Singular Spectrum Analysis: An Application to Kenya’s Industrial Inputs Price Index
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Date
2022-01-07
Journal Title
Journal ISSN
Volume Title
Publisher
EJ-MATH, European Journal of Mathematics and Statistics
Abstract
Time series modeling and forecasting techniques serve as gauging tools to
understand the time-related properties of a given time series and its future course. Most financial
and economic time series data do not meet the restrictive assumptions of normality, linearity, and
stationarity of the observed data, limiting the application of classical models without data
transformation. As non-parametric methods, Singular Spectrum Analysis (SSA) is data adaptive; hence do not necessarily consider these restrictive assumptions as in classical methods.
The current study employed a longitudinal research design to evaluate how SSA fist Kenya’s
monthly industrial inputs price index from January 1992 to April 2022. Since 2018, reducing the
costs of industrial inputs has been one of Kenya’s manufacturing agendas to level the playing
field and foster Kenya’s manufacturing sector. It was expected that Kenya’s Manufacturing
Value Added hit a tune of 22% by 2022. The study results showed that the SSA (L = 12, r =7)
(MAPE = 0.707%) provides more reliable forecasts. The 24-period forecasts showed that the
industrial inputs price index remains high above the index in 2017 before the post-industrial
agenda targeting a reduction in the cost of industrial inputs. Thus, the industrial input prices
should be reduced to a sustainable level
Description
Keywords
Industrial inputs price index,, singular spectrum analysis, singular value decomposition.