Carlo A. Favero
;
M. Hashem Pesaran
;
Sunil Sharma

a duration model of irreversible oil investment: theory and empirical evidence (replication data)

The aim of this paper is to analyse the implications of the theory of irreversible investment under uncertainty for investment in oil fields on the United Kingdom Continental Shelf (UKCS). We consider the problem of an operator who owns a licence to develop and extract oil from a field of known capacity. An intertemporal optimization model in discrete time is developed to derive decision rules for the timing of the irreversible development investment and for the optimal rate of extraction. Model simulation is then used to describe the properties of the numerical solutions. The predictions of the theory on the determinants of the irreversible investment decision are then examined using statistical duration analysis. Data on the length of the time period between discovery and development are available for individual fields on the UKCS. We measure the duration of the irreversible investment gestation lag for each field and test the model by assessing the significance of the theoretical variables in explaining the significance of such a lag. Both our theoretical model and our empirical results suggest the importance of a nonlinear interaction of the level of oil prices and the volatility of oil prices in determining the development lag. The simulation of our theoretical model shows a nonlinear impact of oil price volatility on the trigger level of oil prices. Our empirical results suggest that the effect of price volatility is a function of the expected price level, with increased price volatility having a positive impact on the duration of investment appraisal when expected prices are low and a negative impact when they are high.

Data and Resources

Suggested Citation

Favero, Carlo A.; Pesaran, M. Hashem; Sharma, Sunil (1994): A duration model of irreversible oil investment: Theory and empirical evidence (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022313.1130947069