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

Aynur Alptekin, David Broadstock, Xiaoqi Chen and Dong Wang

Energy Economics, 2019, vol. 82, issue C, 26-41

Abstract: 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.

Keywords: Natural gas demand; Time-varying parameters; State-space model; Rolling regressions; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C22 C51 C63 Q4 Q41 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:82:y:2019:i:c:p:26-41

DOI: 10.1016/j.eneco.2018.03.009

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