Time-varying parameter energy demand functions: Benchmarking state-space methods against rolling-regressions

Time-varying parameters and elasticities are an appealing extension to constant parameter energy demand functions. In a recent study Altinay and Yalta (2016) use a modified rolling-regression method to approximate time-varying elasticities of demand for natural gas in Istanbul. In a related literature the state-space econometric framework has been used to directly/formally estimate such time-varying effects in energy studies. Through a Monte Carlo simulation exercise, we compare and contrast these two methods and provide evidence that rolling regressions fail to obtain ‘accurate’ estimates (and hence economic implications) of time-varying coefficients in around 80% of our replications for small samples and 40% of replications in large samples. Conversely state-space models are ‘accurate’ 60% of the time in small samples, and 90% of the time in larger samples. We further argue that rolling regressions can lead to unsatisfactory policy recommendations more often than might be considered acceptable, by generating ‘over-confident’ estimates of the wrong elasticity value (i.e. ‘inaccurate’ coefficient estimates with tight confidence intervals that never include the true coefficient). Various robustness checks confirm the invariance of our conclusions to: missing variables; serially dependent errors; a mixture of stationary and non-stationary variables; and choices regarding window size. Flexible least squares and structural time series models are also considered for completeness.