درخت حوزه‌های تخصصی

This paper examines monetary and fiscal policy through the estimation of a New-Keynesian dynamic general equilibrium model for Iran’s economy. In this New-Keynesian dynamic general equilibrium model, the consumers encounter the liquidity constraint and the firms face sticky prices, while they are changing them. In the model presented, a role is considered for both government spending and taxation, besides the monetary rule. Then, the model is estimated using Iran's data over the period of 2002-2017, through the method of generalized moments which leads into valuable insight. The results indicate that aggregate demand reacts to changes in interest rates. When inflation occurs, regardless of its source, it is persistent and inertia.The monetary policy has a forward-looking behavior. The output gap with a lag has a negative effect on government spending and the short-term impact of the output gap on government spending is smaller than its impact on taxation (Tax responses to the output gap are stronger and positive).

One of the main concerns of investors is the evaluation of the return on investment, which is conducted using various models such as the CAPM (single-factor model), Fama-French three/five-factor models, and Roy and Shijin’s six-factor model and other models known as multi-factor models. Despite the widespread use of these models, their major drawbacks include sensitivity to unexpected changes, sudden shocks, high turbulence of price bubble, and so on. To eliminate such negatives, the multi-factor model using the penalty function method is used, in which, instead of averaging, the optimization and avoidance of the effects of abnormal changes and other factors affecting the capital market are considered. In order to evaluate stock returns, it is possible to select effective factors, to simulate and develop a model appropriate to the conditions governing the capital market in Iran. In the present study, by forming portfolios of investments and identifying and refining effective factors, the classification and estimation of the hybrid model of penalty and multi-factor (P & PCA) functions were performed based on the functional data during 2007-2017. The results of this study indicated that the extensive use of the simulation algorithm for the penalty function in the form of P & PCA estimation method improves the efficiency of multi-factor methods in stock return evaluation, and that the use of the hybrid algorithm of penalty and multi-factor functions, compared to the exclusive use of multi-factor models, brings a higher accuracy in estimating stock returns.