Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments usually limited, large amounts of observational time series sets are available. Current methods that learn causal often assume linear relationships. Hence, they may fail realistic settings contain nonlinear relations between the variables...

In this paper, I examine the effects of money supply, aggregate spending, and aggregate supply shocks on real US stock prices in a structural vector autoregression framework. Overall, the empirical results indicate that each macro shock has important effects on real stock prices. The real stock price impulse responses to the various macro shocks conform to the standard present-value equity valu...

The primary objective of this paper is to revisit DSGE models with a view to appraise their statistical adequacy and propose ways to ameliorate their empirical pertinence. This paper brings out several weaknesses of the traditional DSGE modeling, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance and potentially mi...

How do aggregate quantities at the business cycle frequency respond to shocks to the spread between residential mortgage rates and government bonds? Using a structural VAR approach, we find that mortgage spread shocks impact the real economy by both economically and statistically significant magnitudes: a 100 basis point decline in the spread causes a peak increase in consumption, residential i...

This note investigates long-run exclusion in a cointegrated vector autoregressive (VAR) model from the viewpoint of …nite-sample statistical inference. Monte Carlo experiments show that, in various circumstances, a mis-speci…ed partial VAR model, which is justi…ed by the existence of a long-run excluded variable, can lead to better …nite-sample inference for cointegrating rank than a fully-spec...