The role of aggregate cost stickiness in unemployment rate prediction

Advances in Mathematical Finance and Applications

مقاله 4، دوره 7، شماره 1، فروردین 2022، صفحه 65-80   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2020.1889826.1361

نویسندگان

Naser Riyahinasab¹؛ Babak Jamshidinavid^{* 1}؛ Alireza Moradi²؛ Mehrdad Ghanbari¹

¹Department of Accounting, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

²Department of Economics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

چکیده

Predicting macroeconomic indicators is very important for policymakers and economists. Unemployment is one of the key indicators of macroeconomics that has adverse economic and social consequences. So far, many models have been proposed to predict this variable, but models in which accounting information was used to predict unemployment rate were ignored. The purpose of this paper is to investigate the relationship between aggregate cost stickiness, as one of the known variables in accounting, and unemployment rate. To this end, seasonal macro level time series data of Tehran Stock Exchange (TSE) and macroeconomic data are analyzed in two stages from 2008:2 to 2018:1. In the first stage, the relationship between these two variables is determined by specifying a linear regression model that is estimated using the OLS method. To investigate the predictive power of this model, the RMSE criterion was estimated in two scenarios with and without aggregate cost stickiness. Secondly, the reaction of the unemployment rate in response to a shock from aggregate cost stickiness is estimated by a Vector Autoregressive (VAR) model and the share of this variable is measured in the fluctuations of unemployment rate. The results show that aggregate cost stickiness improves the forecast of unemployment rate in the horizon previous. Also, the shock of aggregate cost stickiness explains about 6.5 percent of unemployment rate fluctuations.

کلیدواژه‌ها

Unemployment rate؛ aggregate cost stickiness؛ VAR model؛ forecast error

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