Circularly Projected Common Factors for Grouped Data
Bibliographic Data
| ID | 19419920 |
|---|---|
| Authors | Mingjing Chen (0000-0003-3008-0745, Chongqing Technology and Business University, corresponding author) |
| Year | 2023 |
| Volume | 41 |
| Issue | 2 |
| Pages | 636-649 |
| Publication date | 2023-04-03 |
| Open Access | No |
| 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 | 10.1080/07350015.2022.2051520 |
| OpenAlex | W4220997514 |
| Language | EN |
| Citations received | 1 |
| References cited | 18 |
To extract the common factors from grouped data, multilevel factor models have been put forward in the literature, and methods based on iterative principal component analysis (PCA) and canonical correlation analysis (CCA) have been proposed for estimation purpose. While iterative PCA requires iteration and is hence time-consuming, CCA can only deal with two groups of data. Herein, we develop two new methods to address these problems. We first extract the factors within groups and then project the estimated group factors into the space spanned by them in a circular manner. We propose two projection processes to estimate the common factors and determine the number of them. The new methods do not require iteration and are thus computationally efficient. They can estimate the common factors for multiple groups of data in a uniform way, regardless of whether the number of groups is large or small. They not only overcome the drawbacks of CCA but also nest the CCA method as a special case. Finally, we theoretically and numerically study the consistency properties of these new methods and apply them to studying international business cycles and the comovements of retail prices
Algorithm · Canonical correlation · Data mining · Factor analysis · Iterative method · Machine learning · Principal component analysis · Complex Network Analysis Techniques · Complex Systems and Time Series Analysis · Computer Science · Mathematics · Sensory Analysis and Statistical Methods · Artificial Intelligence
International Business Cycles
Determining the Number of Factors in Approximate Factor Models
Inferential Theory for Factor Models of Large Dimensions
Comovements in the real activity of developed and emerging economies
Shrinkage Estimation of Factor Models With Global and Group-Specific Factors
Dynamic Hierarchical Factor Models
| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |