Forecasting economic activity in sectors of the Cypriot economy
Journal
Economic Policy/Analysis Papers
Date Issued
October 2018
Editor(s)
Abstract
The aim of this paper is to apply single equation dynamic models together with information
from a large dataset of predictors in the construction of short-term growth forecasts for the
production-side components of the national accounts, i.e. Gross Value Added of sectors, and
import duties plus Value Added Tax. To summarise the information content in a large number
of predictors, we employ techniques such as common factors and forecast combinations.
Aggregate and component forecasts are computed under two approaches to forecasting GDP
growth, namely a direct and a bottom-up approach. In the direct approach, unconstrained
models for GDP growth are estimated to compute forecasts for the aggregate, while
constrained component models are used to obtain the disaggregate forecasts, which add up
to the GDP growth forecasts computed directly. In the bottom-up approach, unconstrained
component models are estimated to compute growth forecasts for the components as well as
for GDP growth by adding up the unconstrained component forecasts. The performance of
aggregate and disaggregate forecasts from the two approaches is assessed via pseudo outof-sample exercises. The results show that the use of macroeconomic and financial predictors
improves on the accuracy of the naïve forecasts for most production-side components and
the aggregate, under both the direct and bottom-up approaches. GDP growth forecasts from
the direct approach are somewhat superior to those from the bottom-up approach. Both
approaches result in gains in forecasting growth in industry, construction, trade, financial
activities and duties. In the sector of professional services gains are limited for both
constrained and unconstrained forecasts. In the sectors of agriculture and public
administration, education and health, neither the unconstrained models nor the constrained
sectoral models significantly improve on the naïve benchmark. Compared to the
unconstrained component forecasts, gains attained through constrained forecasts are slightly
lower, but more widespread across components and horizons.
from a large dataset of predictors in the construction of short-term growth forecasts for the
production-side components of the national accounts, i.e. Gross Value Added of sectors, and
import duties plus Value Added Tax. To summarise the information content in a large number
of predictors, we employ techniques such as common factors and forecast combinations.
Aggregate and component forecasts are computed under two approaches to forecasting GDP
growth, namely a direct and a bottom-up approach. In the direct approach, unconstrained
models for GDP growth are estimated to compute forecasts for the aggregate, while
constrained component models are used to obtain the disaggregate forecasts, which add up
to the GDP growth forecasts computed directly. In the bottom-up approach, unconstrained
component models are estimated to compute growth forecasts for the components as well as
for GDP growth by adding up the unconstrained component forecasts. The performance of
aggregate and disaggregate forecasts from the two approaches is assessed via pseudo outof-sample exercises. The results show that the use of macroeconomic and financial predictors
improves on the accuracy of the naïve forecasts for most production-side components and
the aggregate, under both the direct and bottom-up approaches. GDP growth forecasts from
the direct approach are somewhat superior to those from the bottom-up approach. Both
approaches result in gains in forecasting growth in industry, construction, trade, financial
activities and duties. In the sector of professional services gains are limited for both
constrained and unconstrained forecasts. In the sectors of agriculture and public
administration, education and health, neither the unconstrained models nor the constrained
sectoral models significantly improve on the naïve benchmark. Compared to the
unconstrained component forecasts, gains attained through constrained forecasts are slightly
lower, but more widespread across components and horizons.
File(s)![Thumbnail Image]()
Name
DOA_03-17.pdf
Size
1.2 MB
Format
Adobe PDF
Checksum (MD5)
ed9ee742c41fbfd65652b269ba477b6c