Abstract:
Despite the importance and necessity of accurate statistics and appropriate sequence of data for economic analysis and policymaking, economic data in many developing countries, including Iran, are produced with low accuracy and frequency because of various reasons. Satellite images, especially night light images, with high accuracy and frequency has been used in recent years as an important source of information to estimate the level of economic activity in the world and specially in developing countries which have limited or not accurate data sets . In this paper, for the first time, a new generation of monthly night light data (VIIRS) has been collected, processed, refined and used to estimate the seasonal GDP of Iran. Results of this study show that using these data alongside with seasonal population estimation and seasonal dummy variables can achieve a very good estimation of Iran's Gross domestic product at the national level and hence these data are not manipulated they can be used for more purposes.
Machine summary:
What distinguishes the present research from previous foreign studies is the evaluation of the explanatory power of nighttime light imagery using Iranian data (including nighttime light information, Gross Domestic Product, and population); the distinguishing features of this study compared to the few Persian studies include the following: Use of the latest version of nighttime light data (VIIRS) instead of the previous version (DMSP) in the economy of Iran, which shows the possibility of utilization for future years.
The results of the estimates in this research show that monthly nighttime light data, especially when integrated with population and seasonal variables, provides a very good estimate of Iran's Gross Domestic Product (excluding oil) at the national level and can be used as an acceptable approximation for estimating this indicator.
The use of seasonal time series, especially prior to the expansion of information technology and the development of real-time data gathering methods2, firstly facilitated the monitoring of changes in economic indicators and variables for researchers and policymakers, and secondly, while using seasonal time series imposed less cost on statistical centers for collection and dissemination, ________________________________________________________________ 1 1Proxy 2Real-time data gathering did not also include short-term fluctuations and measurement errors (Hillmer & Bell, 2012).
By using DMSP nightlight data between the years 2001 and 2013 to estimate the informal sector of Iran's economy and comparing it with official statistics published regarding Gross Domestic Product, they examined the effects of economic sanctions by the United States and the European Union in the 2012-2013 time period.
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