Principal-Component Analysis

The aims of the present study are developing a financial stability index (FSI) using banking indices to measure financial stability in Iran, and examining the relationship between financial stability and macroeconomic variables for policymaking. To these ends, we have employed principal-component analysis, out of sample forecasting, Autoregressive Integrated Moving Average (ARIMA) method, and Vector Error Correction Model (VECM). The monthly data period is spanning 2007:3 through 2017:2. We find evidence of one cointegrating vector. According to the cointegration test, there is a long-run relationship running from inflation, Gross Domestic Product (GDP) growth rate, and unemployment to FSI. Also, the results of the Engle-Granger test indicate bidirectional causality between FSI and unemployment. Forecast evaluation shows that VECM-based FSI prediction is more accurate than the ARIMA model.