One of the main macroeconomic phenomena that has affected Iran's economy for many years is inflation. In line with the importance of this phenomenon and the need to identify the factors affecting it, In the present study, the existence of non-linear relationships between inflation and other influential variables, especially the monetary base, has been examined and in fact, it has been evaluated the threshold effect of high power money on inflation. For this purpose, the Sidesrakis approach and the framework of the nonlinear SMOOTH TRANSITION AUTO-REGRESSIVE have been used in the period of 1354 to 1392. Among variables such as monetary base, exchange rate, capital and time preference rate and internal rate of return; monetary base was chosen as variable TRANSITION. The results showed that: A) Nonlinear LOGISTIC Model, based on the Terasvirta test, has a better estimation than a linear models. B) Inflationary pattern follows regulations of two regimes. In the first regime, the high power money has a positive effect on inflation and is multiplied in the second regime. C) Capital, time preference rate and Internal rate of return have a negative effect on inflation in the first and second regimes; so that the negative effect of variables is reduced in the second regime. D) The transfer speed is 0. 9 and show that the speed of adjustment in the first regime is higher than the second regime.

The main reason of this study is investigation in effect of the number of large industrial segments on Gini coefficient from 1974 to 2012. To this end, linear and nonlinear models are estimated. The results show that nonlinear model has more explanatory power than linear model. We defined three threshold include up, middle and down threshold, with regard to acceptance of nonlinear model. The results also show that variation in the number of industrial segment of last period has the most effect on current Gini coefficient in low threshold and is reduced with moving toward up threshold. The results also show that variation in the number of industrial segment of last period has the most effect on current Gini coefficient in low threshold and is reduced with moving toward up threshold.

In this study, we tried to analyze the asymmetric effects of macroeconomic variables on the added value of agriculture sector of Iran. In this context, the SMOOTH TRANSITION AUTO REGRESSIVE model (STAR) was utilized over 1990-2012. Based on the results, the growth of added value of agricultural sector was chosen as the TRANSITION variable; therefore the growth of added value of agricultural exhibited the asymmetric behavior. In order analyzing the asymmetrical behavior based on Trasvyrta nonlinear test, the LSTR models is selected as suitable model which became a turning point for the coefficients in the LOGISTIC function, corresponding to 43% of added value of agriculture in a previous period of TRANSITION between the growth rate of value added (g), is the moderate amounts is equal to 4.138. LSTR model results indicated that the effectiveness of monetary policies on the agricultural sector in high growth and low-value of agricultural sector has been different. The effects of macroeconomic variables including positive or negative effects on the high growth of value added of agricultural is more than low growth.

In this paper, an approximate mathematical expression is proposed for the residual variance of AUTO-REGRESSIVE (AR) estimation in the case where the AR estimation method is Least-Squares- Forward (LSF), using statistical arguments and approximations as well as Hilbert space concepts. This expression approximates the statistical behavior of the residual variance. While its validity is tested through simulations. This important formula can be employed to propose various AR order selection methods. Such as the method proposed in this paper which is an iterative algorithm. The performance of this algorithm is compared with other existing order selection methods using simulations. The results of which demonstrate that in spite of the information criteria, the overestimation probability of the proposed algorithm does not increase in short data record cases.

This paper investigates non linear effects of income on health expenditure (as out of pocket payment). The explanatory variables in health expenditure equation include urbanization, age, insurance, employment of household's head, the number of people employed, earned income and the number of persons aged fewer than 12 and over 60 years in the household. Database used was chosen from the household income & expenditure surveys in 1386. Database contains 31277 rural and urban households. Linear and non-linear econometric models based on LOGISTIC SMOOTH TRANSITION regression (STR) are estimated. One of the most important findings is that health spending shows asymmetric behavior relative to household income or total expenditure (as a TRANSITION variable) so that the impact of explanatory variables on health expenditure in the high and low income regime is different. Also the results show that health expenditure is a necessary good for households, because the coefficient of the logarithm of income in the demand function is positive and much smaller than unity. In addition, out of pocket payments for health care services in high income groups are more than low income ones. Based on the results, with increased urbanization and for high aged people (beyond sixty years of old), health expenditure increase but if family members are covered by insurance, out of pocket payment for health expenses will reduce. So, it is important to develop the social security system and increase its efficiency to cover more health expenditure, especially for poorer households which lead to justice in health areas and increase in welfare of society.

