Modeling Seasonal Unit Roots as a Simple Empirical Method to Handle Autocorrelation in Demand Systems: Evidence from UK Expenditure Data

dc.creatorSilva, Andres
dc.creatorDharmasena, Senarath
dc.date2017-04-01T19:37:33Z
dc.date.accessioned2026-07-09T07:09:02Z
dc.descriptionEconomic data with substantial seasonality are likely to have unit roots in more than one frequency. Using non-alcoholic beverage expenditure data from the United Kingdom, we empirically show that the absence of unit roots in one frequency (e.g. monthly) does not imply the absence of unit roots in some other frequencies (e.g. quarterly, bi-annually, and annually). Given the evidence of seasonal unit roots, we estimated one static and three dynamic quadratic almost ideal demand system (QUAIDS) specifications. We found that the seasonal-habit QUAIDS outperforms the static, myopic-habit and rational-habit specifications. Additionally, we show that taking into account seasonal habits helps correct autocorrelation in residuals. Simply put, given the presence of seasonal unit roots, lagged seasonal terms can be a useful simple tool for practitioners modeling expenditure data using demand systems.
dc.identifierdoi:10.22004/ag.econ.149928
dc.identifierhttps://ageconsearch.umn.edu/record/149928/files/Seasonal%20QUAIDS%20AAEA%202013.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/149928
dc.identifier.urihttp://hdl.handle.net/123456789/585075
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/149928
dc.titleModeling Seasonal Unit Roots as a Simple Empirical Method to Handle Autocorrelation in Demand Systems: Evidence from UK Expenditure Data
dc.typeText

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