An Assessment of the Consumption Function for Iran

In this study, the real private consumption model for Iran was estimated by applying yearly data from 1990 to 2018. The ARDL method is used to assess short-term and long-term relationships between private consumption, labor income, interest rate, wealth, and unemployment rate. According to long-term estimates, income and wealth determine the actual consumption in Iran. However, in the short run, current incomes, wealth, real interest rates, and the unemployment rate are the key determinants of private consumption in Iran. The dynamic of the consumption function shows that all the factors of consumption i.e. real disposable income, wealth, and unemployment rate, real interest rate, have a noteworthy effect on aggregate consumption. The minor and significant coefficient of wealth indicates that the consumption decision is weakly affected by wealth. It provides evidence of the validity of AIH for Iran

An Assessment of the Consumption Function for Iran

In this study, the real private consumption model for Iran was estimated by applying yearly data from 1990 to 2018. The ARDL method is used to assess short-term and long-term relationships between private consumption, labor income, interest rate, wealth, and unemployment rate. According to long-term estimates, income and wealth determine the actual consumption in Iran. However, in the short run, current incomes, wealth, real interest rates, and the unemployment rate are the key determinants of private consumption in Iran. The dynamic of the consumption function shows that all the factors of consumption i.e. real disposable income, wealth, and unemployment rate, real interest rate, have a noteworthy effect on aggregate consumption. The minor and significant coefficient of wealth indicates that the consumption decision is weakly affected by wealth. It provides evidence of the validity of AIH for Iran

A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates