Estimation of Dynamic Econometric Models with Errors in Variables [electronic resource] /
by Jaime Terceiro Lomba.
- 1st ed. 1990.
- VIII, 121 p. 1 illus. online resource.
- Lecture Notes in Economics and Mathematical Systems, 339 0075-8442 ; .
- Lecture Notes in Economics and Mathematical Systems, 339 .
1. Introduction -- 2. Formulation of Econometric Models in State-Space -- 2.1. Structural Form, Reduced Form and State-Space Form -- 2.2. Additional Remarks -- 3. Formulation of Econometric Models with Measurement Errors -- 3.1. Model of the Exogenous Variables -- 3.2. State-Space Formulation -- 4. Estimation of Econometric Models with Measurement Errors -- 4.1. Evaluation of the Likelihood Function -- 4.2. Maximization of the Likelihood Function -- 4.3. Initial Conditions -- 4.4. Gradient Methods and Identification -- 4.5. Asymptotic Properties -- 4.6. Numerical Considerations -- 4.7. Model Verification -- 5. Extensions of the Analysis -- 5.1. Missing Observations and Contemporaneous Aggregation -- 5.2. Temporal Aggregation -- 5.3. Correlated Measurement Errors -- 6. Numerical Results -- 7. Conclusions -- Appendices -- A. Kalman Filter and Chandrasekhar Equations -- A.1. Kalman Filter -- A.2. Chandrasekhar Equations -- B. Calculation of the Gradient -- C. Calculation of the Hessian -- D. Calculation of the Information Matrix -- E. Estimation of the Initial Conditions -- F. Solution of the Lyapunov and Riccati Equations -- F.1. Lyapunov Equation -- F.2. Riccati Equation -- G. Fixed-Interval Smoothing Algorithm -- References -- Author Index.
A new procedure for the maximum-likelihood estimation of dynamic econometric models with errors in both endogenous and exogenous variables is presented in this monograph. A complete analytical development of the expressions used in problems of estimation and verification of models in state-space form is presented. The results are useful in relation not only to the problem of errors in variables but also to any other possible econometric application of state-space formulations.