Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Automatic locally stationary time series forecasting with application to predicting UK gross value added time series
AU - Killick, Rebecca
AU - Knight, Marina I
AU - Nason, Guy P
AU - Nunes, Matthew A
AU - Eckley, Idris A
PY - 2024/8/23
Y1 - 2024/8/23
N2 - Accurate forecasting of the UK gross value added (GVA) is fundamental for measuring the growth of the UK economy. A common nonstationarity in GVA data, such as the ABML series, is its increase in variance over time due to inflation. Transformed or inflation-adjusted series can still be challenging for classical stationarity-assuming forecasters. We adopt a different approach that works directly with the GVA series by advancing recent forecasting methods for locally stationary time series. Our approach results in more accurate and reliable forecasts, and continues to work well even when the ABML series becomes highly variable during the COVID pandemic.
AB - Accurate forecasting of the UK gross value added (GVA) is fundamental for measuring the growth of the UK economy. A common nonstationarity in GVA data, such as the ABML series, is its increase in variance over time due to inflation. Transformed or inflation-adjusted series can still be challenging for classical stationarity-assuming forecasters. We adopt a different approach that works directly with the GVA series by advancing recent forecasting methods for locally stationary time series. Our approach results in more accurate and reliable forecasts, and continues to work well even when the ABML series becomes highly variable during the COVID pandemic.
U2 - 10.1093/jrsssc/qlae043
DO - 10.1093/jrsssc/qlae043
M3 - Journal article
JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)
JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)
SN - 0035-9254
ER -