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William Mccausland

Personal Details

First Name:William
Middle Name:
Last Name:McCausland
Suffix:
RePEc Short-ID:pmc8
http://www.mac.com/mccauslw
Département de sciences économiques Université de Montréal C.P. 6128, succursale Centre-ville Montréal, Québec H3C 3J7 Canada
(514) 343-7281
Terminal Degree:2001 Department of Economics; University of Minnesota (from RePEc Genealogy)

Affiliation

(75%) Département de Sciences Économiques
Université de Montréal

Montréal, Canada
http://www.sceco.umontreal.ca/
RePEc:edi:demtlca (more details at EDIRC)

(20%) Centre Interuniversitaire de Recherche en Économie Quantitative (CIREQ)

Montréal, Canada
https://cireqmontreal.com/
RePEc:edi:cdmtlca (more details at EDIRC)

(5%) Centre Interuniversitaire de Recherche en Analyse des Organisations (CIRANO)

Montréal, Canada
http://www.cirano.qc.ca/
RePEc:edi:ciranca (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Samuel Gingras & William J. McCausland, 2020. "A Flexible Stochastic Conditional Duration Model," Papers 2005.09166, arXiv.org.
  2. McCAUSLAND, William & MARLEY, A. A. J., 2013. "Bayesian inference and model comparison for ramdom choice structures," Cahiers de recherche 2013-06, Universite de Montreal, Departement de sciences economiques.
  3. McCAUSLAND, William, 2008. "The Hessian Method (Highly Efficient State Smoothing, In a Nutshell)," Cahiers de recherche 2008-03, Universite de Montreal, Departement de sciences economiques.
  4. William J. McCausland & Shirley Miller & Denis Pelletier, 2007. "A New Approach to Drawing States in State Space Models," Working Paper Series 014, North Carolina State University, Department of Economics, revised Aug 2007.
  5. McCAUSLAND, William, 2004. "Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods," Cahiers de recherche 2004-05, Universite de Montreal, Departement de sciences economiques.
  6. McCAUSLAND, William, 2004. "A Theory of Random Consumer Demand," Cahiers de recherche 2004-04, Universite de Montreal, Departement de sciences economiques.
  7. McCAUSLAND, William, 2004. "Time Reversibility of Stationary Regular Finite State Markov Chains," Cahiers de recherche 2004-07, Universite de Montreal, Departement de sciences economiques.
  8. ENGLE-WARNICK, Jim & McCAUSLAND, William J. & MILLER, John H., 2004. "The Ghost in the Machine: Inferring Machine-Based Strategies from Observed Behavior," Cahiers de recherche 2004-11, Universite de Montreal, Departement de sciences economiques.
  9. William McCausland, 1999. "Using the BACC Software for Bayesian Inference," Computing in Economics and Finance 1999 833, Society for Computational Economics.

Articles

  1. McCausland, William & Miller, Shirley & Pelletier, Denis, 2021. "Multivariate stochastic volatility using the HESSIAN method," Econometrics and Statistics, Elsevier, vol. 17(C), pages 76-94.
  2. William J McCausland & Clintin Davis-Stober & AAJ Marley & Sanghyuk Park & Nicholas Brown, 2020. "Testing the Random Utility Hypothesis Directly," The Economic Journal, Royal Economic Society, vol. 130(625), pages 183-207.
  3. Barnabé Djegnéné & William J. McCausland, 2015. "The HESSIAN Method for Models with Leverage-like Effects," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 722-755.
  4. McCausland, William J., 2012. "The HESSIAN method: Highly efficient simulation smoothing, in a nutshell," Journal of Econometrics, Elsevier, vol. 168(2), pages 189-206.
  5. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.
  6. McCausland, William, 2010. "Economic modeling and inference, by Bent Jesper Christensen and Nicholas M. Kiefer," International Review of Economics & Finance, Elsevier, vol. 19(4), pages 793-794, October.
  7. WILLIAM J. McCAUSLAND, 2009. "Random Consumer Demand," Economica, London School of Economics and Political Science, vol. 76(301), pages 89-107, February.
  8. McCausland, William J., 2008. "On Bayesian analysis and computation for functions with monotonicity and curvature restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 484-507, January.
  9. McCausland, William J., 2007. "Time reversibility of stationary regular finite-state Markov chains," Journal of Econometrics, Elsevier, vol. 136(1), pages 303-318, January.
  10. William J. McCausland, 2004. "Using the BACC Software for Bayesian Inference," Computational Economics, Springer;Society for Computational Economics, vol. 23(3), pages 201-218, April.
  11. John Geweke & William McCausland, 2001. "Bayesian Specification Analysis in Econometrics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(5), pages 1181-1186.

