In this paper, the use of weighted pairwise likelihood instead of the full likelihood in estimating the parameters of the multivariate AR(1) is investigated. A closed formula for typical elements of the Godambe information (sandwich information) is presented. Some efficiency calculations are also given to discuss the feasibility and computational advantages of the weighted pairwise likelihood a...

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...

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. ...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

This paper proposes a new estimation procedure for the first-order spatial autoregressive (SAR) model, where disturbance term also follows autoregression and its innovations may be heteroscedastic. The is based on principle of indirect inference that matches ordinary least squares estimator two SAR coefficients (one in outcome equation other equation) with approximate analytical expectation. re...