A detector for the case of a radar target with known Doppler and unknown complex amplitude in complex Gaussian noise with unknown parameters has been derived. The detector assumes that the noise is an AUTO-REGRESSIVE (AR) process with Gaussian AUTOcorrelation function which is a suitable model for ground clutter in most scenarios involving airborne radars. The detector estimates the unknown parameters by Maximum Likelihood (ML) estimation for the use in the Generalized Likelihood Ratio Test (GLRT). By computer simulations, it has been shown that for large data records, this detector is Constant False Alarm Rate (CFAR) with respect to AR model driving noise variance. Also, measurements show the detector excellent performance in a practical setting. The detector’s performance in various simulated and actual conditions and the result of comparison with Kelly’s GLR and AR-GLR detectors are also presented.

Summary:The main purpose of exploration seismology is data gathering, data processing, and finally obtaining an interpretable image of subsurface layers. Sometimes, because of problems such as undesirable area topography, instrument defects, and environmental constraints, we have data with missing spatial samples. Reconstruction and recovery of the missing data can be carried out using interpolation and reconstruction methods. There are many reconstruction and interpolation methods. One of the most useful methods to reconstruct missing data is the AUTO-REGRESSIVE model. This method refers to the techniques that model the evolution of a signal as a function of its past/future samples(Lau et al., 2002; Takalo et al., 2005). Also, it has a wide range of applications in signal processing including noise suppression (Canales, 1984), parametric spectral analysis (Marple, 1987), and signal interpolation and reconstruction (Sacchi and Ulrych, 1996; Porssani, 1999; Spits, 1991; Naghizade and Sacchi, 2007). The AUTOREGRESSIVE reconstruction methods were introduced by Spitz (1991). Spitz (1991) proposed computing prediction filters (AUTOREGRESSIVE operators) from low frequencies to predict interpolated traces at high frequencies. This methodology is applicable only if the original seismic section is regularly sampled in space. Conversely, irregularly sampled data can be reconstructed using Fourier methods. In this case, the Fourier coefficients of the irregularly sampled data are retrieved by inverting the inverse Fourier operator with a band limiting and/or a sparsity constraint (Sacchi et al., 1998; Zwartjes and Gisolf, 2006). In this paper, a reconstruction method has been introduced that combines a Fourier-based method and an AUTO-REGRESSIVE model to reconstruct the missing data. The method includes a two-stage algorithm. The first step of the proposed algorithm involves the reconstruction of the irregularly missing spatial data on a regular grid at low frequencies using a Fourier-based algorithm called the minimum-weighted norm (Liu and Sacchi, 2004) method. Fourier reconstruction methods are well suited to reconstruct seismic data in the low-frequency (non-aliased) portion of the Fourier spectrum. The reconstruction problem is well-conditioned at low frequencies where only a few wavenumbers are required to honor the data. This makes the problem well-posed; therefore, it is quite easy to obtain a low frequency spatial reconstruction of the data. Seismic data at low frequencies are band-limited in the wavenumber domain. Due to the band-limited nature of the wavenumber spectra at low frequencies, this portion of the data can be reconstructed with high accuracy (Duijndam et al., 1999). Then, prediction filter components are computed for all frequency bands from the low-frequency portion of the reconstructed data using the AUTO-REGRESSIVE method. Finally, these prediction filters are used to reconstruct the missing data. The basic equations for computing the prediction filter components (AUTO-REGRESSIVE operators) and reconstructing the missing data are as follows:xn(f)=åLj=1Pj(af)xn-aj(f),¬ forward & xn*(f)= åLj=1Pj(af)xn+ aj(f), ¬ backwardn=aL+1, …, N, n=1, …, N-aLThe aforementioned equations show that one can predict the data samples using past/future samples (forward/backward equations). It is important to stress that the technique presented in this paper can only be used to reconstruct data that live on a regular grid with missing observations. The results of the application of the algorithm on both synthetic and real seismic data showed and confirmed the performance of the method.

The main purpose of this paper is using the probablity models, AUTO REGRESSIVE Moving Average (ARMA) in order to modeling of daily position time series of permanent GPS station. The daily position time series of LLAS site in Southern California region from SCIGN array that were active during January 1,2000 to Dec 30, 2006 are evaluated for analysis and determinig of daily position time series. According of daily position time series, a site motion model is used to estimate simultaneously geodetic parameters such as: linear trend, annual harmonics, semi annual harmonics and offsets. In each daily position time series, model parameters are estimated using weighted least squares. In this study, AUTO Correlation Function (ACF) and Partial AUTO Correlation Function (PACF) are used as study tools for identification of behavior of daily position time series of permanent GPS station. These functions provide consideration of correlations between daily positions of daily time series. Moreover, Akaike Information Criterion is used to identify model orders, because some kind of ARMA model may appropriate for a daily position time series of GPS station. In this study, some numerical results shows that a model order from (1, 1) is appropriate for direction N of permanent GPS station. Probabality model of ARMA (2, 1) is best model for direction E and a model order from (1, 1) is suitable for direction U. In the final step, a daily position time series of LLAS permanent station were predicted for seasonal component.