Arbeitspapier
A flexible approach to parametric inference in nonlinear time series models
Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible parametric model that accommodates virtually any of these specifications—and does so in a simple way that allows for straightforward Bayesian inference. The basic idea underlying our model is that it adds two concepts to a standard state space framework. These ideas are ordering and distance. By ordering the data in different ways, we can accommodate a wide range of nonlinear time series models. By allowing the state equation variances to depend on the distance between observations, the parameters can evolve in a wide variety of ways, allowing for models that exhibit abrupt change as well as those that permit a gradual evolution of parameters. We show how our model will (approximately) nest almost every popular model in the regime-switching and structural break literatures. Bayesian econometric methods for inference in this model are developed. Because we stay within a state space framework, these methods are relatively straightforward and draw on the existing literature. We use artificial data to show the advantages of our approach and then provide two empirical illustrations involving the modeling of real GDP growth.
- Language
-
Englisch
- Bibliographic citation
-
Series: Staff Report ; No. 285
- Classification
-
Wirtschaft
Bayesian Analysis: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
General Aggregative Models: Forecasting and Simulation: Models and Applications
- Subject
-
Bayesian, structural break, threshold autoregression, regime switching, state space model
Zeitreihenanalyse
Bayes-Statistik
Strukturbruch
Autokorrelation
Zustandsraummodell
Theorie
- Event
-
Geistige Schöpfung
- (who)
-
Koop, Gary
Potter, Simon
- Event
-
Veröffentlichung
- (who)
-
Federal Reserve Bank of New York
- (where)
-
New York, NY
- (when)
-
2007
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Koop, Gary
- Potter, Simon
- Federal Reserve Bank of New York
Time of origin
- 2007
Other Objects (12)
Nonlinear time series : nonparametric and parametric methods
Nonlinear time series : nonparametric and parametric methods
Parametric inference for index functionals
Non-linear parametric optimization
Parametric and non-parametric gradient matching for network inference: a comparison
A topological nonlinear parametric amplifier
Multiscale inference and long‐run variance estimation in non‐parametric regression with time series errors
Parametric statistical inference : basic theory and modern approaches
Parametric and nonparametric inference from record breaking data
Nonlinear Long-Range Correlated Stochastic Models of Temperature Time Series: Inference and Prediction
Parametric Stability Score for Local Solutions of Constrained Parametric Nonlinear Programs
Graph-based causal inference using parametric structural equation models
Nonlinear time series : nonparametric and parametric methods
Nonlinear time series : nonparametric and parametric methods
Parametric inference for index functionals
Non-linear parametric optimization
Parametric and non-parametric gradient matching for network inference: a comparison
A topological nonlinear parametric amplifier
Multiscale inference and long‐run variance estimation in non‐parametric regression with time series errors
Parametric statistical inference : basic theory and modern approaches
Parametric and nonparametric inference from record breaking data
Nonlinear Long-Range Correlated Stochastic Models of Temperature Time Series: Inference and Prediction
Parametric Stability Score for Local Solutions of Constrained Parametric Nonlinear Programs
Graph-based causal inference using parametric structural equation models
Nonlinear time series : nonparametric and parametric methods
Nonlinear time series : nonparametric and parametric methods
Parametric inference for index functionals
Non-linear parametric optimization
Parametric and non-parametric gradient matching for network inference: a comparison
A topological nonlinear parametric amplifier
Multiscale inference and long‐run variance estimation in non‐parametric regression with time series errors
Parametric statistical inference : basic theory and modern approaches
Parametric and nonparametric inference from record breaking data
Nonlinear Long-Range Correlated Stochastic Models of Temperature Time Series: Inference and Prediction
Parametric Stability Score for Local Solutions of Constrained Parametric Nonlinear Programs