric integer‐valued autoregressive model with covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 495-516, June.
    14. Frazier, David T. & Maneesoonthorn, Worapree & Martin, Gael M. & McCabe, Brendan P.M., 2019. "Approximate Bayesian forecasting," International Journal of Forecasting, Elsevier, vol. 35(2), pages 521-539.
    15. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    16. Germán Aneiros, 2012. "Comments on: Some recent theory for autoregressive count time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 439-441, September.
    17. Vance L. Martin & Andrew R. Tremayne & Robert C. Jung, 2014. "Efficient Method Of Moments Estimators For Integer Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(6), pages 491-516, November.
    18. Darolles, Serge & Fol, Gaëlle Le & Lu, Yang & Sun, Ran, 2019. "Bivariate integer-autoregressive process with an application to mutual fund flows," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 181-203.
    19. Luisa Bisaglia & Margherita Gerolimetto, 2019. "Model-based INAR bootstrap for forecasting INAR(p) models," Computational Statistics, Springer, vol. 34(4), pages 1815-1848, December.

  5. Lahiri, Kajal & Martin, Gael, 2010. "Bayesian forecasting in economics," International Journal of Forecasting, Elsevier, vol. 26(2), pages 211-215, April.

    Cited by:

    1. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.

  6. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    See citations under working paper version above.
  7. Strickland, Chris M. & Martin, Gael M. & Forbes, Catherine S., 2008. "Parameterisation and efficient MCMC estimation of non-Gaussian state space models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2911-2930, February.
    See citations under working paper version above.
  8. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.

    Cited by:

    1. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    2. Ralph D. Snyder & J. Keith Ord, 2009. "Exponential Smoothing and the Akaike Information Criterion," Monash Econometrics and Business Statistics Working Papers 4/09, Monash University, Department of Econometrics and Business Statistics.
    3. James W. Taylor, 2012. "Density Forecasting of Intraday Call Center Arrivals Using Models Based on Exponential Smoothing," Management Science, INFORMS, vol. 58(3), pages 534-549, March.

  9. Catherine S. Forbes & Gael M. Martin & Jill Wright, 2007. "Inference for a Class of Stochastic Volatility Models Using Option and Spot Prices: Application of a Bivariate Kalman Filter," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 387-418.

    Cited by:

    1. Maneesoonthorn, Worapree & Martin, Gael M. & Forbes, Catherine S. & Grose, Simone D., 2012. "Probabilistic forecasts of volatility and its risk premia," Journal of Econometrics, Elsevier, vol. 171(2), pages 217-236.
    2. A. S. Hurn & K. A. Lindsay & A. J. McClelland, 2015. "Estimating the Parameters of Stochastic Volatility Models Using Option Price Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 579-594, October.
    3. Gael M. Martin & Andrew Reidy & Jill Wright, 2009. "Does the option market produce superior forecasts of noise-corrected volatility measures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 77-104.
    4. Gael M. Martin & Brendan P.M. McCabe & Worapree Maneesoonthorn & Christian P. Robert, 2014. "Approximate Bayesian Computation in State Space Models," Monash Econometrics and Business Statistics Working Papers 20/14, Monash University, Department of Econometrics and Business Statistics.
    5. Worapree Maneesoonthorn & David T. Frazier & Gael M. Martin, 2024. "Probabilistic Predictions of Option Prices Using Multiple Sources of Data," Papers 2412.00658, arXiv.org.
    6. Richard Finlay & Mark Chambers, 2009. "A Term Structure Decomposition of the Australian Yield Curve," The Economic Record, The Economic Society of Australia, vol. 85(271), pages 383-400, December.
    7. Marcel Prokopczuk & Yingying Wu, 2013. "Estimating term structure models with the Kalman filter," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 4, pages 97-113, Edward Elgar Publishing.
    8. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    9. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    10. Abel Rodr�guez & Enrique ter Horst, 2011. "Measuring expectations in options markets: an application to the S&P500 index," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1393-1405, July.
    11. Shu Wing Ho & Alan Lee & Alastair Marsden, 2011. "Use of Bayesian Estimates to determine the Volatility Parameter Input in the Black-Scholes and Binomial Option Pricing Models," JRFM, MDPI, vol. 4(1), pages 1-23, December.
    12. Lorenzo Mercuri & Edit Rroji, 2018. "Option pricing in an exponential MixedTS Lévy process," Annals of Operations Research, Springer, vol. 260(1), pages 353-374, January.

