IDEAS home Printed from https://ideas.repec.org/a/rpo/ripoec/y2013i1p271-326.html
   My bibliography  Save this article

Previsioni delle spese del bilancio dello Stato attraverso i flussi di contabilità finanziaria

Author

Listed:
  • Giuseppe Bianchi

    (Ministero dell’Economia e delle Finanze - Ragioneria Generale dello Stato, Roma)

  • Tatiana Cesaroni

    (Ministero dell’Economia e delle Finanze - Ragioneria Generale dello Stato - Servizio Studi Dipartimentale)

  • Ottavio Ricchi

    (Ministero dell’Economia e delle Finanze - Ragioneria Generale dello Stato - Servizio Studi Dipartimentale)

Abstract

In this paper, we model and forecast monthly budget balance expenditures. The annual dimension of budget figures is integrated with an approach based on higher frequency data. To this end we develop an econometric model (disaggregated at expenditures category level) based on behavioral equations and budget balances identities linking data at different frequencies. The aggregate expenditure forecasts are obtained as a linear combination of the macroaggregates forecasts. This approach determines an improvement of forecast performance with respect to pure benchmark autoregressive models. The econometric model also represents a useful instrument to support the government forecasts based on more traditional tools.

Suggested Citation

  • Giuseppe Bianchi & Tatiana Cesaroni & Ottavio Ricchi, 2013. "Previsioni delle spese del bilancio dello Stato attraverso i flussi di contabilità finanziaria," Rivista di Politica Economica, SIPI Spa, issue 1, pages 271-326, January-M.
  • Handle: RePEc:rpo:ripoec:y:2013:i:1:p:271-326
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Onorante, Luca & Pedregal, Diego J. & Pérez, Javier J. & Signorini, Sara, 2010. "The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area," Journal of Policy Modeling, Elsevier, vol. 32(1), pages 98-119, January.
    2. Pedregal, Diego J. & Pérez, Javier J., 2010. "Should quarterly government finance statistics be used for fiscal surveillance in Europe?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 794-807, October.
    3. Teresa Leal & Diego J. Pedregal & Javier J. Pérez, 2009. "Short-term monitoring of the Spanish Government balance with mixed-frequencies models," Working Papers 0931, Banco de España.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giuseppe Bianchi & Tatiana Cesaroni & Ottavio Ricchi, 2015. "ISBEM: An econometric model for the Italian State Budget Expenditures," Working Papers LuissLab 15120, Dipartimento di Economia e Finanza, LUISS Guido Carli.

    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. Robert Ambrisko, 2022. "Nowcasting Macroeconomic Variables Using High-Frequency Fiscal Data," Working Papers 2022/5, Czech National Bank.
    2. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    3. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    4. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    5. Andrew Hughes Hallett & Moritz Kuhn & Thomas Warmedinger, 2012. "The gains from early intervention in Europe: Fiscal surveillance and fiscal planning using cash data," European Journal of Government and Economics, Europa Grande, vol. 1(1), pages 44-65, June.
    6. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    7. Diego J. Pedregal & Javier J. Pérez & Antonio Sánchez Fuentes, 2014. "A Tookit to strengthen Government," Hacienda Pública Española / Review of Public Economics, IEF, vol. 211(4), pages 117-146, December.
    8. Teresa Leal Linares & Javier J. Pérez, 2009. "Un sistema ARIMA con agregación temporal para la previsión y el seguimiento del déficit del Estado," Hacienda Pública Española / Review of Public Economics, IEF, vol. 190(3), pages 27-58, June.
    9. António Afonso & Ricardo Sousa, 2011. "The macroeconomic effects of fiscal policy in Portugal: a Bayesian SVAR analysis," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 10(1), pages 61-82, April.
    10. Onorante, Luca & Pedregal, Diego J. & Pérez, Javier J. & Signorini, Sara, 2010. "The usefulness of infra-annual government cash budgetary data for fiscal forecasting in the euro area," Journal of Policy Modeling, Elsevier, vol. 32(1), pages 98-119, January.
    11. Joan Paredes & Javier J. Pérez & Gabriel Perez Quiros, 2023. "Fiscal targets. A guide to forecasters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 472-492, June.
    12. Jacopo Cimadomo & Antonello D'Agostino, 2016. "Combining Time Variation and Mixed Frequencies: an Analysis of Government Spending Multipliers in Italy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1276-1290, November.
    13. Camba-Méndez, Gonzalo & Serwa, Dobromił, 2016. "Market perception of sovereign credit risk in the euro area during the financial crisis," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 168-189.
    14. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    15. Cimadomo, Jacopo & Claeys, Peter & Poplawski-Ribeiro, Marcos, 2016. "How do experts forecast sovereign spreads?," European Economic Review, Elsevier, vol. 87(C), pages 216-235.
    16. Carabotta, Laura & Paluzie, Elisenda & Ramos, Raul, 2017. "Does fiscal responsibility matter? Evidence from public and private forecasters in Italy," International Journal of Forecasting, Elsevier, vol. 33(3), pages 694-706.
    17. Diego J. Pedregal & Javier J. Pérez & A. Jesús Sánchez-Fuentes, 2014. "A toolkit to strengthen government budget surveillance," Working Papers 1416, Banco de España.
    18. Francisco de Castro & Francisco Martí & Antonio Montesinos & Javier J. Pérez & Antonio Jesús Sánchez Fuentes, 2018. "A Quarterly Fiscal Database Fit for Macroeconomic Analysis," Hacienda Pública Española / Review of Public Economics, IEF, vol. 224(1), pages 139-155, March.
    19. Alberto Urtasun & Mara Gil & Javier J. Perez, 2017. "Nowcasting private consumption: traditional indicators, uncertainty measures, and the role of internet search query data," EcoMod2017 10745, EcoMod.
    20. Afonso, António & Agnello, Luca & Furceri, Davide & Sousa, Ricardo M., 2011. "Assessing long-term fiscal developments: A new approach," Journal of International Money and Finance, Elsevier, vol. 30(1), pages 130-146, February.

    More about this item

    Keywords

    state budget expenditures; infra annual financial indicators; econometric models; forecast performance;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • H50 - Public Economics - - National Government Expenditures and Related Policies - - - General

    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:rpo:ripoec:y:2013:i:1:p:271-326. 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: Sabrina Marino (email available below). General contact details of provider: .

    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.