Tests of Equal Forecasting Accuracy for Nested Models with Estimated CCE Factors
Bibliographic Data
| ID | 19418655 |
|---|---|
| Authors | Ovidijus Stauskas (0000-0002-5326-8794, Lund University, Melbourne, Sweden), Joakim Westerlund (0000-0002-2461-351X, Lund University, Deakin University, Lund, Sweden, corresponding author) |
| Year | 2022 |
| Volume | 40 |
| Issue | 4 |
| Pages | 1745-1758 |
| Publication date | 2022-10-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2021.1970576 |
| OpenAlex | W3193332322 |
| Language | EN |
| Citations received | 1 |
| References cited | 46 |
In this article, we propose new tests of equal predictive ability between nested models when factor-augmented regressions are used to forecast. In contrast to the previous literature, the unknown factors are not estimated by principal components but by the common correlated effects (CCE) approach, which employs cross-sectional averages of blocks of variables. This makes for easy interpretation of the estimated factors, and the resulting tests are easy to implement and they account for the block structure of the data. Assuming that the number of averages is larger than the true number of factors, we establish the limiting distributions of the new tests as the number of time periods and the number of variables within each block jointly go to infinity. The main finding is that the limiting distributions do not depend on the number of factors but only on the number of averages, which is known. The important practical implication of this finding is that one does not need to estimate the number of factors consistently in order to apply our tests
Combinatorics · Data mining · Econometrics · Factor analysis · Infinity · Limiting · Nested set model · Principal component analysis · Statistics · Advanced Statistical Methods and Models · Computer Science · Mathematics · Monetary Policy and Economic Impact · Spatial and Panel Data Analysis · Artificial Intelligence
Forecasting inflation
Tests of Conditional Predictive Ability
Large panels with common factors and spatial correlation
Determining the Number of Factors in Approximate Factor Models
Grouped Patterns of Heterogeneity in Panel Data
Forecasting Using Principal Components From a Large Number of Predictors
Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions
Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
Fred-Md
Macroeconomic Forecasting Using Diffusion Indexes
Determining the Number of Primitive Shocks in Factor Models
Dynamic Hierarchical Factor Models
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |