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Title: Estimation of semiparametric econometric time-series models with non-linear or heteroscedastic disturbances
Author: Javier Hidalgo Moreno, Francisco
Awarding Body: London School of Economics and Political Science (University of London)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 1990
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This thesis proposes and justifies parameter estimates in two semiparametric models for economic time series. In both models the parametric component consists of a linear regression model. The nonparametric aspect consists of relevant features of the distribution function of the disturbances. In the first model the disturbances follow a possibly non-linear autoregressive model, with autoregression function of unknown form. In the second model the disturbances are both linearly serially correlated and heteroscedastic, the serial correlation and heteroscedasticity being of unknown form. For both models estimates of the regression coefficients of generalized least squares type are proposed, and shown to have the same limiting distribution as estimates based on correct parameterization of the relevant features of the disturbances. Monte-Carlo simulation evidence of the finite sample performance of both estimates is reported.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available