We investigate the potential for statistical forecasting of aggregate oil and gas investment on the Norwegian Continental Shelf (NCS). A unique and detailed dataset containing data from 109 different fields on the NCS between 1970 and 2015 was employed. A set of 1080 autoregressive distributed lag models are evaluated pseudo out-of-sample and tested for data mining by utilizing a Diebold-Mariano hypothesis test and the model confidence set procedure by Hansen and Lunde (2011). The main results are as follows. First, we find that it is indeed possible but challenging to outperform the parsimonious random walk benchmark in an out-of-sample environment. Second, lags of investment growth, crude oil price growth and realized volatility is found to be adequate predictors for the investment growth. Finally, there is a clear benefit from re-estimating the models coefficient at every step.
investment oil and gas sector Norwegian Continental Shelf pseudo out-of-sample forecasting