IDEAS home Printed from https://ideas.repec.org/a/phs/prejrn/v59y2022i1p77-107.html
   My bibliography  Save this article

The BSP's Forecasting and Policy Analysis System

Author

Listed:
  • Zeno Ronald R. Abenoja

    (Bangko Sentral ng Pilipinas)

  • Jasmin E. Dacio

    (Bangko Sentral ng Pilipinas)

  • Sarah Jane A. Castañares

    (Bangko Sentral ng Pilipinas)

  • Jan Christopher G. Ocampo

    (Bangko Sentral ng Pilipinas)

  • Mark Rex S. Romaraog

    (Bangko Sentral ng Pilipinas)

Abstract

The Bangko Sentral ng Pilipinas (BSP) subscribes to the Forecasting and Policy Analysis System (FPAS) as the framework for macroeconomic forecasting and analysis in support of monetary policy formulation. FPAS is a standard framework, adopted by many in"ation-targeting central banks, that organizes the generation, consolidation, and analysis of economic information relavant to monetary policy formulation. This paper aims to describe how macroeconomic forecasts and policy simulations are generated to support monetary policy analysis and formulation at the BSP. To this end, this article summarizes the main features of the process involved in generating the baseline forecasts, alternative scenarios, and policy simulations. We highlight the complementary roles played by the BSP’s suite of models and the expert judgement from the sector specialists as well as the importance of forecast communication in the transmission of monetary policy. Finally, we present a systematic evaluation of the forecasting performance of the BSP from 2010 to 2020 together with some of the lessons in forecasting during the COVID-19 pandemic and the recent efforts to improve the BSP’s FPAS.

Suggested Citation

  • Zeno Ronald R. Abenoja & Jasmin E. Dacio & Sarah Jane A. Castañares & Jan Christopher G. Ocampo & Mark Rex S. Romaraog, 2022. "The BSP's Forecasting and Policy Analysis System," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 59(1), pages 77-107, June.
    • Handle: RePEc:phs:prejrn:v:59:y:2022:i:1:p:77-107
    as

    Download full text from publisher

    File URL: https://econ.upd.edu.ph/pre/index.php/pre/article/view/1024/933
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    2. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1.
    3. Mr. Philippe D Karam & Mikhail Pranovich & Mr. Jan Vlcek, 2021. "An Extended Quarterly Projection Model: Credit Cycle, Macrofinancial Linkages and Macroprudential Measures: The Case of the Philippines," IMF Working Papers 2021/256, International Monetary Fund.
    4. Mr. Douglas Laxton & Mr. Alasdair Scott & David Rose, 2009. "Developing a Structured Forecasting and Policy Analysis System to Support Inflation-Forecast Targeting (IFT)," IMF Working Papers 2009/065, International Monetary Fund.
    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. Bank for International Settlements, 2023. "Inflation and labour markets," BIS Papers, Bank for International Settlements, number 142.

    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. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    2. Pascal Bührig & Klaus Wohlrabe, 2015. "Revisionen der deutschen Industrieproduktion und die ifo Indikatoren," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(21), pages 27-31, November.
    3. Baris Soybilgen & Ege Yazgan, 2017. "An evaluation of inflation expectations in Turkey," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 17(1), pages 1-31–38.
    4. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    5. Pericoli, Marcello & Taboga, Marco, 2012. "Bond risk premia, macroeconomic fundamentals and the exchange rate," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 42-65.
    6. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    7. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    8. Isiklar, Gultekin, 2005. "On aggregation bias in fixed-event forecast efficiency tests," Economics Letters, Elsevier, vol. 89(3), pages 312-316, December.
    9. Christopher Hansman & Harrison Hong & Áureo de Paula & Vishal Singh, 2020. "A Sticky-Price View of Hoarding," NBER Working Papers 27051, National Bureau of Economic Research, Inc.
    10. Barbara Rossi & Tatevik Sekhposyan, 2016. "Forecast Rationality Tests in the Presence of Instabilities, with Applications to Federal Reserve and Survey Forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 507-532, April.
    11. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    12. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    13. Patrick Rizzetto, 2018. "GDP by Industry in Real Time: Are Revisions Well Behaved?," Staff Analytical Notes 2018-40, Bank of Canada.
    14. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    15. Isengildina Massa, Olga & Stewart, Shamar & Hassman, Colburn H., 2021. "RETURN DIVERGENCE IN COMMODITY ETFs: NATURE AND CAUSES," 2021 Annual Meeting, August 1-3, Austin, Texas 313896, Agricultural and Applied Economics Association.
    16. repec:zbw:bofrdp:037 is not listed on IDEAS
    17. Panagiotis Lazaris & Anastasios Petropoulos & Vasileios Siakoulis & Evangelos Stavroulakis & Nikolaos Vlachogiannakis, 2021. "Interest rate pass through in the deposit and loan products provided by Greek banks," Working Papers 287, Bank of Greece.
    18. Thomas Lustenberger & Enzo Rossi, 2022. "The Social Value of Information: A Test of a Beauty and Nonbeauty Contest," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(7), pages 2125-2148, October.
    19. Angelini, Giovanni & De Angelis, Luca & Singleton, Carl, 2022. "Informational efficiency and behaviour within in-play prediction markets," International Journal of Forecasting, Elsevier, vol. 38(1), pages 282-299.
    20. Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2020. "Economic implications of forecasting electricity generation from variable renewable energy sources," Renewable Energy, Elsevier, vol. 161(C), pages 1318-1327.
    21. Johannes Bleher & Michael Bleher & Thomas Dimpfl, 2020. "From orders to prices: A stochastic description of the limit order book to forecast intraday returns," Papers 2004.11953, arXiv.org, revised May 2021.

    More about this item

    Keywords

    monetary policy; macroeconomic modelling; forecasting; inflation targeting;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

    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:phs:prejrn:v:59:y:2022:i:1:p:77-107. 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: RT Campos (email available below). General contact details of provider: https://edirc.repec.org/data/seupdph.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.