  10. Andrew D. Sanford & Gael M. Martin, 2006. "Bayesian comparison of several continuous time models of the Australian short rate," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 46(2), pages 309-326, June.

    Cited by:

    1. Chew Lian Chua & Sandy Suardi & Sarantis Tsiaplias, 2011. "Predicting Short-Term Interest Rates: Does Bayesian Model Averaging Provide Forecast Improvement?," Melbourne Institute Working Paper Series wp2011n01, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    2. Shyh-Wei Chen & Chung-Hua Shen, 2007. "Evidence of the duration-dependence from the stock markets in the Pacific Rim economies," Applied Economics, Taylor & Francis Journals, vol. 39(11), pages 1461-1474.
    3. Chua, Chew Lian & Suardi, Sandy & Tsiaplias, Sarantis, 2013. "Predicting short-term interest rates using Bayesian model averaging: Evidence from weekly and high frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 442-455.
    4. Vijay A Murik, 2013. "Measuring monetary policy expectations," Australian Journal of Management, Australian School of Business, vol. 38(1), pages 49-65, April.
    5. Zhang, Yonghui & Chen, Zhongtian & Li, Yong, 2017. "Bayesian testing for short term interest rate models," Finance Research Letters, Elsevier, vol. 20(C), pages 146-152.
    6. Muteba Mwamba, John & Thabo, Lethaba & Uwilingiye, Josine, 2014. "Modelling the short-term interest rate with stochastic differential equation in continuous time: linear and nonlinear models," MPRA Paper 64386, University Library of Munich, Germany.
    7. Tunaru, Diana, 2017. "Gaussian estimation and forecasting of the U.K. yield curve with multi-factor continuous-time models," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 119-129.
    8. Vijay A. Murik, 2013. "Bond pricing with a surface of zero coupon yields," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(2), pages 497-512, June.

  11. Strickland, Chris M. & Forbes, Catherine S. & Martin, Gael M., 2006. "Bayesian analysis of the stochastic conditional duration model," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2247-2267, May.
    See citations under working paper version above.
  12. Lim, G.C. & Martin, G.M. & Martin, V.L., 2006. "Pricing currency options in the presence of time-varying volatility and non-normalities," Journal of Multinational Financial Management, Elsevier, vol. 16(3), pages 291-314, July.

    Cited by:

    1. Monica Billio & Bertrand Maillet & Loriana Pelizzon, 2022. "A meta-measure of performance related to both investors and investments characteristics," Annals of Operations Research, Springer, vol. 313(2), pages 1405-1447, June.
    2. Wang, Xiao-Tian & Li, Zhe & Zhuang, Le, 2017. "European option pricing under the Student’s t noise with jumps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 848-858.
    3. Cassidy, Daniel T. & Hamp, Michael J. & Ouyed, Rachid, 2010. "Pricing European options with a log Student’s t-distribution: A Gosset formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5736-5748.
    4. Xiao, Wei-Lin & Zhang, Wei-Guo & Zhang, Xi-Li & Wang, Ying-Luo, 2010. "Pricing currency options in a fractional Brownian motion with jumps," Economic Modelling, Elsevier, vol. 27(5), pages 935-942, September.
    5. Cassidy, Daniel T., 2011. "Describing n-day returns with Student’s t-distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(15), pages 2794-2802.
    6. Renée Fry-McKibbin & Vance Martin & Chrismin Tang, 2013. "Financial Contagion and Asset Pricing," CAMA Working Papers 2013-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Sun, Lin, 2013. "Pricing currency options in the mixed fractional Brownian motion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3441-3458.

  13. Sanford, Andrew D. & Martin, Gael M., 2005. "Simulation-based Bayesian estimation of an affine term structure model," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 527-554, April.
    See citations under working paper version above.
  14. McCabe, B.P.M. & Martin, G.M., 2005. "Bayesian predictions of low count time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 315-330.

