Arbeitspapier
Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels
We propose a new unified approach to identifying and estimating spatio-temporal dependence structures in large panels. The model accommodates global cross-sectional dependence due to global dynamic factors as well as local cross-sectional dependence, which may arise from local network structures. Model selection, filtering of the dynamic factors, and estimation are carried out iteratively using a new algorithm that combines the Expectation-Maximization algorithm with coordinate descent and gradient descent, allowing us to efficiently maximize an l1- and l2-penalized state space likelihood function. A Monte Carlo simulation study illustrates the good performance of the algorithm in terms of determining the presence and magnitude of global and/or local cross-sectional dependence. In an empirical application, we investigate monthly US interest rate data on 15 maturities over almost 40 years. We find that besides a changing number of global dynamic factors, there is heterogeneous local dependence among neighboring maturities. Taking this heterogeneity into account substantially improves out-of-sample forecasting performance.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. TI 2021-008/III
- Klassifikation
-
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
- Thema
-
high-dimensional factor model
Lasso
spatial error model
yield curve
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Koopman, Siem Jan
Schaumburg, Julia
Wiersma, Quint
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Koopman, Siem Jan
- Schaumburg, Julia
- Wiersma, Quint
- Tinbergen Institute
Entstanden
- 2021
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