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 behavior of infla...

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed whe...

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to Decemb...

Using monthly data from 2005 to 2019, we employ a dynamic heterogeneous cross-sectionally autoregressive distributed lag (CS-ARDL) model examine the impact of higher regulatory capital requirements on interest rate pass-through (IRPT) bank lending and deposit rates in 22 Sub-Saharan Africa (SSA) countries. Two key findings emerge investigation: (i) average IRPT SSA is incomplete long run, (ii) ...

In this paper we examine the forecast accuracy of four univariate time series models for 47 macroeconomic variables of the G7 economies. The models considered are the linear autoregressive model, the smooth transition autoregressive model, and two neural network models. The two neural network models are different because they are specified using two different techniques. Forecast accuracy is as...

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final...

We report preliminary results of an eeort to use variants of the Hidden Markov Models developed by speech researchers to characterize persistence and recurrence of atmospheric circulation patterns in a 36 year record of Northern Hemisphere 700-mb geopotential heights. Using a cross validation scheme, we t autoregressive hidden Markov models (ARHMMs) with a range of complexities , varying the au...

Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of both models. Our contribution is to train such hybrid models using an auxiliary...

We consider model identification for infinite variance autoregressive time series processes. It is shown that a consistent estimate of autoregressive model order can be obtained by minimizing Akaike’s information criterion, and we use all-pass models to identify noncausal autoregressive processes and estimate the order of noncausality (the number of roots of the autoregressive polynomial inside...