    Cited by:

    1. Wooi Chen Khoo & Seng Huat Ong & Biswas Atanu, 2022. "Coherent Forecasting for a Mixed Integer-Valued Time Series Model," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    2. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    3. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    4. Feike C. Drost & Ramon van den Akker & Bas J. M. Werker, 2009. "Efficient estimation of auto‐regression parameters and innovation distributions for semiparametric integer‐valued AR(p) models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 467-485, April.
    5. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    6. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2019. "Evaluating Approximate Point Forecasting of Count Processes," Econometrics, MDPI, vol. 7(3), pages 1-28, July.
    7. T M Christensen & A S Hurn & K A Lindsay, 2008. "It never rains but it pours: Modelling the persistence of spikes in electricity prices," NCER Working Paper Series 25, National Centre for Econometric Research.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Serge Darolles & Gaëlle Le Fol & Yang Lu & Ran Sun, 2019. "Bivariate integer-autoregressive process with an application to mutual fund flows," Post-Print hal-04582262, HAL.
    10. Jung, Robert C. & Tremayne, A.R., 2006. "Coherent forecasting in integer time series models," International Journal of Forecasting, Elsevier, vol. 22(2), pages 223-238.
    11. Bisaglia, Luisa & Canale, Antonio, 2016. "Bayesian nonparametric forecasting for INAR models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 70-78.
    12. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    13. Aldo M. Garay & Francyelle L. Medina & Suelem Torres de Freitas & Víctor H. Lachos, 2024. "Bayesian analysis of linear regression models with autoregressive symmetrical errors and incomplete data," Statistical Papers, Springer, vol. 65(9), pages 5649-5690, December.
    14. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    15. Bu, Ruijun & McCabe, Brendan, 2008. "Model selection, estimation and forecasting in INAR(p) models: A likelihood-based Markov Chain approach," International Journal of Forecasting, Elsevier, vol. 24(1), pages 151-162.
    16. Jonas Andersson & Dimitris Karlis, 2010. "Treating missing values in INAR(1) models: An application to syndromic surveillance data," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(1), pages 12-19, January.
    17. Berry, Lindsay R. & Helman, Paul & West, Mike, 2020. "Probabilistic forecasting of heterogeneous consumer transaction–sales time series," International Journal of Forecasting, Elsevier, vol. 36(2), pages 552-569.
    18. Ralph D. Snyder & Gael M. Martin & Phillip Gould & Paul D. Feigin, 2007. "An Assessment of Alternative State Space Models for Count Time Series," Monash Econometrics and Business Statistics Working Papers 4/07, Monash University, Department of Econometrics and Business Statistics.
    19. T M Christensen & A. S. Hurn & K A Lindsay, 2008. "Discrete time-series models when counts are unobservable," NCER Working Paper Series 35, National Centre for Econometric Research.
    20. Bennedsen, Mikkel & Lunde, Asger & Shephard, Neil & Veraart, Almut E.D., 2023. "Inference and forecasting for continuous-time integer-valued trawl processes," Journal of Econometrics, Elsevier, vol. 236(2).
    21. Wei Wei & Leonhard Held, 2014. "Calibration tests for count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 787-805, December.
    22. Ovielt Baltodano Lopez & Federico Bassetti & Giulia Carallo & Roberto Casarin, 2022. "First-order integer-valued autoregressive processes with Generalized Katz innovations," Papers 2202.02029, arXiv.org, revised Dec 2024.
    23. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    24. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    25. Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2020. "On an integer-valued stochastic intensity model for time series of counts," MPRA Paper 105406, University Library of Munich, Germany.
    26. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    27. Yang Lu, 2021. "The predictive distributions of thinning‐based count processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 42-67, March.
    28. Giulia Carallo & Roberto Casarin & Christian P. Robert, 2020. "Generalized Poisson Difference Autoregressive Processes," Papers 2002.04470, arXiv.org.
    29. Yelland, Phillip M., 2009. "Bayesian forecasting for low-count time series using state-space models: An empirical evaluation for inventory management," International Journal of Production Economics, Elsevier, vol. 118(1), pages 95-103, March.
    30. Francesco Bravo, 2011. "Comment on: Subsampling weakly dependent time series and application to extremes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(3), pages 483-486, November.
    31. Raju Maiti & Atanu Biswas & Bibhas Chakraborty, 2018. "Modelling of low count heavy tailed time series data consisting large number of zeros and ones," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 407-435, August.
    32. Feigin, Paul D. & Gould, Phillip & Martin, Gael M. & Snyder, Ralph D., 2008. "Feasible parameter regions for alternative discrete state space models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2963-2970, December.
    33. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2021. "A performance analysis of prediction intervals for count time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 603-625, July.
    34. Víctor Enciso‐Mora & Peter Neal & T. Subba Rao, 2009. "Efficient order selection algorithms for integer‐valued ARMA processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 1-18, January.
    35. Mohammad Khajehzadeh & Farhad Pazhuheian & Farima Seifi & Rassoul Noorossana & Ali Asli & Niloufar Saeedi, 2022. "Analysis of Factors Affecting Product Sales with an Outlook toward Sale Forecasting in Cosmetic Industry using Statistical Methods," International Review of Management and Marketing, Econjournals, vol. 12(6), pages 55-63, November.
    36. Vance L. Martin & Andrew R. Tremayne & Robert C. Jung, 2014. "Efficient Method Of Moments Estimators For Integer Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(6), pages 491-516, November.
    37. Brajendra C. Sutradhar, 2008. "On forecasting counts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 109-129.
    38. Darolles, Serge & Fol, Gaëlle Le & Lu, Yang & Sun, Ran, 2019. "Bivariate integer-autoregressive process with an application to mutual fund flows," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 181-203.
    39. Andersson, Jonas & Karlis, Dimitris, 2008. "Treating missing values in INAR(1) models," Discussion Papers 2008/14, Norwegian School of Economics, Department of Business and Management Science.

