Serially correlated measurement errors in time series regression: The potential of instrumental variable estimators

Arbeitspapier

The measurement error problem in linear time series regression, with focus on the impact of error memory, modeled as nite-order MA processes, is considered. Three prototype models, two bivariate and one univariate ARMA, and ways of handling the problem by using instrumental variables (IVs) are discussed as examples. One has a bivariate regression equation that is static, although with dynamics, entering via the memory of its latent variables. The examples illustrate how 'structural dynamics' interacting with measurement error memory create bias in Ordinary Least Squares (OLS) and illustrate the potential of IV estimation procedures. Supplementary Monte Carlo simulations are provided for two of the example models.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Memorandum ; No. 28/2014

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Multiple or Simultaneous Equation Models: Instrumental Variables (IV) Estimation ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods
• Subject: Errors in variables ; ARMA ; Error memory ; Simultaneity bias ; Attenuation ; Monte Carlo

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Biørn, Erik
• Event: Veröffentlichung
• (who): University of Oslo, Department of Economics
• (where): Oslo
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/119542
• Last update: 10.03.2025, 11:44 AM CET