Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis
By Alessio Moneta. 2008
55 citations recorded by the source corpus.
This paper assesses the empirical plausibility of the real business cycle view that shocks to real variables are the dominant sources of economic fluctuations and that monetary policy shocks play an insignificant role in determining the behavior of real variables. I reconsider the vector autoregressive model of King et al. (Am Econ Rev 81:819–840, 1991), but propose an alternative identification method, based on graphical causal models. This method selects the contemporaneous causal structure using the information incorporated in the partial correlations among the residuals. The residuals orthogonalization which follows and the study of the impulse response functions confirm the results of King et al. (Am Econ Rev 81:819–840, 1991): permanent productivity shocks are not the dominant sources of aggregate fluctuations in US economy.
Subfields: Economics and Econometrics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
Citations in the corpus
- Causal Discovery from Temporally Aggregated Time Series (2017, methodology): “, 2016) and the macroeconomic data used in (Moneta, 2008).”
- Causal Inference by Independent Component Analysis: Theory and Applications* (2013, unknown): “…structures belonging to this class, so the outcome of the search procedure is not a unique B. Bessler and Lee (2002), Demiralp and Hoover (2003), and Moneta (2008) use partial correlation as a measure of conditional dependence, based on the assumption that error terms are normally distributed.”