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Predicting small region sectoral responses to changes in aggregate economic activity: A time series approach
Author(s) -
Weller Barry R.
Publication year - 1990
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980090306
Subject(s) - univariate , aggregate (composite) , econometrics , series (stratigraphy) , econometric model , time series , economics , manufacturing sector , function (biology) , computer science , multivariate statistics , macroeconomics , paleontology , materials science , machine learning , evolutionary biology , composite material , biology
The purpose of this paper is to investigate the applicability of a contemporary time series forecasting technique, transfer function modeling, to the problem of forecasting sectoral employment levels in small regional economies. The specific sectoral employment levels to be forecast are manufacturing, durable manufacturing, non‐durable manufacturing and non‐manufacturing employment. Due to data constraints at the small region level, construction of traditional causal econometric models is often very difficult; thus time series approaches become particularly attractive. The results suggest that transfer function models using readily available national indicator series as drivers can provide more accurate forecasts of small region sectoral employment levels than univariate time series models.