Arbeitspapier
Time-varying state correlations in state space models and their estimation via indirect inference
Statistics Netherlands uses a state space model to estimate the Dutch unemployment by using monthly series about the labour force surveys (LFS). More accurate estimates of this variable can be obtained by including auxiliary information in the model, such as the univariate administrative series of claimant counts. Legislative changes and economic crises may affect the relation between survey-based and auxiliary series. This time-changing relationship is captured by a time-varying correlation parameter in the covariance matrix of the transition equation's error terms. We treat the latter parameter as a state variable, which makes the state space model become nonlinear and therefore its estimation by Kalman filtering and maximum likelihood infeasible. We therefore propose an indirect inference approach to estimate the static parameters of the model, which employs cubic splines for the auxiliary model, and a bootstrap filter method to estimate the time-varying correlation together with the other state variables of the model. We conduct a Monte Carlo simulation study that shows that our proposed methodology is able to correctly estimate both the time-constant parameters and the state vector of the model. Empirically we find that the financial crisis of 2008 triggered a deeper and more prolonged deviation between the survey-based and the claimant counts series, than a legislative change in 2015. Promptly tackling such changes, which our proposed method does, results in more realistic real-time unemployment estimates.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. TI 2021-020/III
- Klassifikation
-
Wirtschaft
Unemployment: Models, Duration, Incidence, and Job Search
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Thema
-
bootstrap filter
cubic splines
indirect inference
nonlinear state space
time-varying parameter
unemployment
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Schiavoni, Caterina
Koopman, Siem Jan
Palm, Franz
Smeekes, Stephan
van den Brakel, Jan
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Schiavoni, Caterina
- Koopman, Siem Jan
- Palm, Franz
- Smeekes, Stephan
- van den Brakel, Jan
- Tinbergen Institute
Entstanden
- 2021
Ähnliche Objekte (12)
A Time-Varying Parameter Model for Local Explosions
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
A General Framework for Observation Driven Time-Varying Parameter Models
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
On Importance Sampling for State Space Models
Fast Efficient Importance Sampling by State Space Methods
In-Sample Bounds for Time-Varying Parameters of Observation Driven Models
Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters
The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model
In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models
A Time-Varying Parameter Model for Local Explosions
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
A General Framework for Observation Driven Time-Varying Parameter Models
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
On Importance Sampling for State Space Models
Fast Efficient Importance Sampling by State Space Methods
In-Sample Bounds for Time-Varying Parameters of Observation Driven Models
Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters
The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model
In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation Driven Models
A Time-Varying Parameter Model for Local Explosions
Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production
Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series
A General Framework for Observation Driven Time-Varying Parameter Models
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
On Importance Sampling for State Space Models
Fast Efficient Importance Sampling by State Space Methods
In-Sample Bounds for Time-Varying Parameters of Observation Driven Models
Analyzing the Term Structure of Interest Rates using the Dynamic Nelson-Siegel Model with Time-Varying Parameters
The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model