Measuring Economic Uncertainty in Iran: Integrating Economic and Institutional Factors

Uncertainty is a controversial issue in the philosophy and methodology of economics. since economic uncertainty is not directly observable, quantifying it is confronted with significant complexities. a common method in this context involves computing the proxy of uncertainty using time series models. within this framework, the conditional volatility of the unpredictable components of time series is considered as an uncertainty measure. in this regard, the basic forecasting model should be specified in a way that its forecast errors lack any predictable content. in previous studies, the focus has solely been on economic and financial variables in computing the uncertainty measure, while the role of institutional factors has been neglected in the forecasting model. meanwhile, based on economic literature, institutions play an important role in controlling and reducing uncertainty. therefore, in the present study, the economic uncertainty measure is extracted based on a large-dimensional dynamic factor model, employing a set of 72 macroeconomic and institutional time series for the iranian economy. the data are quarterly and span the period 1991:q2-2022:q1. the results indicate that overlooking institutional factors in the forecasting model can lead to an overestimation of uncertainty. our perspective enhances the accuracy of uncertainty measurement and provides a more comprehensive understanding of the determinant factors of uncertainty.