[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-501755-105":59,"doc-detail-501755-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","constructing-quarterly-chinese-time-series-usable-for-macroeconomic-analysis","CONSTRUCTING QUARTERLY CHINESE TIME SERIES USABLE FOR MACROECONOMIC ANALYSIS","","China experienced pronounced fluctuations in GDP growth and major expenditure components during episodes such as the global financial crisis and the Covid-19 pandemic. To identify the underlying sources, the paper builds a comprehensive quarterly dataset for GDP and its components in both nominal and real terms. Using two SVAR models, it estimates the principal drivers across episodes. Results highlight how consumption-constrained shocks affect household consumption and its subcomponents during the Covid-19 period.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/constructing-quarterly-chinese-time-series-usable-for-macroeconomic-analysis/501755/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/constructing-quarterly-chinese-time-series-usable-for-macroeconomic-analysis/501755.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What dataset does the paper construct, and at what frequency?","Question",{"text":112,"@type":113},"The paper constructs a comprehensive quarterly dataset of expenditure-based GDP (GDP-exp) and its principal components in both nominal and real terms, designed for macroeconomic fluctuation analysis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What method does the paper use to identify drivers of economic fluctuations?",{"text":117,"@type":113},"It applies two SVAR models to the constructed quarterly data to uncover the principal drivers of China’s economic fluctuations across different episodes.",{"name":119,"@type":110,"acceptedAnswer":120},"What does the paper find about consumption-constrained shocks during Covid-19?",{"text":121,"@type":113},"The findings emphasize the distinct impacts of consumption-constrained shocks on household consumption and its various subcomponents throughout the Covid-19 pandemic.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},501755,1791360409,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068","NBER WORKING PAPER SERIES  \nCONSTRUCTING QUARTERLY CHINESE TIME SERIES USABLE FOR MACROECONOMIC ANALYSIS  \nKaiji Chen  \nPatrick C. Higgins  \nTao Zha  \nWorking Paper 32087  \n[http://www.nber.org/papers/w32087](http://www.nber.org/papers/w32087)  \nNATIONAL BUREAU OF ECONOMIC RESEARCH  \n1050 Massachusetts Avenue  \nCambridge, MA 02138  \nJanuary 2024  \nWe are grateful to Hongyi Fu for her superlative research assistance. We thank John Fernald, John Rogers, and the participants of the 2023 Asian Economic Policy Conference at the Federal Reserve Bank of San Francisco, as well as the seminar attendees at the IMF, for their helpful discussions and comments. The views expressed herein are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of Atlanta, the Federal Reserve System, orthe National Bureau of Economic Research.  \nNBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.  \n© 2024 by Kaiji Chen, Patrick C. Higgins, and Tao Zha. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.  \nConstructing Quarterly Chinese Time Series Usable for Macroeconomic Analysis Kaiji Chen, Patrick C. Higgins, and Tao Zha  \nNBER Working Paper No. 32087 January 2024  \nJEL No. C82,E02  \nABSTRACT  \nDuring episodes such as the global financial crisis and the Covid-19 pandemic, China experienced notable fluctuations in its GDP growth and key expenditure components. To explore the primary sources of these fluctuations, we construct a comprehensive dataset of GDP and its components in both nominal and real terms at a quarterly frequency. Applying two SVAR models to this dataset, we uncover the principal drivers of China's economic fluctuations across different episodes. In particular, our findings underscore the distinct impacts of consumption-constrained shocks on household consumption and its various subcomponents throughout the Covid-19 pandemic.  \nKaiji Chen  \nEmory University 1602 Fishburne Drive Atlanta, GA 30322-2240  \nand Federal Reserve Bank of Atlanta [kaiji.chen@emory.edu](kaiji.chen@emory.edu)  \nPatrick C. Higgins  \nFederal Reserve Bank of Atlanta 1000 Peachtree Street, N.E. Atlanta, GA 30309-4470  \n[patrick.higgins@atl.frb.org](patrick.higgins@atl.frb.org)  \nTao Zha  \nDepartment of Economics Emory University  \nRich Memorial Building 1602 Fishburne Drive Atlanta, GA 30322-2240  \nand Federal Reserve Bank of Atlanta and also NBER  \n[tzha@emory.edu](tzha@emory.edu)  \nCONSTRUCTING QUARTERLY CHINESE TIME SERIES 1  \nI. Introduction  \nOver the past four decades, China has experienced sustained growth. At the same time, its macroeconomy has also undergone significant fluctuations, particularly during the global financial crisis (GFC) and the Covid-19 pandemic periods. As China has become the secondlargest economy in the world, the macroeconomic fluctuations in its GDP and various expenditure components have far-reaching impacts on the global economy. It is therefore crucial to understand the driving forces behind these fluctuations and to assess the magnitude and persistence of their impacts on the Chinese economy.  \nResearch on China’s macroeconomic fluctuations faces two primary data challenges. The first concerns the reliability of China’s official quarterly GDP data. The National Bureau of Statistics of China (NBS) provides quarterly data on value-added measures of GDP (hereafter referred to as “GDP-va”) . As Fernald et al. (2021) find, however, the quarterly data on GDP-va was excessively smooth between 2008 and 2016 .1  \nA second, and arguably more critical, challenge pertains to the availability of data on various GDP components. China does not provide basic quarterly macroeconomic data, and much of the data it does provide is not necessar","cbCaikf93nkFx8H8","https://ap.wps.com/l/cbCaikf93nkFx8H8","pdf",4708212,87,"English","# Introduction\n## Data challenges for China’s macroeconomic fluctuations\n## Paper objectives and methodology","[{\"question\":\"What dataset does the paper construct, and at what frequency?\",\"answer\":\"The paper constructs a comprehensive quarterly dataset of expenditure-based GDP (GDP-exp) and its principal components in both nominal and real terms, designed for macroeconomic fluctuation analysis.\"},{\"question\":\"What method does the paper use to identify drivers of economic fluctuations?\",\"answer\":\"It applies two SVAR models to the constructed quarterly data to uncover the principal drivers of China’s economic fluctuations across different episodes.\"},{\"question\":\"What does the paper find about consumption-constrained shocks during Covid-19?\",\"answer\":\"The findings emphasize the distinct impacts of consumption-constrained shocks on household consumption and its various subcomponents throughout the Covid-19 pandemic.\"}]","CONSTRUCTING QUARTERLY CHINESE TIME SERIES USABLE FOR MACROECONOMIC ANALYSIS | PDF",1791024411,219]