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A STOCHASTIC ESTIMATION FRAMEWORK FOR COMPONENTS OF THE SOUTH AFRICAN CONSUMER PRICE INDEX
Author(s) -
Aron Janine,
Muellbauer John N. J.,
Pretorius Coen
Publication year - 2009
Publication title -
south african journal of economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.502
H-Index - 31
eISSN - 1813-6982
pISSN - 0038-2280
DOI - 10.1111/j.1813-6982.2009.01214.x
Subject(s) - inflation (cosmology) , economics , econometrics , index (typography) , kalman filter , identification (biology) , aggregate (composite) , price index , estimation , monetary policy , consumer price index (south africa) , transmission (telecommunications) , inflation targeting , macroeconomics , computer science , statistics , mathematics , telecommunications , physics , botany , materials science , management , world wide web , composite material , biology , theoretical physics
Under inflation targeting in South Africa, it is important to monitor and forecast changes in prices, not only for aggregate measures of the consumer price index, but also its underlying sub‐components. Hypotheses about sectoral transmission of policy and shocks are often more specific than hypotheses about overall transmission. This study employs a stochastic framework to estimate richly specified equilibrium correction models, four‐quarters‐ahead, for the 10 sub‐components of the first targeted measure of the consumer price index, CPIX. The stochastic trends are estimated by the Kalman filter, and interpreted as capturing structural breaks and institutional change, a frequent cause of forecast failure. The trends suggest the design of deterministic split trends for use in recursive forecasting models, towards more accurate overall inflation forecasting. This research also has practical use for monetary policy in allowing identification of sectoral sources of inflation.