principal components analysis

Increasing growth of human knowledge and the effects of its expansion on the various dimensions of economy has increased the welfare of societies. It has affected the function of many economic policy instruments by influencing mechanisms. In this paper, the impacts of financial development on the effectiveness of monetary policy have been observed with special attention to anti-inflationary monetary policies in Iran. For this purpose, we examined the effects of monetary policies using the two instruments of monetary base and interest rates with the aim of controlling inflation, separately. Moreover, in the autoregressive distributed lags models for the period 1368:1 to 1395:4, the effectiveness of each policy has been extracted over time by the rolling regression models. The results again re-estimated in another autoregressive distributed lags model based on the financial development indicator. The coefficients showed from the computed regression demonstrates the decreasing effect of the financial development indicator on the effectiveness of the monetary policies, in the sense that with the development of the financial systems and increasing the methods of access to capital, the impacts of monetary policies of monetary base change and interest rates change on the inflation will be decreased.

Anticipating process of crude oil prices and its fluctuations volatility has always been one of the challenges the traders face in the exchange oil markets. This study estimates the Brent crude oil daily price forecast with a proposed hybrid model. The sample is Brent North Sea crude oil daily prices from July 2008 to July 2016 that is selected from the total oil daily prices in all of the oil markets. In this research, a model for combining statistical and artificial intelligence (PCA-SVR) methods is presented. With regard to the superiority of the accuracy of the prediction of the support vector regression model (SVR) in comparison with other predictive methods in past studies, the main goal in this research is to improve the prediction of the support vector regression using the initial pre-processing of data by principal components analysis (PCA). To do research, after carrying out a static test, using principal components analysis, the input variables are converted into the principal components that cover the entire data scattering and considered as an input to the prediction model. Then, using supporting vector regression model and simulate it in MATLAB software we predict daily price of Brent crude oil. In order to compare the performance of the SVR and PCA-SVR models, we used the paired comparison test. The result of this study was that the initial pre-processing by means of the principal components analysis on the data gives rise to reducing suggested model error,

Typically, Two Parametric and Nonparametric methods are used for calculating Total Factor Productivity (TFP). One the famous nonparametric method is Tornqvist index. In this method, which is based on linear programming especially Data Envelopment Analysis, TFP of one DMU is evaluated during a specific period using some inputs and outputs. In this paper, TFP in road transportation sub sector is evaluated by using Tornqvist index. However, one of the assumptions of this method is that three multiplied by total sum of inputs and outputs should not exceed the number of DMUs. The results show that average technical and technological efficiency and TFP growth are equal to 0. 94, 0. 988, and 0. 932 during period 1377 to 1387, respectively. Also, TFP growth in this sub sector in all considered periods except two periods is negative.

There are two main parametric and nonparametric methods in the literature to measure efficiency. In this paper, nonparametric approach is used to examine e-Commerce efficiency in selected developing countries. Since the output variables included in the model are correlated, principal component analysis is considered and then cross DEA is employed to rank e-Commerce efficiency of developing countries.The results indicate that under the fixed outcome to scale assumption, the most e-Commerce efficient countries are Egypt, Nigeria, Pakistan, Kazakhstan, Vietnam, Algeria, Indonesia and Azerbaijan. The average efficiency score is 43.8 percent. Under the variable outcome to scale assumption, some other countries such as Estonia, India, China, Russia, Ukraine and Iran are also ranked as the most efficient countries and the average efficiency is 63.9 percent. Moreover, using the Cross DEA approach, Indonesia and Vietnam are ranked as the most efficient countries among all selected countries.