Nonparametric transfer function models |
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Authors: | Jun M Liu Rong Chen Qiwei Yao |
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Institution: | 1. Georgia Southern University, United States;2. Peking University, China;3. Rutgers University, United States;4. London School of Economics, United Kingdom |
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Abstract: | In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example. |
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Keywords: | C14 C22 |
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