High levels of nonperforming loans provide negative impacts on the growth rate of gross domestic product. Therefore, predicting the occurrence of nonperforming loans is a vital issue for the financial sector and governments. In this paper, an intelligent nonlinear model is proposed for describing the behavior of nonperforming loans. In order to find the optimal parameters of the model, a new fractional bee colony algorithm (BCA) based on fractional calculus techniques is proposed. The inputs of the nonlinear model are the loan type, approved amount, refund amount, and economic realm. The output of the regression model is that whether the current information is for a nonperforming loan or not. Consequently, the model is modified to detect the status of a loan. So, the modified model predicts the occurrence of a nonperforming loan and determines the loan status, i. e., current, overdue, and nonperforming. The proposed procedure is applied to data gathered from an economic institution in Iran. The findings of this study are helpful for the managers of banks, and financial sectors to forecast the future of the loans and, therefore, manage the budget for the upcoming loan requests.

The present study investigates the effect of energy consumption on economic growth and emission through applying non-linear framework, STR† and TAR‡ , using the annual data from 1969 to 2017. Therefore, impact of energy consumption on economic growth and emission have been tackled using two models. In both, energy consumption causes the breakpoint and effects of included variables are depending on the value of energy consumption. Discussing the economic growth, the growth fossil fuel energy consumption variable is considered as a transition variable at 12 percent value. For emission beside the energy variables, total population used as control variable and for economic growth, physical capital. The prominent point in this framework is that in both of them, the fossil fuel consumption growth variable has been chosen as threshold variable which is in fact policy making variable. Due to asymmetric impacts of included, variables on emission and economic growth which both are highly crucial, nonlinear approach capture the dynamics much better and gives clear descriptions to policy maker on how to react according to the state’ s economy is in.

In this paper, the nonlinear regression models when the model errors follow a slash skew-elliptical distribution, are considered. In the special case of nonlinear regression models under slash skew-t distribution, we present some distributional properties, and to estimate their parameters, we use an EM-type algorithm. Also, to find the estimation errors, we derive the observed information matrix analytically. To describe the influence of the observations on the ML estimates, we use a sensitivity analysis. Finally, we conduct some simulation studies and a real data analysis to show the performance of the proposed model.

1. Introduction: From 1960s several attempts have been made to measuring the rock brittleness index BI. Schwartz (1964) using results of a series of triaxial tests on rock samples, stated that the rock’ s behavior from frangibility to ductility happens in 4. 3 ratios of principal stresses. Altindag (2002; 2003) introduced a new method for prediction of the BI by the division of the uniaxial compressive strength (UCS) of the rock to Brazilian tensile strength (BTS). In the late 1960s punch penetration test (PPT) introduced by Handewith (1971) to measure some physical properties of rock sample related to hardness and toughness of rock. Yagiz (2006) stated that the PPT’ s results for measuring the BI have a very high correlation with TBM penetration rate. Although the PPT has very delightful results, application of this test is very expensive and needs much time as well...

The purpose of this study is to analyze the nonlinear behavior of monetary policy at the Central Bank of the Islamic Republic of Iran in uncertainty condition. To achieve this goal, the nonlinear behavior of the central bank's reaction function to the inflation uncertainty in the form of smooth Transition Regression model (STR) is estimated using seasonal data during the 1997: 1-2015: 3 periods. The results of this study indicate that the variable behavior and regime change in monetary policy of the central bank of Iran in the various conditions of inflation. By passing the uncertainty of inflation from a threshold level, the behavior of monetary policy has been changed so that the coefficients of the model are consistent with the Taylor rule. Based on the results of the present study, it is suggested to consider the economic uncertainty index in order to determine the optimal monetary policy.