IDEAS home Printed from https://ideas.repec.org/p/eoe/escoed/escoe-dp-2023-20.html

Nowcasting Unpaid Production Activity and Quality Adjustments in Public Service

Author

Listed:
  • Papailias, Fotis
  • Kapetanios, George

Abstract

Economists are so focused on key macroeconomic variables, such as the GDP, Inflation, Unemployment, Industrial Production, Retail Sales, etc., who often omit or skip variables which are more qualitative in nature but of key importance for social conditions and living. These variables include the Unpaid Household Production Activity as well as Quality Adjustment for Public Service Productivity. This research attempts to provide a generic methodological framework for a dynamic nowcasting of such variables and assist relevant economists and statisticians in tracking of these low frequency qualitative variables.

Suggested Citation

  • Papailias, Fotis & Kapetanios, George, 2023. "Nowcasting Unpaid Production Activity and Quality Adjustments in Public Service," Discussion Papers escoe-dp-2023-20, Economic Statistics Centre of Excellence.
    • Handle: RePEc:eoe:escoed:escoe-dp-2023-20
    as

    Download full text from publisher

    File URL: https://www.repository-escoe.com/id/eprint/32/1/ESCoE%20DP%202023-20.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Babecký, Jan & Havránek, Tomáš & Matějů, Jakub & Rusnák, Marek & Šmídková, Kateřina & Vašíček, Bořek, 2013. "Leading indicators of crisis incidence: Evidence from developed countries," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 1-19.
    2. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    3. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tommaso Proietti, 2008. "Estimation of Common Factors Under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and Its Main Components," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 547-558, Springer.
    2. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    3. Jie Wei & Luiz Renato Lima & Lucas Lúcio Godeiro, 2026. "Factor‐Based Quantile Forecasting With Textual Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(4), pages 394-407, June.
    4. James Sampi, 2016. "High Dimensional Factor Models: An Empirical Bayes Approach," Working Papers 75, Peruvian Economic Association.
    5. Necati Tekatli, 2007. "Generalized Factor Models: A Bayesian Approach," UFAE and IAE Working Papers 730.08, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    6. Isaiah Hull & Or Sattath & Eleni Diamanti & Göran Wendin, 2024. "Quantum Technology for Economists," Contributions to Economics, Springer, number 978-3-031-50780-9, January.
    7. repec:dau:papers:123456789/11663 is not listed on IDEAS
    8. Diego Fresoli & Pilar Poncela & Esther Ruiz, 2024. "Dealing with idiosyncratic cross-correlation when constructing confidence regions for PC factors," Papers 2407.06883, arXiv.org.
    9. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    10. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
    11. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
    12. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," EIEF Working Papers Series 1106, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2011.
    13. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    14. Luciani, Matteo, 2014. "Forecasting with approximate dynamic factor models: The role of non-pervasive shocks," International Journal of Forecasting, Elsevier, vol. 30(1), pages 20-29.
    15. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
    16. Choi, In, 2012. "Efficient Estimation Of Factor Models," Econometric Theory, Cambridge University Press, vol. 28(2), pages 274-308, April.
    17. Shintani, Mototsugu & Guo, Zi-Yi, 2011. "Finite Sample Performance of Principal Components Estimators for Dynamic Factor Models: Asymptotic vs. Bootstrap Approximations," EconStor Preprints 167627, ZBW - Leibniz Information Centre for Economics.
    18. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
    19. Angelini, Elena & Marcellino, Massimiliano, 2011. "Econometric analyses with backdated data: Unified Germany and the euro area," Economic Modelling, Elsevier, vol. 28(3), pages 1405-1414, May.
    20. Fresoli, Diego & Poncela, Pilar & Ruiz, Esther, 2023. "Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models," Economics Letters, Elsevier, vol. 230(C).
    21. repec:dau:papers:123456789/11692 is not listed on IDEAS
    22. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eoe:escoed:escoe-dp-2023-20. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Communications Manager (email available below). General contact details of provider: https://edirc.repec.org/data/escoeuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.