Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis

Abstract

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.

Graphical Methods, Inductive Causal Inference, and Econometrics: A Literature Review

“…Finally a macroeconomic example is found in Moneta (2008). He provides a modification to PC algorithm to study real business cycles with U.S. data on nominal interest rates, real investment, real income, real consumption, real money balances, and price inflation.…”

“…Swanson and Granger 1997;Bessler and Akleman 1998;Demiralp and Hoover 2003;Perez and Siegler 2006;Moneta and Spirtes 2006;Moneta 2008). However, clear overviews of their fundamental logics are not provided or are not easily accessible for economists.…”

The Janus-Faced Nature of Debt: Results from a Data-Driven Cointegrated SVAR Approach

“…Following Lutkepohl (1991), the impulse response analysis in cointegrated systems can be conducted in the same way as for stationary systems, hence no particular issue in the estimation of IRF arises. In particular, to estimate 4 This identi cation strategy builds on the previous works by by Swanson and Granger (1997); Bessler and Lee (2002); Demiralp and Hoover (2003); Moneta (2008). Recently also Bayesian strategies have been used to tackle the same issue (see Ahelegbey et al, 2016).…”

A Method for Agent-Based Models Validation

“…Unfortunately these identification strategies are grounded on some level of theoretical apriorism which does not completely solves the critique put forward by Sims (1980). A relatively recent approach for solving the identification issue of a SVAR model in a more agnostic and data-driven fashion, allowing to avoid as much as possible subjective choices and theory driven considerations, has been put forward by Swanson andGranger (1997), Bessler andLee (2002), Demiralp and Hoover (2003), Moneta (2008) and Moneta et al (2011) and is based on graphical causal models (see Pearl, 2000;Spirtes et al, 2000).…”

“…In many statistical packages the default option is to test zero partial correlation through the Fisherz-transformation, as proposed by (Spirtes et al, 2000). An alternative option, suited for the SVAR framework, is to test zero partial correlations among the VAR residuals through a Wald test that exploits the asymptotic normality of the covariance matrix of the maximum-likelihood estimated VAR residuals (for details see Moneta, 2008). If Gaussianity is rejected or one is not willing to make distributional assumptions, one way to proceed is to rely on nonparametric tests of conditional independence, which, however, present the well-known problem of dimensionality (cfr.…”