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.

Working papers

  1. McCAUSLAND, William, 2008. "The Hessian Method (Highly Efficient State Smoothing, In a Nutshell)," Cahiers de recherche 2008-03, Universite de Montreal, Departement de sciences economiques.

    Cited by:

    1. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. 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.
    4. Kleppe, Tore Selland & Skaug, Hans Julius, 2012. "Fitting general stochastic volatility models using Laplace accelerated sequential importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3105-3119.

  2. William J. McCausland & Shirley Miller & Denis Pelletier, 2007. "A New Approach to Drawing States in State Space Models," Working Paper Series 014, North Carolina State University, Department of Economics, revised Aug 2007.

    Cited by:

    1. McCAUSLAND, William, 2008. "The Hessian Method (Highly Efficient State Smoothing, In a Nutshell)," Cahiers de recherche 2008-03, Universite de Montreal, Departement de sciences economiques.

  3. McCAUSLAND, William, 2004. "Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods," Cahiers de recherche 2004-05, Universite de Montreal, Departement de sciences economiques.

    Cited by:

    1. McCAUSLAND, William, 2004. "A Theory of Random Consumer Demand," Cahiers de recherche 2004-04, Universite de Montreal, Departement de sciences economiques.
    2. WILLIAM J. McCAUSLAND, 2009. "Random Consumer Demand," Economica, London School of Economics and Political Science, vol. 76(301), pages 89-107, February.

  4. McCAUSLAND, William, 2004. "A Theory of Random Consumer Demand," Cahiers de recherche 2004-04, Universite de Montreal, Departement de sciences economiques.

    Cited by:

    1. McCAUSLAND, William, 2004. "A Theory of Random Consumer Demand," Cahiers de recherche 2004-04, Universite de Montreal, Departement de sciences economiques.
    2. WILLIAM J. McCAUSLAND, 2009. "Random Consumer Demand," Economica, London School of Economics and Political Science, vol. 76(301), pages 89-107, February.
    3. McCAUSLAND, William J., 2004. "Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods," Cahiers de recherche 10-2004, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    4. Daniel McFadden, 2005. "Revealed stochastic preference: a synthesis," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 26(2), pages 245-264, August.
    5. McCausland, William J., 2008. "On Bayesian analysis and computation for functions with monotonicity and curvature restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 484-507, January.

  5. McCAUSLAND, William, 2004. "Time Reversibility of Stationary Regular Finite State Markov Chains," Cahiers de recherche 2004-07, Universite de Montreal, Departement de sciences economiques.

    Cited by:

    1. MacDonald, Iain L. & Pienaar, Etienne A.D., 2021. "Fitting a reversible Markov chain by maximum likelihood: Converting an awkwardly constrained optimization problem to an unconstrained one," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    2. Beare, Brendan K., 2009. "Copulas and Temporal Dependence," University of California at San Diego, Economics Working Paper Series qt87p829d4, Department of Economics, UC San Diego.
    3. J. Besag & D. Mondal, 2013. "Exact Goodness-of-Fit Tests for Markov Chains," Biometrics, The International Biometric Society, vol. 69(2), pages 488-496, June.
    4. Yong Chen & Jianmin Chen, 2011. "On the Imbedding Problem for Three-State Time Homogeneous Markov Chains with Coinciding Negative Eigenvalues," Journal of Theoretical Probability, Springer, vol. 24(4), pages 928-938, December.
    5. McCAUSLAND, William J., 2004. "Time Reversibility of Stationary Regular Finite State Markov Chains," Cahiers de recherche 09-2004, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    6. Davide Di Cecco, 2012. "Conditional exact tests for Markovianity and reversibility in multiple categorical sequences," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 170-187, March.
    7. Beare, Brendan K. & Seo, Juwon, 2014. "Time Irreversible Copula-Based Markov Models," Econometric Theory, Cambridge University Press, vol. 30(5), pages 923-960, October.

  6. ENGLE-WARNICK, Jim & McCAUSLAND, William J. & MILLER, John H., 2004. "The Ghost in the Machine: Inferring Machine-Based Strategies from Observed Behavior," Cahiers de recherche 2004-11, Universite de Montreal, Departement de sciences economiques.

