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Erschienen in: Journal of Quantitative Economics 2/2023

27.01.2023 | Original Article

Oil Demand and Supply Shocks in Canada’s Economy

verfasst von: Juste Somé

Erschienen in: Journal of Quantitative Economics | Ausgabe 2/2023

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Abstract

This paper investigates how oil supply shocks, aggregate demand shocks, and speculative oil demand shocks affect Canada’s economy, within an estimated Dynamic Stochastic General Equilibrium (DSGE) model. The estimation is conducted using Bayesian methods, with Canadian quarterly data from 1983Q1 to 2021Q4. The results suggest that the dynamic effects of oil price shocks on Canadian macroeconomic variables vary according to their sources. In particular, a 10 percent increase in the real price of oil driven by positive foreign aggregate demand shocks has a positive effect of about 1.2 percent on Canada’s real GDP upon impact and the effect remains positive over time. In contrast, an increase in the real price of oil driven by negative foreign oil supply shocks or by positive speculative oil demand shocks causes a small effect of about 0.15 percent on Canada’s real GDP upon impact but causes a slightly decline afterwards. At the same time, an oil price increase that originates from aggregate demand shock causes an increase in consumption and investment, while an oil price increase that originates from oil supply shocks or from speculative oil demand shocks cause a decline in consumption and investment. Furthermore, among the identified oil shocks, aggregate demand shocks have been by fare more important in explaining the variations of most of Canadian macroeconomic variables over the estimation period. In contrast, speculative oil demand shock appears to be the first source of variations in real oil price.

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Fußnoten
1
The main advantage DSGE models is their theoretical economic coherence while VAR models aim at only empirical coherence. Parameters in VAR models most often do not have clear economic sense. Moreover, the identified structure of the shocks in VAR models is fundamental and most often there is no consensus between researchers on the identifying assumption.
 
2
The model has been designed to explain the macroeconomic effects of increases in oil prices driven by exogenous shocks that originate from abroad. It is not meant to explain the effects of oil price fluctuations due to new oil reserve discoveries in Canada.
 
3
This way of introducing adjustment costs in oil intensity provides a flexible approach to capture the wedge between the short and long run elasticity of oil demand.
 
4
There are several reasons for carrying inventories for oil, including uncertainty about the size of future demand, uncertainty about the amount of lead time for deliveries, provision for greater assurance of continuing production, and speculation on future prices of oil.
 
5
The strategy to choose appropriate values for prior information is to start with given values in the prior domains and adjust these according to whether the optimizer indicates upper-bound constraints or lower-bound constraints for the particular parameter.
 
6
Like the Metropolis–Hastings sampler, the Gibbs sampler is an alternative MCMC sampler. However, this sampler requires to know the close form of the conditional distributions for each of the variables, which is most often two complex to determine in the case of DSGE model. The Metropolis-Hasting sampler is more adequate for estimation of DSGE models since it does not require to know the closed form of the conditional distributions for sampling. The disadvantage of Metropolis–Hastings sampler is that it can have a poor convergence rate.
 
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Metadaten
Titel
Oil Demand and Supply Shocks in Canada’s Economy
verfasst von
Juste Somé
Publikationsdatum
27.01.2023
Verlag
Springer India
Erschienen in
Journal of Quantitative Economics / Ausgabe 2/2023
Print ISSN: 0971-1554
Elektronische ISSN: 2364-1045
DOI
https://doi.org/10.1007/s40953-023-00339-w

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