Health progress and economic growth in the United States: the mixed frequency VAR analyses

Yi Hui Liu, Wei-Shiun Chang, Wen Yi Chen

研究成果: Article

摘要

Previous time series analyses on the effect of business cycles on population health suffered from potential aggregation and omitted variable biases stemming from the aggregation of the high frequency data commonly used to measure business cycles (such as quarterly GDP per capita or monthly unemployment rate) into low frequency data (such as annual GDP per capita or unemployment rate). In order to deal with this temporal aggregation issue, this study applies the Mixed Frequency Vector Auto-regressive (MF-VAR) model to investigate the causal relationship between health progress and economic growth during the period of 1948:Q1–2016:Q4 in the US for the first time. Our results based on the forecast error variance decomposition suggest that the MF-VAR model achieves higher explanatory power than the conventional VAR model with single frequency data. Despite a bi-directional causation between health progress and economic growth being identified using the mixed frequency Granger causality tests, the impulse-response analyses under the MF-VAR model found a changing correlation between health progress and economic growth across the four quarters within each 1-year time period. These findings effectively reconcile the controversial results from previous time series analyses on the effect of business cycles on population health.

原文English
頁(從 - 到)1895-1911
頁數17
期刊Quality and Quantity
53
發行號4
DOIs
出版狀態Published - 2019 七月 15

指紋

Economic Growth
Health
Vector Autoregressive Model
economic growth
Business Cycles
business cycle
aggregation
health
Unemployment
unemployment rate
time series
Aggregation
Time series
Temporal Aggregation
Variance Decomposition
High-frequency Data
Granger Causality
Causation
Impulse Response
causality

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences(all)

引用此文

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abstract = "Previous time series analyses on the effect of business cycles on population health suffered from potential aggregation and omitted variable biases stemming from the aggregation of the high frequency data commonly used to measure business cycles (such as quarterly GDP per capita or monthly unemployment rate) into low frequency data (such as annual GDP per capita or unemployment rate). In order to deal with this temporal aggregation issue, this study applies the Mixed Frequency Vector Auto-regressive (MF-VAR) model to investigate the causal relationship between health progress and economic growth during the period of 1948:Q1–2016:Q4 in the US for the first time. Our results based on the forecast error variance decomposition suggest that the MF-VAR model achieves higher explanatory power than the conventional VAR model with single frequency data. Despite a bi-directional causation between health progress and economic growth being identified using the mixed frequency Granger causality tests, the impulse-response analyses under the MF-VAR model found a changing correlation between health progress and economic growth across the four quarters within each 1-year time period. These findings effectively reconcile the controversial results from previous time series analyses on the effect of business cycles on population health.",
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Health progress and economic growth in the United States : the mixed frequency VAR analyses. / Liu, Yi Hui; Chang, Wei-Shiun; Chen, Wen Yi.

於: Quality and Quantity, 卷 53, 編號 4, 15.07.2019, p. 1895-1911.

研究成果: Article

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