    Cited by:

    1. Camera, Gabriele & Casari, Marco & Bigoni, Maria, 2012. "Cooperative strategies in anonymous economies: An experiment," Games and Economic Behavior, Elsevier, vol. 75(2), pages 570-586.
    2. Gabriele Camera & Marco Casari & Maria Bigoni, 2010. "Cooperative Strategies in Groups of Strangers: An Experiment," Purdue University Economics Working Papers 1237, Purdue University, Department of Economics.
    3. Dal Bó, Pedro & Fréchette, Guillaume R., 2013. "Strategy choice in the infinitely repeated prisoners' dilemma," Discussion Papers, Research Unit: Economics of Change SP II 2013-311, WZB Berlin Social Science Center.
    4. Jones, Matthew T., 2014. "Strategic complexity and cooperation: An experimental study," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 352-366.
    5. Pedro Dal Bo & Guillaume R. Frochette, 2011. "The Evolution of Cooperation in Infinitely Repeated Games: Experimental Evidence," American Economic Review, American Economic Association, vol. 101(1), pages 411-429, February.
    6. Asen Ivanov & Douglas D. Davis & Korenok Oleg, 2011. "A Simple Approach for Organizing Behavior and Explaining Cooperation in Repeated Games," Working Papers 1101, VCU School of Business, Department of Economics.
    7. Burkov, Andriy & Chaib-draa, Brahim, 2015. "Computing equilibria in discounted dynamic games," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 863-884.
    8. Douglas Davis & Asen Ivanov & Oleg Korenok, 2014. "Aspects of Behavior in Repeated Games: An Experimental Study," Working Papers 727, Queen Mary University of London, School of Economics and Finance.

  7. William McCausland, 1999. "Using the BACC Software for Bayesian Inference," Computing in Economics and Finance 1999 833, Society for Computational Economics.

    Cited by:

    1. Takashi Kano & James M. Nason, 2009. "Business Cycle Implications of Internal Consumption Habit for New Keynesian Model," CARF F-Series CARF-F-151, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. McCAUSLAND, William J., 2004. "Time Reversibility of Stationary Regular Finite State Markov Chains," Cahiers de recherche 09-2004, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Bryant, Henry L. & Davis, George C., 2001. "Beyond The Model Specification Problem: Model And Parameter Averaging Using Bayesian Techniques," 2001 Annual meeting, August 5-8, Chicago, IL 20689, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Kano, Takashi & 加納, 隆 & Nason, James M., 2012. "Appendix: Business Cycle Implications of Internal Consumption Habit for New Keynesian Models," Discussion Papers 2012-08, Graduate School of Economics, Hitotsubashi University.

Articles

  1. William J McCausland & Clintin Davis-Stober & AAJ Marley & Sanghyuk Park & Nicholas Brown, 2020. "Testing the Random Utility Hypothesis Directly," The Economic Journal, Royal Economic Society, vol. 130(625), pages 183-207.

    Cited by:

    1. Gronau, Quentin F. & Bennett, Murray S. & Brown, Scott D. & Hawkins, Guy E. & Eidels, Ami, 2023. "Do choice tasks and rating scales elicit the same judgments?," Journal of choice modelling, Elsevier, vol. 49(C).
    2. Victor H. Aguiar & Maria Jose Boccardi & Nail Kashaev & Jeongbin Kim, 2018. "Random Utility and Limited Consideration," Papers 1812.09619, arXiv.org, revised Jul 2022.
    3. Alós-Ferrer, Carlos & Garagnani, Michele, 2021. "Choice consistency and strength of preference," Economics Letters, Elsevier, vol. 198(C).
    4. Daniele Caliari & Henrik Petri, 2024. "Irrational Random Utility Models," Papers 2403.10208, arXiv.org.
    5. Caliari, Daniele, 2023. "Behavioural welfare analysis and revealed preference: Theory and experimental evidence," Discussion Papers, Research Unit: Economics of Change SP II 2023-303, WZB Berlin Social Science Center.

  2. Barnabé Djegnéné & William J. McCausland, 2015. "The HESSIAN Method for Models with Leverage-like Effects," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 722-755.

    Cited by:

    1. Antonio A. F. Santos, 2021. "Bayesian Estimation for High-Frequency Volatility Models in a Time Deformed Framework," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 455-479, February.
    2. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    4. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
    5. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.

  3. McCausland, William J., 2012. "The HESSIAN method: Highly efficient simulation smoothing, in a nutshell," Journal of Econometrics, Elsevier, vol. 168(2), pages 189-206.

