Monetary policy uncertainty spillovers in time and frequency domains

We use the recently created monthly Interest Rate Uncertainty measure, to investigate monetary policy uncertainty across the US, Germany, France, Italy, Spain, UK, Japan, Canada, and Sweden in both the time and frequency domains. We find that the largest spillover indices are from innovations in the country itself; however, there are some instances where spillover indices between countries are large. These relationships change over time and we observe large variances in pairwise spillovers during the global financial crisis. We find that most of the volatility is confined to the crisis period. Policy makers should consider accounting for the spillovers from the US, Germany, France and Spain, as we found that they are the most consistent net transmitters of monetary policy uncertainty

The stability of money demand in the long-run: Italy 1861–2011

Money demand stability is a crucial issue for monetary policy efficacy, and it is particularly endangered when substantial changes occur in the monetary system. By implementing the ARDL technique, this study intends to estimate the impact of money demand determinants in Italy over a long period (1861–2011) and to investigate the stability of the estimated relations. We show that instability cannot be excluded when a standard money demand function is estimated, irrespectively of the use of M1 or M2. Then, we argue that the reason for possible instability resides in the omission of relevant variables, as we show that a fully stable demand for narrow money (M1) can be obtained from an augmented money demand function involving real exchange rate and its volatility as additional explanatory variables. These results also allow us to argue that narrower monetary aggregates should be employed in order to obtain a stable estimated relation

Spatial panel models and common factors

This chapter provides a survey of the existing literature on spatial panel data models. Both static, dynamic, and dynamic models with common factors will be considered. Common factors are modeled by time-period fixed effects, cross-sectional averages, or principal components. It is demonstrated that spatial econometric models that include lags of the dependent variable and of the independent variables in both space and time provide a useful tool to quantify the magnitude of direct and indirect effects, both in the short term and long term. Direct effects can be used to test the hypothesis as to whether a particular variable has a significant effect on the own dependent variable, and indirect effects to test the hypothesis whether spatial spillovers affect the dependent variable of other units. To illustrate these models, their effects estimates, and the impact of the type of common factors, a demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period 1963 to 1992.<br/

The Russian Economy and the Oil Price: A Co-integrated VAR Approach

Energy, Russia, Q4,

The spillover effects of public capital on the Turkish private manufacturing industries in the geographical regions

C32, L60, R00,