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Emily J. Whitehouse

Personal Details

First Name:Emily
Middle Name:J.
Last Name:Whitehouse
Suffix:
RePEc Short-ID:pwh58
[This author has chosen not to make the email address public]
https://emilywhitehouse.co.uk
Terminal Degree:2017 School of Economics; University of Nottingham (from RePEc Genealogy)

Affiliation

Department of Economics
University of Sheffield

Sheffield, United Kingdom
http://www.shef.ac.uk/economics/
RePEc:edi:desheuk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Whitehouse, E. J. & Harvey, D. I. & Leybourne, S. J., 2022. "Real-time monitoring of bubbles and crashes," Working Papers 2022007, The University of Sheffield, Department of Economics.
  2. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.
  3. David I. Harvey & Stephen J. Leybourne & Emily J. Whitehouse, 2017. "Testing for a unit root against ESTAR stationarity," Discussion Papers 17/02, University of Nottingham, Granger Centre for Time Series Econometrics.
  4. David I. Harvey & Stephen J. Leybourne & Emily J. Whitehouse, 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," Discussion Papers 17/03, University of Nottingham, Granger Centre for Time Series Econometrics.

Articles

  1. Emily J. Whitehouse & David I. Harvey & Stephen J. Leybourne, 2023. "Real‐Time Monitoring of Bubbles and Crashes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 482-513, June.
  2. Harvey, David I. & Leybourne, Stephen J. & Whitehouse, Emily J., 2020. "Date-stamping multiple bubble regimes," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 226-246.
  3. Emily J. Whitehouse, 2019. "Explosive Asset Price Bubble Detection with Unknown Bubble Length and Initial Condition," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(1), pages 20-41, February.
  4. Harvey David I. & Leybourne Stephen J. & Whitehouse Emily J., 2018. "Testing for a unit root against ESTAR stationarity," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(1), pages 1-29, February.
  5. Harvey, David I. & Leybourne, Stephen J. & Whitehouse, Emily J., 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," International Journal of Forecasting, Elsevier, vol. 33(4), pages 833-847.

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. Whitehouse, E. J. & Harvey, D. I. & Leybourne, S. J., 2022. "Real-time monitoring of bubbles and crashes," Working Papers 2022007, The University of Sheffield, Department of Economics.

    Cited by:

    1. Hansen, Jacob H. & Møller, Stig V. & Pedersen, Thomas Q. & Schütte, Christian M., 2024. "House price bubbles under the COVID-19 pandemic," Journal of Empirical Finance, Elsevier, vol. 75(C).
    2. Lajos Horv'ath & Lorenzo Trapani, 2023. "Real-time monitoring with RCA models," Papers 2312.11710, arXiv.org.

  2. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.

    Cited by:

    1. Skrobotov, Anton, 2021. "Structural breaks in cointegration models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 117-141.

  3. David I. Harvey & Stephen J. Leybourne & Emily J. Whitehouse, 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," Discussion Papers 17/03, University of Nottingham, Granger Centre for Time Series Econometrics.

    Cited by:

    1. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    2. Laura Coroneo & Fabrizio Iacone, 2020. "Comparing predictive accuracy in small samples using fixed‐smoothing asymptotics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 391-409, June.
    3. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    4. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    5. Arabinda Basistha & Richard Startz, 2024. "Measuring persistent global economic factors with output, commodity price, and commodity currency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2860-2885, November.
    6. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    7. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    8. Zhou, Jin & Li, Haiqi & Zhong, Wanling, 2021. "A modified Diebold–Mariano test for equal forecast accuracy with clustered dependence," Economics Letters, Elsevier, vol. 207(C).
    9. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    10. Yang, Kailing & Zhang, Xi & Luo, Haojia & Hou, Xianping & Lin, Yu & Wu, Jingyu & Yu, Liang, 2024. "Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting," Energy, Elsevier, vol. 298(C).
    11. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    12. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
    13. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    14. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    15. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    16. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    17. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
    18. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.

Articles

  1. Emily J. Whitehouse & David I. Harvey & Stephen J. Leybourne, 2023. "Real‐Time Monitoring of Bubbles and Crashes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 482-513, June.
    See citations under working paper version above.
  2. Harvey, David I. & Leybourne, Stephen J. & Whitehouse, Emily J., 2020. "Date-stamping multiple bubble regimes," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 226-246.

    Cited by:

    1. Yang, Bingduo & Long, Wei & Yang, Zihui, 2022. "Testing predictability of stock returns under possible bubbles," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 246-260.
    2. Lajos Horváth & Hemei Li & Zhenya Liu, 2021. "How to identify the different phases of stock market bubbles statistically?," Post-Print hal-03511435, HAL.
    3. Skrobotov Anton, 2023. "Testing for explosive bubbles: a review," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-26, January.
    4. Hansen, Jacob H. & Møller, Stig V. & Pedersen, Thomas Q. & Schütte, Christian M., 2024. "House price bubbles under the COVID-19 pandemic," Journal of Empirical Finance, Elsevier, vol. 75(C).
    5. Eiji Kurozumi & Anton Skrobotov, 2021. "On the asymptotic behavior of bubble date estimators," Papers 2110.04500, arXiv.org, revised Sep 2022.

  3. Emily J. Whitehouse, 2019. "Explosive Asset Price Bubble Detection with Unknown Bubble Length and Initial Condition," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(1), pages 20-41, February.

    Cited by:

    1. Yang Hu, 2023. "A review of Phillips‐type right‐tailed unit root bubble detection tests," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 141-158, February.
    2. Emily J. Whitehouse & David I. Harvey & Stephen J. Leybourne, 2023. "Real‐Time Monitoring of Bubbles and Crashes," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 482-513, June.
    3. Skrobotov Anton, 2023. "Testing for explosive bubbles: a review," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-26, January.

  4. Harvey, David I. & Leybourne, Stephen J. & Whitehouse, Emily J., 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," International Journal of Forecasting, Elsevier, vol. 33(4), pages 833-847.
    See citations under working paper version above.

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 4 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 (4) 2017-07-23 2017-07-23 2020-04-13 2022-05-09. Author is listed
  2. NEP-ETS: Econometric Time Series (4) 2017-07-23 2017-07-23 2020-04-13 2022-05-09. Author is listed
  3. NEP-FOR: Forecasting (1) 2017-07-23. Author is listed
  4. NEP-ORE: Operations Research (1) 2022-05-09. Author is listed
  5. NEP-URE: Urban and Real Estate Economics (1) 2022-05-09. Author is listed

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