    Cited by:

    1. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Joshua C.C. Chan & Angelia L. Grant, 2015. "Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean," CAMA Working Papers 2015-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. István Barra & Lennart Hoogerheide & Siem Jan Koopman & André Lucas, 2017. "Joint Bayesian Analysis of Parameters and States in Nonlinear non‐Gaussian State Space Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 1003-1026, August.
    4. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    6. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    7. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    8. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.
    9. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    10. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Hauber, Philipp, 2021. "How useful is external information from professional forecasters? Conditional forecasts in large factor models," EconStor Preprints 251469, ZBW - Leibniz Information Centre for Economics.
    12. Joshua Chan & Eric Eisenstat & Xuewen Yu, 2022. "Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis," Papers 2207.03988, arXiv.org.
    13. Hedibert F. Lopes & Nicholas G. Polson, 2016. "Particle Learning for Fat-Tailed Distributions," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1666-1691, December.
    14. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
    15. Joshua C.C. Chan & Angelia L. Grant, 2014. "Fast Computation of the Deviance Information Criterion for Latent Variable Models," CAMA Working Papers 2014-09, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

  4. McCausland, William J. & Miller, Shirley & Pelletier, Denis, 2011. "Simulation smoothing for state-space models: A computational efficiency analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 199-212, January.

    Cited by:

    1. Alessia Naccarato & Andrea Pierini & Giovanna Ferraro, 2021. "Markowitz portfolio optimization through pairs trading cointegrated strategy in long-term investment," Annals of Operations Research, Springer, vol. 299(1), pages 81-99, April.
    2. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Joshua C.C. Chan & Rodney Strachan, 2014. "The Zero Lower Bound: Implications for Modelling the Interest Rate," Working Paper series 42_14, Rimini Centre for Economic Analysis.
    4. Angelia L. Grant & Joshua C.C. Chan, 2017. "A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(2-3), pages 525-552, March.
    5. Chan, Joshua C.C. & Santi, Caterina, 2021. "Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    6. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
    7. Andreas Dibiasi & Samad Sarferaz, 2020. "Measuring Macroeconomic Uncertainty: The Labor Channel of Uncertainty from a Cross-Country Perspective," Papers 2006.09007, arXiv.org, revised Dec 2020.
    8. Niko Hauzenberger & Florian Huber & Gary Koop, 2020. "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods," Papers 2005.03906, arXiv.org, revised May 2023.
    9. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    10. Liu, Wei-han, 2016. "A re-examination of maturity effect of energy futures price from the perspective of stochastic volatility," Energy Economics, Elsevier, vol. 56(C), pages 351-362.
    11. Pettenuzzo, Davide & Sabbatucci, Riccardo & Timmermann, Allan, 2023. "Dividend suspensions and cash flows during the Covid-19 pandemic: A dynamic econometric model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1522-1541.
    12. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    13. Luis Uzeda, 2018. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Staff Working Papers 18-14, Bank of Canada.
    14. Terence D. Agbeyegbe, 2023. "The Link Between Output Growth and Output Growth Volatility: Barbados," Annals of Data Science, Springer, vol. 10(3), pages 787-804, June.
    15. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    16. Eric Eisenstat & Rodney W. Strachan, 2014. "Modelling Inflation Volatility," CAMA Working Papers 2014-68, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Martin Iseringhausen, 2018. "The Time-Varying Asymmetry Of Exchange Rate Returns: A Stochastic Volatility – Stochastic Skewness Model," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 18/944, Ghent University, Faculty of Economics and Business Administration.
    18. Danilo Leiva-Leon & Luis Uzeda, 2020. "Endogenous Time Variation in Vector Autoregressions," Staff Working Papers 20-16, Bank of Canada.
    19. Yunjong Eo & Luis Uzeda & Benjamin Wong, 2022. "Understanding trend inflation through the lens of the goods and services sectors," CAMA Working Papers 2022-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    20. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    21. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    22. Bo Zhang & Joshua C.C. Chan & Jamie L. Cross, 2018. "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," CAMA Working Papers 2018-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    23. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    24. Gregor Kastner, 2019. "Dealing with Stochastic Volatility in Time Series Using the R Package stochvol," Papers 1906.12134, arXiv.org.
    25. Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics 2012-591, Australian National University, College of Business and Economics, School of Economics.
    26. 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.
    27. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    28. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The volatility effect on precious metals price returns in a stochastic volatility in mean model with time-varying parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    29. Carlos A. Abanto-Valle & Hernán B. Garrafa-Aragón, 2019. "Threshold Stochastic Volatility Models with Heavy Tails:A Bayesian Approach," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 42(83), pages 32-53.
    30. Grant, Angelia L., 2018. "The Great Recession and Okun's law," Economic Modelling, Elsevier, vol. 69(C), pages 291-300.
    31. Samad Sarferaz & Andreas Dibiasi, 2020. "Measuring Macroeconomic Uncertainty: A Cross-Country Analysis," KOF Working papers 20-479, KOF Swiss Economic Institute, ETH Zurich.
    32. Li, Junye, 2013. "An unscented Kalman smoother for volatility extraction: Evidence from stock prices and options," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 15-26.
    33. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    34. Agbeyegbe, Terence D., 2020. "Bayesian analysis of output gap in Barbados," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    35. Makoto Nakakita & Teruo Nakatsuma, 2021. "Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors," JRFM, MDPI, vol. 14(4), pages 1-29, March.
    36. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2022. "An Alternative Estimation Method for Time-Varying Parameter Models," Econometrics, MDPI, vol. 10(2), pages 1-27, April.
    37. Gregor Kastner & Sylvia Fruhwirth-Schnatter, 2017. "Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models," Papers 1706.05280, arXiv.org.
    38. 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.
    39. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient matrix approach for classical inference in state space models," Economics Letters, Elsevier, vol. 181(C), pages 22-27.
    40. Iseringhausen, Martin, 2024. "A time-varying skewness model for Growth-at-Risk," International Journal of Forecasting, Elsevier, vol. 40(1), pages 229-246.
    41. Jan van den Brakel & Martijn Souren & Sabine Krieg, 2022. "Estimating monthly labour force figures during the COVID‐19 pandemic in the Netherlands," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1560-1583, October.
    42. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2019. "Non-Gaussian VARMA model with stochastic volatility and applications in stock market bubbles," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 129-136.
    43. Mao, Xiuping & Czellar, Veronika & Ruiz, Esther & Veiga, Helena, 2020. "Asymmetric stochastic volatility models: Properties and particle filter-based simulated maximum likelihood estimation," Econometrics and Statistics, Elsevier, vol. 13(C), pages 84-105.
    44. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    45. Hong, Bingyuan & Li, Xiaoping & Li, Yu & Chen, Shilin & Tan, Yao & Fan, Di & Song, Shangfei & Zhu, Baikang & Gong, Jing, 2022. "An improved hydraulic model of gathering pipeline network integrating pressure-exchange ejector," Energy, Elsevier, vol. 260(C).
    46. David E. Allen & Michael McAleer, 2020. "Do We Need Stochastic Volatility and Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns on FTSE," Risks, MDPI, vol. 8(1), pages 1-20, February.
    47. Hauber, Philipp, 2021. "How useful is external information from professional forecasters? Conditional forecasts in large factor models," EconStor Preprints 251469, ZBW - Leibniz Information Centre for Economics.
    48. Peter Knaus & Sylvia Fruhwirth-Schnatter, 2023. "The Dynamic Triple Gamma Prior as a Shrinkage Process Prior for Time-Varying Parameter Models," Papers 2312.10487, arXiv.org.
    49. Joshua C. C. Chan, 2017. "The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
    50. David Edmund Allen, 2020. "Stochastic Volatility and GARCH: Do Squared End-of-Day Returns Provide Similar Information?," JRFM, MDPI, vol. 13(9), pages 1-25, September.
    51. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    52. Peter Knaus & Angela Bitto-Nemling & Annalisa Cadonna & Sylvia Fruhwirth-Schnatter, 2019. "Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP," Papers 1907.07065, arXiv.org, revised Nov 2020.
    53. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.
    54. Joshua C.C. Chan & Angelia L. Grant, 2014. "Fast Computation of the Deviance Information Criterion for Latent Variable Models," CAMA Working Papers 2014-09, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    55. McCausland, William J., 2012. "The HESSIAN method: Highly efficient simulation smoothing, in a nutshell," Journal of Econometrics, Elsevier, vol. 168(2), pages 189-206.
    56. Darjus Hosszejni & Gregor Kastner, 2019. "Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage," Papers 1901.11491, arXiv.org, revised Nov 2019.
    57. Cross, Jamie L. & Hou, Chenghan & Trinh, Kelly, 2021. "Returns, volatility and the cryptocurrency bubble of 2017–18," Economic Modelling, Elsevier, vol. 104(C).
    58. Darjus Hosszejni & Gregor Kastner, 2019. "Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol," Papers 1906.12123, arXiv.org, revised Feb 2021.
    59. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics 2013-604, Australian National University, College of Business and Economics, School of Economics.
    60. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    61. Osman Doğan & Süleyman Taşpınar & Anil K. Bera, 2021. "Bayesian estimation of stochastic tail index from high-frequency financial data," Empirical Economics, Springer, vol. 61(5), pages 2685-2711, November.

