the purpose of this study is to determine spatial dependency pattern of systematic risk of dry land wheat production in iran, using spatial autoregressive models. to this end, spatial weighted contiguity matrix was constructed based on the delaunay triangularization method, and correlation coefficient among these neighbors were estimated using spatial autoregressive models. in addition, the rol...

Vector autoregressive model is a very popular tool in multiple time series analysis. Its parameters are usually estimated by the least squares procedure which is very sensitive to the presence of errors in data, e.g. outliers. If outliers were present, the estimation results would become unreliable. Therefore in the presented paper we will propose a new procedure for estimating multivariate reg...

A renormalization group analysis is applied to autoregressive processes with an infinite series of coefficients. A simple fixed point is given by a random walk, and a second class is found that is proportional to the high order coefficients of fractional autoregressive integrated moving average (ARIMA) processes. The approach might be useful to detect nonstationarity in autoregressive processes.

Bu çalışmada, Türkiye'de faaliyette bulunan hayat sigortacılığı yapan 10 sigorta şirketi ile dışı sigortacılık ele alınarak, bunların teknik karlılığı üzerine etki eden faktörler Panel Autoregressive Distributed Lag yöntemi analiz edilmiştir. Analizde 2005-2019 yılları arası 15 yıllık veri kullanılmıştır. Hayat ve branşlarda faaliyet gösteren şirketlerinin durumu ayrı Her bir grubu için toplam ...

T his paper investigates the asymmetric behavior of inflation. We use logistic smooth transition autoregressive (LSTAR) model to characterize the regime-switching behavior of Iran’s monthly inflation during the period May 1990 to December 2013. We find that there is a triple relationship between the inflation level, its fluctuations and persistence. The findings imply that the behavi...

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. We argue that noncausal autoregressive models are especially well suited for modeling expectations. Unlike conventional causal autoregressive models, they explicitly show how the considered economic variable is a¤ected by expectations and how expectations are formed. ...

We present a formulation of the autoregressive HMM for speech synthesis and compare it to the standard HMM synthesis framework and the trajectory HMM. We give details of how to do efficient parameter estimation and synthesis with the autoregressive HMM and discuss consequences of the autoregressive HMM model. There are substantial similarities between the three models, which we explore. The adv...