Abstract
This paper derives the best linear unbiased predictor for a one‐way error component model with serial correlation. A transformation derived by Baltagi and Li (1991) is used to show how the forecast can be easily computed from the GLS estimates and residuals. This result is useful for panel data applications which utilize the error component specification and exhibit serial correlation in the remainder disturbance term. Analytical expressions for this predictor are given when the remainder disturbances follow (1) an AR(1) process, (2) an AR(2) process, (3) a special AR(4) process for quarterly data, and (4) an MA(1) process.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 561-567 |
| Number of pages | 7 |
| Journal | Journal of Forecasting |
| Volume | 11 |
| Issue number | 6 |
| DOIs | |
| State | Published - Sep 1992 |
| Externally published | Yes |
Keywords
- Autoregressive Moving average
- Error components
- Panel data
- Prediction
ASJC Scopus subject areas
- Modeling and Simulation
- Computer Science Applications
- Strategy and Management
- Statistics, Probability and Uncertainty
- Management Science and Operations Research