  5. WILLIAM J. McCAUSLAND, 2009. "Random Consumer Demand," Economica, London School of Economics and Political Science, vol. 76(301), pages 89-107, February.

    Cited by:

    1. Indraneel Dasgupta, 2011. "Contraction consistent stochastic choice correspondence," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 37(4), pages 643-658, October.
    2. Indraneel Dasgupta, 2007. "Revealed Preference with Stochastic Demand Correspondence," Discussion Papers 07/06, University of Nottingham, School of Economics.
    3. Simone Cerreia-Vioglio & Per Olov Lindberg & Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2020. "A Canon of Probabilistic Rationality," Papers 2007.11386, arXiv.org, revised May 2021.
    4. Per Hjertstrand & James Swofford, 2014. "Are the choices of people stochastically rational? A stochastic test of the number of revealed preference violations," Empirical Economics, Springer, vol. 46(4), pages 1495-1519, June.
    5. Javier A. Birchenall, 2024. "Random choice and market demand," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(1), pages 165-198, February.

  6. McCausland, William J., 2008. "On Bayesian analysis and computation for functions with monotonicity and curvature restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 484-507, January.

    Cited by:

    1. Oum, Tae H. & Yan, Jia & Yu, Chunyan, 2008. "Ownership forms matter for airport efficiency: A stochastic frontier investigation of worldwide airports," Journal of Urban Economics, Elsevier, vol. 64(2), pages 422-435, September.
    2. Griffiths, William E. & Newton, Lisa S. & O'Donnell, Christopher J., 2010. "Predictive densities for models with stochastic regressors and inequality constraints: Forecasting local-area wheat yield," International Journal of Forecasting, Elsevier, vol. 26(2), pages 397-412, April.
    3. Caroline Khan & Mike G. Tsionas, 2021. "Constraints in models of production and cost via slack-based measures," Empirical Economics, Springer, vol. 61(6), pages 3347-3374, December.

  7. McCausland, William J., 2007. "Time reversibility of stationary regular finite-state Markov chains," Journal of Econometrics, Elsevier, vol. 136(1), pages 303-318, January.
    See citations under working paper version above.
  8. William J. McCausland, 2004. "Using the BACC Software for Bayesian Inference," Computational Economics, Springer;Society for Computational Economics, vol. 23(3), pages 201-218, April.
    See citations under working paper version above.
  9. John Geweke & William McCausland, 2001. "Bayesian Specification Analysis in Econometrics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(5), pages 1181-1186.

    Cited by:

    1. Patrizia Ordine & Claudio Lupi, 2009. "Family Income and Students' Mobility," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 68(1), pages 1-23, April.
    2. Cainelli, Giulio & Lupi, Claudio, 2008. "Does Spatial Proximity Matter? Micro-evidence from Italy," Economics & Statistics Discussion Papers esdp08042, University of Molise, Department of Economics.
    3. von Haefen, Roger H. & Phaneuf, Daniel J., 2003. "Estimating preferences for outdoor recreation:: a comparison of continuous and count data demand system frameworks," Journal of Environmental Economics and Management, Elsevier, vol. 45(3), pages 612-630, May.

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 14 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (10) 1999-07-12 2004-06-09 2004-08-30 2004-08-30 2007-08-27 2008-02-16 2008-08-21 2013-08-31 2014-03-30 2020-06-15. Author is listed
  2. NEP-DCM: Discrete Choice Models (6) 2004-06-02 2004-08-23 2004-08-23 2004-08-31 2013-08-31 2014-03-30. Author is listed
  3. NEP-ETS: Econometric Time Series (5) 2004-06-02 2004-08-23 2007-08-27 2008-02-16 2008-08-21. Author is listed
  4. NEP-MIC: Microeconomics (3) 2004-06-02 2004-08-23 2004-08-31
  5. NEP-ORE: Operations Research (2) 2013-08-31 2014-03-30
  6. NEP-UPT: Utility Models and Prospect Theory (2) 2013-08-31 2014-03-30
  7. NEP-EVO: Evolutionary Economics (1) 2004-09-05
  8. NEP-EXP: Experimental Economics (1) 2004-09-05

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