Employing the Structural Time Series Model (STSM) approach suggested by Harvey (1989Harvey ( , 1997, and based on annual data for the UK from 1967-2002, this paper reiterates the importance of using a stochastic rather than a linear deterministic trend formulation when estimating energy demand models, a practice originally established by Hunt et al. (2003a,b) using quarterly UK data. The findings confirm that important non-linear and stochastic trends are present as a result of technical change and other exogenous factors driving demand, and that a failure to account for these trends will lead to biased estimates of the long-run price and income elasticities. The study also establishes that, provided these effects are allowed for, the estimated long-run elasticities are robust to the different data frequencies used in the modelling.JEL Classification: C52, Q41
This paper investigates whether Google Trends search information can improve forecasts of cinema admissions, over and above those based on seasonal patterns in the data. Using monthly data for the UK for the period 2004(1) to 2008(12) we examine various forecasting models that incorporate Google Trends search information. We find clear evidence that Google Trends data on searches relevant to cinema visits do have the potential to increase the accuracy of cinema admissions forecasting models. There is also some evidence to suggest that Google Trends indexes based on combined information from searches using a number of different search terms work better than those based on only a single keyword. The results also appear to confirm earlier findings that the UK cinema admissions series is more suitably modelled by the use of fixed seasonal dummies than through autoregressive formulations.
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