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The Estimation of Higher-Order Continuous Time Autoregressive Models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Harvey, Andrew Stock, James H. |
| Copyright Year | 1985 |
| Abstract | A method is presented for computing maximum likelihood, or Gaussian, estimators of the structural parameters in a continuous time system of higherorder stochastic differential equations. It is argued that it is computationally efficient in the standard case of exact observations made at equally spaced intervals. Furthermore it can be applied in situations where the observations are at unequally spaced intervals, some observations are missing and/or the endogenous variables are subject to measurement error. The method is based on a state space representation and the use of the Kalman–Bucy filter. It is shown how the Kalman-Bucy filter can be modified to deal with flows as well as stocks. |
| Starting Page | 97 |
| Ending Page | 117 |
| Page Count | 21 |
| File Format | PDF HTM / HTML |
| DOI | 10.1017/s0266466600011026 |
| Volume Number | 1 |
| Alternate Webpage(s) | http://scholar.harvard.edu/files/stock/files/the_estimation_of_higher_order_continuous_time_autoregressive_models.pdf |
| Alternate Webpage(s) | https://doi.org/10.1017/s0266466600011026 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
