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.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- General Social Sciences