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on Econometric Time Series |
| By: | Harvey, A.; Simons, J. |
| Abstract: | Threshold models are set up so that there is a switch between regimes for the parameters of an unobserved components model. When Gaussianity is assumed, the model is handled by the Kalman filter. The switching depends on a component crossing a boundary, and, because the component is not observed directly, the error in its estimation leads naturally to a smooth transition mechanism. A prominent example motivating thresholds is that of a cyclical time series characterized by a downturn that is more, or less, rapid than the upturn. The situation is illustrated by fitting a model with three potentially asymmetric cycles, each with its own threshold, to observations on ice volume in Antarctica since 799, 000 BCE. The model is able to produce multi-step forecasts with associated prediction intervals. A second example shows how a hidden threshold model is able to deal with the asymmetric cycle in monthly US unemployment. |
| Keywords: | Conditionally Gaussian state space model, Kalman filter, nonlinear time series model, regimes, smooth transition autoregressive model, unobserved components |
| JEL: | C22 |
| Date: | 2024–08–21 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2448 |
| By: | Meyer-Gohde, Alexander |
| Abstract: | I provide a solution method in the frequency domain for multivariate linear rational expectations models. The method works with the generalized Schur decomposition, providing a numerical implementation of the underlying analytic function solution methods suitable for standard DSGE estimation and analysis procedures. This approach generalizes the time-domain restriction of autoregressive-moving average exogenous driving forces to arbitrary covariance stationary processes. Applied to the standard New Keynesian model, I find that a Bayesian analysis favors a single parameter log harmonic function of the lag operator over the usual AR(1) assumption as it generates humped shaped autocorrelation patterns more consistent with the data. |
| Keywords: | DSGE, solution methods, spectral methods, Bayesian estimation, general exogenous processes |
| JEL: | C32 C62 C63 E17 E47 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:imfswp:302176 |