Arbeitspapier
Causal inference by independent component analysis with applications to micro- and macroeconomic data
Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this paper, we present a recently developed method for exploiting non-Gaussianity in the data for estimating such models, with the aim of capturing the causal structure underlying the data, and show how the method can be applied to both microeconomic data (processes of firm growth and firm performance) as well as macroeconomic data (effects of monetary policy).
- Language
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Englisch
- Bibliographic citation
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Series: Jena Economic Research Papers ; No. 2010,031
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Evaluation, Validation, and Selection
Firm Behavior: Theory
Monetary Policy
Business Objectives of the Firm
- Subject
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Causality
Structural VAR
Independent Components Analysis
Non-Gaussianity
Firm Growth
Monetary Policy
Kausalanalyse
VAR-Modell
Strukturgleichungsmodell
Hauptkomponentenanalyse
Theorie
Schätzung
Unternehmenswachstum
Unternehmensentwicklung
Geldpolitik
Wirkungsanalyse
USA
- Event
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Geistige Schöpfung
- (who)
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Moneta, Alessio
Entner, Doris
Hoyer, Patrik
Coad, Alex
- Event
-
Veröffentlichung
- (who)
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Friedrich Schiller University Jena and Max Planck Institute of Economics
- (where)
-
Jena
- (when)
-
2010
- Handle
- Last update
-
28.03.2025, 11:14 AM CET
Data provider
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Object type
- Arbeitspapier
Associated
- Moneta, Alessio
- Entner, Doris
- Hoyer, Patrik
- Coad, Alex
- Friedrich Schiller University Jena and Max Planck Institute of Economics
Time of origin
- 2010
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