this paper investigates the relationship between inflation and growth uncertainty in iran for the period of 1988-2008 by using quarterly data. we employ generalized autoregressive conditional heteroscedasticity in mean (garch-m) model to estimate time-varying conditional residual variance of growth, as a standard measures of growth uncertainty. the empirical evidence shows that growth uncertain...

In this Supplementary Appendix we present additional new material related to the main paper “Generalized Autoregressive Score Models with Applications”. We refer to the model as the GAS model. For reference purposes, we first give a short review of the relevant equations for the general GAS model. Appendix A presents more existing models that can be represented as special cases of GAS models. A...

In this paper, the problem of generalised minimum variance control of linear time-varying systems is studied. A time-varying optimal controller is developed for a standard controlled autoregressive moving average model using a cost function that is the sum of the plant output tracking error variance plus a penalty term of the squared manipulated variable. Copyright © 2005 IFAC

This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a gener...

A multi-input and multi-output optimal controller for stochastic systems with time-varying dynamics is developed. The system to be controlled is described using a multi-input and multi-output time-varying autoregressive moving average model with multiple delays. The controller minimises a generalised minimum variance cost functional that is the sum of quadratic output tracking error variances a...

This article deals with asymmetrical spatial data which can be modeled by a partially linear varying coefficient autoregressive panel model (PLVCSARPM) random effects. We constructed its profile quasi-maximum likelihood estimators (PQMLE). The consistency and asymptotic normality of the were proved under some regular conditions. Monte Carlo simulations implied our have good finite sample perfor...

In this paper, a method to detect unknown signals ina non-stationaryenvironmentis proposed. In addition, due to the sensor, the data are corrupted by an additive measurement stationary zero-mean white noise.Our approach, which can be useful in a wide range of situations such as the analysis of the object passing by, anomaly detection and digital communications, operates in three steps.Firstly, ...