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We assess the implications of price indexation for estimated frequency of price adjustment in sticky price models of business cycles. These models predominantly assume that non-reoptimized prices are indexed to lagged or average inflation.

Policy-makers in the United States over the past 15 to 20 years seem to have been cautious in setting policy: empirical estimates of monetary policy rules such as Taylor's (1993) rule are much less aggressive than those derived from optimizing models.

There appears to be a disconnect between the importance of the zero bound on nominal interest rates in the real-world and predictions from quantitative DSGE models. Recent economic events have reinforced the relevance of the zero bound for monetary policy whereas quantitative models suggest that the zero bound does not constrain (optimal) monetary policy.

In this paper we explore variables that may have an impact on multifactor productivity (MFP) in the long-run using the KLEMS database for Canada. We estimate a dynamic heterogeneous panel error-correction model of twelve 2-digit level industries.

Several empirical studies suggest that exchange rate pass-through has declined in recent years in industrialized countries.

Phillips curves are generally estimated under the assumption of linearity and parameter constancy. Linear models of inflation, however, have recently been criticized for their poor forecasting performance.

The intertemporal current account approach predicts that the current account of a small open economy is independent of global shocks, and that responses of the current account to country-specific shocks depend on the persistence of the shocks. The author shows that these predictions impose cross-equation restrictions (CERS) on a structural vector autoregression (SVAR).

I use a structural vector autoregression (SVAR) with sign restrictions to provide conditional evidence on the behavior of the US external finance premium (EFP). The results indicate that the excess bond premium, a proxy for the EFP, reacts countercyclically to supply and monetary policy shocks and procyclically to demand shocks.

This article examines what market conditions lead to extreme losses in global financial systems. Using a reverse stress testing approach, it introduces two measures of systemic risk by starting from the tail losses and working backward to identify the events most closely associated with them.

Weak identification is likely to be prevalent in multi-equation macroeconomic models such as in dynamic stochastic general equilibrium setups. Identification difficulties cause the breakdown of standard asymptotic procedures, making inference unreliable.