Causal Inference by Independent Component Analysis with Applications to Micro- and Macroeconomic Data
AbstractStructural 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).
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Bibliographic InfoPaper provided by Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics in its series Jena Economic Research Papers with number 2010-031.
Date of creation: 11 May 2010
Date of revision:
Causality; Structural VAR; Independent Components Analysis; Non-Gaussianity; Firm Growth; Monetary Policy;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
- E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
- L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm
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