Assessing Residual Seasonality in the U.S. National Income and Product Accounts Aggregates

Author:

Chen Baoline1,McElroy Tucker S.2,Pang Osbert C.2

Affiliation:

1. U.S. Bureau of Economic Analysis , 4600 Silver Hill Road, Washington DC 20233, U.S.A.

2. U.S. Census Bureau , 4600 Silver Hill Road, Washington DC 20233-9100, U.S.A.

Abstract

Abstract There is an ongoing debate on whether residual seasonality is present in the estimates of real Gross Domestic Product (GDP) in U.S. national accounts and whether it explains the slower quarter-one GDP growth rate in the recent years. This article aims to bring clarity to this topic by (1) summarizing the techniques and methodologies used in these studies; (2) arguing for a sound methodological framework for evaluating claims of residual seasonality; and (3) proposing three diagnostic tests for detecting residual seasonality, applying them to different vintages and different sample spans of data on real GDP and its major components from the U.S. national accounts and making comparisons with results from the previous studies.

Publisher

Walter de Gruyter GmbH

Reference40 articles.

1. Blakely, C., and T.S. McElroy. 2017. “Signal Extraction Goodness-of-fit Diagnostic Tests Under Model Parameter Uncertainty: Formulations and Empirical Evaluation.” Econometric Reviews 36: 447–467. DOI: https://doi.org/10.1080/07474938.2016.1140277.10.1080/07474938.2016.1140277

2. Canova, F., and B.E. Hansen. 1995. “Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability.” Journal of Business and Economic Statistics 13: 237–252. DOI: https://doi.org/10.1080/07350015.1995.10524598.10.1080/07350015.1995.10524598

3. Cowan, B., S. Smith, and S. Thompson. 2018. “Seasonal Adjustment in the National Income and Product Accounts.” Survey of Current Business 98(8). Available at: https://apps.bea.gov/scb/2018/08-august/pdf/0818-gdp-seasonality.pdf (accessed April 2022).

4. Findley, D.F., D.P. Lytras, and T.S. McElroy. 2017. Detecting Seasonality in Seasonally Adjusted Monthly Time Series. U.S. Census Bureau research report, 2017(3). Available at: https://www.census.gov/content/dam/Census/library/working-papers/2017/adrm/rrs2017-03.pdf (accessed April 2022).

5. Findley, D.F., B.C. Monsell, W.R. Bell, M.C. Otto, and B.C. Chen. 1998. “New Capabilities and Methods of the X-12-ARIMA Seasonal Adjustment Program.” Journal of Business and Economic Statistics 16: 127–177. DOI: https://doi.org/10.1080/07350015.1998.10524743.10.1080/07350015.1998.10524743

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3