低频数据的经济预测框架-亚开行

Irfan A. Qureshi, Arief Ramayandi, and Ghufran Ahmad ADB ECONOMICSWORKING PAPER SERIES ADB Economics Working Paper Series An Economic Framework to Nowcast Low-Frequency Data Irfan A. Qureshi (iqureshi@adb.org) is a senior publicsector specialist at the Sectors Department 3,Asian Development Bank. Arief Ramayandi(aramayandi@adbi.org) is a senior research economistat the Asian Development Bank Institute.Ghufran Ahmad (ahmadg@cardiff.ac.uk) is a lecturerat Cardiff Business School, Cardiff University. Irfan A. Qureshi, Arief Ramayandi,and Ghufran Ahmad No. 800 | September 2025 TheADB Economics Working Paper Seriespresents research in progress to elicit commentsand encourage debate on development issuesin Asia and the Pacific. The views expressedare those of the authors and do not necessarilyreflect the views and policies of ADB orits Board of Governors or the governmentsthey represent. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2025 Asian Development Bank6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, PhilippinesTel +63 2 8632 4444; Fax +63 2 8636 2444www.adb.org Some rights reserved. Published in 2025. ISSN 2313-6537 (print), 2313-6545 (PDF)Publication Stock No. WPS250358-2DOI: http://dx.doi.org/10.22617/WPS250358-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policiesof the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for anyconsequence of their use. The mention of specific companies or products of manufacturers does not imply that theyare endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area in this document, ADB does notintend to make any judgments as to the legal or other status of any territory or area. This publication is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO)https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be boundby the terms of this license. For attribution, translations, adaptations, and permissions, please read the provisionsand terms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributedto another source, please contact the copyright owner or publisher of that source for permission to reproduce it.ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact pubsmarketing@adb.org if you have questions or comments with respect to content, or if you wishto obtain copyright permission for your intended use that does not fall within these terms, or for permission to usethe ADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. ABSTRACT Standard nowcasting frameworks commonly use weekly or monthly variables to monitor quarterlygross domestic product (GDP). However, this method is not suitable for economies that trackGDP annually. We modify the state-space representation of an otherwise standard dynamic factormodel to represent annual variables as a linear combination of latent monthly indicators for morefrequently released variables. Using data from a lower middle-income country, we derive amonthly activity measure that effectively tracks annual GDP growth. These estimates outperforminstitutional forecasts and competing approaches to estimate low-frequency data. The modeloffers broader applications to countries facing data limitations, especially lower-income countries. Keywords:monitoring real activity, Kalman filter, dynamic factor model, annual nowcasting JEL codes:C38, C53, E37, O11, O47 I. INTRODUCTION Standard nowcasting frameworks typically utilize high-frequency variables to monitor lower-frequency variables—for example weekly or monthly variables to track quarterly indicators. Onelimitation of the standard frameworks is that these cannot be used directly for monitoring lower-frequency variables, such as biannual or annual indicators. This is of particular concern in manydeveloping and emerging market economies, where numerous shortcomings often plague grossdomestic product (GDP) data, which are typically tracked annually. As Figure 1 illustrates,measurement of quarterly GDP has received relatively wider attention (around 40% of countries)only in the past decade. While most high-income countries have been measuring quarterly GDPsince 2005, most lower and upper middle-income countries still do not track it, and quarterly GDPis not available for any low-income countries. The low-frequency measurement of GDP makes itchallenging to accurately assess the state of the economy in real time, especially during theperiods