Andreas Pick
Biographic Data
| ID | 8920865 |
|---|---|
| NAME | Andreas Pick |
| GIVEN NAMES | Andreas |
| FAMILY NAME | Pick |
| SIGNATURE | PICK A |
| AFFILIATIONS | Erasmus University Rotterdam |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2011 |
| LATEST PUBLICATION YEAR | 2018 |
| H-INDEX | 0 |
Optimal Forecasts from Markov Switching Models
We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the constru…
Forecast Combination Across Estimation Windows
In this article we consider combining forecasts generated from the same model but over different estimation windows. We develop theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error (RMSFE) compared with forecasts based on a single estimation w…
No prominent works on this page.
Forecast Combination Across Estimation Windows
In this article we consider combining forecasts generated from the same model but over different estimation windows. We develop theoretical results for random walks with breaks in the drift and volatility and for a linear regression model with a break in the slope parameter. Averaging forecasts over different estimation windows leads to a lower bias and root mean square forecast error (RMSFE) compared with forecasts based on a single estimation w…
Optimal Forecasts from Markov Switching Models
We derive forecasts for Markov switching models that are optimal in the mean square forecast error (MSFE) sense by means of weighting observations. We provide analytic expressions of the weights conditional on the Markov states and conditional on state probabilities. This allows us to study the effect of uncertainty around states on forecasts. It emerges that, even in large samples, forecasting performance increases substantially when the constru…
Econometrics (2 works) · Forecasting Techniques and Applications (2 works) · Mathematics (2 works) · Monetary Policy and Economic Impact (2 works) · Statistics (2 works) · Computer Science (1 work) · Conditional expectation (1 work) · Conditional probability (1 work) · Economics (1 work) · Estimation (1 work)