  15. V. L. Martin & G. M. Martin & G. C. Lim, 2005. "Parametric pricing of higher order moments in S&P500 options," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 377-404.
    See citations under working paper version above.
  16. Gael M. Martin & Catherine S. Forbes & Vance L. Martin, 2005. "Implicit Bayesian Inference Using Option Prices," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(3), pages 437-462, May.
    See citations under working paper version above.
  17. Gael Martin, 2001. "Bayesian Analysis Of A Fractional Cointegration Model," Econometric Reviews, Taylor & Francis Journals, vol. 20(2), pages 217-234.

    Cited by:

    1. Koop, G. & Strachan, R.W. & van Dijk, H.K. & Villani, M., 2005. "Bayesian approaches to cointegratrion," Econometric Institute Research Papers EI 2005-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Hwa-Taek Lee & Gawon Yoon, 2013. "Does purchasing power parity hold sometimes? Regime switching in real exchange rates," Applied Economics, Taylor & Francis Journals, vol. 45(16), pages 2279-2294, June.
    3. Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Is the US fiscal deficit sustainable?: A fractionally integrated approach," Journal of Economics and Business, Elsevier, vol. 56(6), pages 501-526.
    4. C.S. Forbes & G.M. Martin & J. Wright, 2002. "Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices," Monash Econometrics and Business Statistics Working Papers 2/02, Monash University, Department of Econometrics and Business Statistics.
    5. Kleibergen, Frank, 2004. "Invariant Bayesian inference in regression models that is robust against the Jeffreys-Lindley's paradox," Journal of Econometrics, Elsevier, vol. 123(2), pages 227-258, December.
    6. Luis A. Gil-Alana, 2004. "Fractional cointegration in the consumption and income relationship using semiparametric techniques," Economics Bulletin, AccessEcon, vol. 3(47), pages 1-8.

  18. Gael M. Martin, 2000. "US deficit sustainability: a new approach based on multiple endogenous breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 83-105.
    See citations under working paper version above.
  19. G. M. Martin & C. S. Forbes, 1999. "Using simulation methods for bayesian econometric models: inference, development and communication: some comments," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 113-118.

