Adaptive estimation in time-series models

Adaptive estimation in time series regression models

This work develops adaptive estimators for a linear regression model with serially correlated errors. We show that these results continue to hold when the order of the ARMA process characterizing the errors is unknown. The finite sample results are promising, indicating that substantial efficiency gains may be possible for samples as small as 50 observations. We use these estimators to investig...

Evaluation of SARIMA time series models in monthly streamflow estimation in Idanak hydrometry station

prediction of hydrological variables is a highly effective tool in water resource management. One of the important tools for modeling hydrological processes is the use of time series modeling and analysis. River series production series can be used by time series models in various studies such as drought, flood, reservoir systems design and many other purposes For this purpose, monthly flow dat...

Variance Estimation in Time Series Regression Models

Estimation Procedures for Structural Time Series Models

A univariate structural time series model based on the traditional decomposition into trend, seasonal and irregular components is defined. A number of methods of computing maximum likelihood estimators are then considered. These include direct maximization of various time domain likelihood function. The asymptotic properties of the estimators are given and a comparison between the various metho...

Reply to B.M. P6tscher’s comment on ‘Adaptive estimation in time series regression models’

Nearly all of Pbtscher’s comment on my 1992 paper is taken up with three accusations, each of which is wrong and easily dismissed. Potscher also finds and reports two typographical errors. As well, the reviewer of Potscher’s comment finds two typographical errors and one substantive but minor error, which I correct below. For brevity (P) refers to Potscher’s comment and (S) to my original paper...