How do daily changes in oil prices affect US monthly industrial output?

- We test for asymmetry between daily oil prices and monthly industrial output.
- We compare the results with those using averaged/aggregated oil price data.
- The use of averaged/aggregated oil price data masks the extent of asymmetries.
- Dynamic marginal lagged effects of price rises are relatively shorter and sharper.
- Marginal effects of price falls take longer to die off than those of price hikes.

Detecting asymmetry has become increasingly difficult using single frequency data. This paper goes beyond the prevailing use of aggregate/averaged data in order to provide a more in-depth treatment of the dynamic effects of the price of crude oil on industrial output growth. To do so, we propose an Asymmetric Mixed Data Sampling (AMIDAS) model to examine if there is any concealed evidence of asymmetry arising from daily effects of the price of crude oil on monthly changes in industrial output in the United States (US). We find that this model is able to detect dynamic asymmetric impacts of a high frequency independent variable on a low frequency dependent variable more effectively than when the high frequency variable is aggregated up at the time interval of the low frequency variable. We find that, in comparison with the marginal lagged effects of a rise in the daily price of crude oil, the effects of a fall in the daily price of crude oil are more sluggish as it takes longer for the effects of the oil price drop to die off over time. This finding implies that a fall in the price of crude oil shifts the supply curve rightward less and at a much slower pace than an equivalent price rise shifts it to the left.