    Cited by:

    1. Liu, Chun, 2010. "Marginal likelihood calculation for gelfand-dey and Chib Method," MPRA Paper 34928, University Library of Munich, Germany.
    2. Yasuo Hirose, 2008. "Equilibrium Indeterminacy and Asset Price Fluctuation in Japan: A Bayesian Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(5), pages 967-999, August.
    3. Hibiki Ichiue & Takushi Kurozumi & Takeki Sunakawa, 2008. "Inflation Dynamics and Labor Adjustments in Japan: A Bayesian DSGE Approach," Bank of Japan Working Paper Series 08-E-9, Bank of Japan.
    4. Xibin Zhang & Maxwell L. King, 2011. "Bayesian semiparametric GARCH models," Monash Econometrics and Business Statistics Working Papers 24/11, Monash University, Department of Econometrics and Business Statistics.
    5. Dewachter, Hans & Iania, Leonardo & Lyrio, Marco, 2011. "A New-Keynesian Model of the Yield Curve with Learning Dynamics: A Bayesian Evaluation," Insper Working Papers wpe_250, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    6. Hirose, Yasuo, 2010. "Monetary policy and sunspot fluctuation in the U.S. and the Euro area," MPRA Paper 33693, University Library of Munich, Germany.
    7. Rangan Gupta & Rudi Steinbach, 2010. "Forecasting Key Macroeconomic Variables of the South African Economy: A Small Open Economy New Keynesian DSGE-VAR Model," Working Papers 201019, University of Pretoria, Department of Economics.
    8. Warne, Anders, 2006. "Bayesian inference in cointegrated VAR models: with applications to the demand for euro area M3," Working Paper Series 692, European Central Bank.
    9. Xibin Zhang & Maxwell L. King & Han Lin Shang, 2011. "Bayesian estimation of bandwidths for a nonparametric regression model with a flexible error density," Monash Econometrics and Business Statistics Working Papers 10/11, Monash University, Department of Econometrics and Business Statistics.
    10. Viktors Ajevskis & Kristine Vitola, 2011. "Fixed Exchange Rate Versus Inflation Targeting: Evidence from DSGE Modelling," Working Papers 2011/02, Latvijas Banka.
    11. Riggi, Marianna & Tancioni, Massimiliano, 2010. "Nominal vs real wage rigidities in New Keynesian models with hiring costs: A Bayesian evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 34(7), pages 1305-1324, July.

  20. Lim, G. C. & Lye, J. N. & Martin, G. M. & Martin*, V. L., 1998. "The distribution of exchange rate returns and the pricing of currency options," Journal of International Economics, Elsevier, vol. 45(2), pages 351-368, August.

    Cited by:

    1. Pierdzioch, Christian, 2000. "Noise Traders? Trigger Rates, FX Options, and Smiles," Kiel Working Papers 970, Kiel Institute for the World Economy (IfW Kiel).
    2. Martin, G.M. & Forbes, C.S. & Martin, V.L., 2000. "Implicit Bayesian Inference Using Option Prices," Monash Econometrics and Business Statistics Working Papers 5/00, Monash University, Department of Econometrics and Business Statistics.
    3. G.C. Lim & G.M. Martin & V.L. Martin, 2002. "Parametric Pricing of Higher Order Moments in S&P500 Options," Monash Econometrics and Business Statistics Working Papers 1/02, Monash University, Department of Econometrics and Business Statistics.
    4. Lim, G.C. & Martin, G.M. & Martin, V.L., 2006. "Pricing currency options in the presence of time-varying volatility and non-normalities," Journal of Multinational Financial Management, Elsevier, vol. 16(3), pages 291-314, July.
    5. Ribeiro de Castro, Claudia, 1999. "Inside and Outside the Band Exchange Rate Fluctuations for Brazil," LIDAM Discussion Papers IRES 2000004, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    6. Xiao, Wei-Lin & Zhang, Wei-Guo & Zhang, Xi-Li & Wang, Ying-Luo, 2010. "Pricing currency options in a fractional Brownian motion with jumps," Economic Modelling, Elsevier, vol. 27(5), pages 935-942, September.
    7. Renée Fry-McKibbin & Vance Martin & Chrismin Tang, 2013. "Financial Contagion and Asset Pricing," CAMA Working Papers 2013-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

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