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Mismatch and the Forecasting Performance of Matching Functions
Author(s) -
Hutter Christian,
Weber Enzo
Publication year - 2017
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12142
Subject(s) - matching (statistics) , econometrics , benchmark (surveying) , measure (data warehouse) , aggregate (composite) , great recession , sample (material) , function (biology) , computer science , recession , economics , scale (ratio) , mathematics , statistics , data mining , labour economics , macroeconomics , materials science , chemistry , quantum mechanics , evolutionary biology , biology , composite material , geodesy , physics , chromatography , geography
This paper investigates the role of structural imbalance between job seekers and job openings for the forecasting performance of a labour market matching function. Starting from a Cobb–Douglas matching function with constant returns to scale (CRS) in each frictional micro market shows that on the aggregate level, a measure of mismatch is a crucial ingredient of the matching function and hence should not be ignored for forecasting hiring figures. Consequently, we allow the matching process to depend on the level of regional, qualificatory and occupational mismatch between unemployed and vacancies. In pseudo out‐of‐sample tests that account for the nested model environment, we find that forecasting models enhanced by a measure of mismatch significantly outperform their benchmark counterparts for all forecast horizons ranging between one month and a year. This is especially pronounced during and in the aftermath of the Great Recession where a low level of mismatch improved the possibility of unemployed to find a job again. The results show that imposing CRS helps improve forecast accuracy compared to unrestricted models.