Many time series contain measurement (often sampling) error and the problem of assessing the impacts of such errors and accounting for them has been receiving increasing attention of late. This paper provides a survey of this problem with an emphasis on estimating the coefficients of the underlying dynamic model, primarily in the context of fitting linear and nonlinear autoregressive models. An overview is provided of the biases induced by ignoring the measurement error and of methods that have been proposed to correct for it, and remaining inferential challenges are outlined.
|Title of host publication||ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers|
|Editors||Brajendra C. Sutradhar|
|Place of Publication||New York|
|Number of pages||16|
|Publication status||Published - 2013|
|Name||Lecture Notes in Statistics|