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Thomas James van Florenstein Mulder

Personal Details

First Name:Thomas
Middle Name:James
Last Name:van Florenstein Mulder
Suffix:
RePEc Short-ID:pva914
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Affiliation

Reserve Bank of New Zealand

Wellington, New Zealand
http://www.rbnz.govt.nz/
RePEc:edi:rbngvnz (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Christopher Ball & Adam Richardson & Thomas van Florenstein Mulder, 2020. "Using job transitions data as a labour market indicator," Reserve Bank of New Zealand Analytical Notes series AN2020/02, Reserve Bank of New Zealand.
  2. Michael Callaghan & Thomas van Florenstein Mulder, 2020. "GDP Plus: An Economic Activity Indicator for New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2020/01, Reserve Bank of New Zealand.
  3. Punnoose Jacob & Thomas van Florenstein Mulder, 2019. "The flattening of the Phillips curve: Rounding up the suspects," Reserve Bank of New Zealand Analytical Notes series AN2019/06, Reserve Bank of New Zealand.
  4. Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Forecasting with a Global VAR model," Reserve Bank of New Zealand Analytical Notes series AN2019/03, Reserve Bank of New Zealand.
  5. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2018. "Nowcasting New Zealand GDP using machine learning algorithms," CAMA Working Papers 2018-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

Articles

  1. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.

Chapters

  1. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.

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. Punnoose Jacob & Thomas van Florenstein Mulder, 2019. "The flattening of the Phillips curve: Rounding up the suspects," Reserve Bank of New Zealand Analytical Notes series AN2019/06, Reserve Bank of New Zealand.

    Cited by:

    1. Harsha Paranavithana & Leandro Magnusson & Rod Tyers, 2021. "Monetary Policy Regimes in Small Open Economies: The Case of Sri Lanka," Asian Economic Journal, East Asian Economic Association, vol. 35(4), pages 434-462, December.
    2. Aquino, Juan, 2019. "The Small Open Economy New-Keynesian Phillips Curve: Specification, Structural Breaks and Robustness," Working Papers 2019-019, Banco Central de Reserva del Perú.
    3. Carlos Medel, 2021. "Forecasting Brazilian Inflation with the Hybrid New Keynesian Phillips Curve: Assessing the Predictive Role of Trading Partners," Working Papers Central Bank of Chile 900, Central Bank of Chile.

  2. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2018. "Nowcasting New Zealand GDP using machine learning algorithms," CAMA Working Papers 2018-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
    2. Christopher Ball & Adam Richardson & Thomas van Florenstein Mulder, 2020. "Using job transitions data as a labour market indicator," Reserve Bank of New Zealand Analytical Notes series AN2020/02, Reserve Bank of New Zealand.
    3. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    4. Isaac K. Ofori, 2021. "Catching the Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning," Working Papers of the African Governance and Development Institute. 21/044, African Governance and Development Institute..
    5. Tamara, Novian & Dwi Muchisha, Nadya & Andriansyah, Andriansyah & Soleh, Agus M, 2020. "Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms," MPRA Paper 105235, University Library of Munich, Germany.
    6. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    7. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    8. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    9. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    10. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    11. Harris Ntantanis & Lawrence Pohlman, 2020. "Market implied GDP," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 636-646, December.
    12. Brett Rayner & Marijn A. Bolhuis, 2020. "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers 2020/044, International Monetary Fund.
    13. Kristian Jönsson, 2020. "Machine Learning and Nowcasts of Swedish GDP," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 123-134, November.
    14. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    15. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    16. Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2022. "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from The Lasso Regularization and Inferential Techniques," Working Papers 22/061, European Xtramile Centre of African Studies (EXCAS).
    17. Rossouw, Stephanie & Greyling, Talita, 2020. "Big Data and Happiness," GLO Discussion Paper Series 634, Global Labor Organization (GLO).
    18. Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021. "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers 446, Institute of Economic Growth.
    19. Brett Rayner & Marijn A. Bolhuis, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
    20. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    21. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    22. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

Articles

  1. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    See citations under working paper version above.Sorry, no citations of articles recorded.

Chapters

  1. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

More information

Research fields, statistics, top rankings, if available.

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 2 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-FOR: Forecasting (2) 2018-10-08 2019-04-15. Author is listed
  2. NEP-BIG: Big Data (1) 2018-10-08. Author is listed
  3. NEP-CMP: Computational Economics (1) 2018-10-08. Author is listed

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