Estimation in autoregressive model with measurement error

Estimation in autoregressive model with measurement error

Consider an autoregressive model with measurement error: we observe Zi = Xi + εi, where Xi is a stationary solution of the autoregressive equation Xi = fθ0(Xi−1) + ξi. The regression function fθ0 is known up to a finite dimensional parameter θ. The distributions of X0 and ξ1 are unknown whereas the distribution of ε0 is completely known. We want to estimate the parameter θ by using the observat...

Experiments with autoregressive error estimation

Correcting for Omitted-Variable and Measurement-Error Bias in Autoregressive Model Estimation with Panel Data

The parameter estimates based on an econometric equation are biased and can also be inconsistent when relevant regressors are omitted from the equation or when included regressors are measured with error. This problem gets complicated when the ‘true’ functional form of the equation is unknown. Here, we demonstrate how auxiliary variables, called concomitants, can be used to remove omitted-varia...

Measurement Error in Linear Autoregressive Models

Time series data are often subject to measurement error, usually the result of needing to estimate the variable of interest. While it is often reasonable to assume the measurement error is additive, that is, the estimator is conditionally unbiased for the missing true value, the measurement error variances often vary as a result of changes in the population/process over time and/or changes in s...

Improved GMM estimation of the spatial autoregressive error model

We suggest an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are different from observable regression residuals.