This paper presents an exchange rate forecasting model which combines the multi-state Markov-switching model with smoothing techniques. The model outperforms a random walk at short horizons and its superior forecastability appears to be robust over different sample spans. Our finding hinges on...

This study investigates the causal relationship between exports, imports and economic growth in India during 1970-71 to 2013-14. Following time series properties, Johansen cointegration test confirms the existence of long-run relationship between variables. Results of multiple Granger causality...

The recent plunge in oil prices has brought into question the generally accepted view that lower oil prices are good for the US and the global economy. In this paper, using a quarterly multi-country econometric model, we first show that a fall in oil prices tends relatively quickly to lower...

We apply a global vector autoregressive (GVAR) model to the analysis of inflation, output growth and global imbalances among a group of 33 countries (26 regions). We account for structural instability by use of country-specific intercept shifts, the timings of which are identified taking into...

This paper investigates the global macroeconomic consequences of falling oil prices due to the oil revolution in the United States, using a Global VAR model estimated for 38 countries/regions over the period 1979Q2 to 2011Q2. Set-identification of the U.S. oil supply shock is achieved through...

This paper investigates the global macroeconomic consequences of falling oil prices due to the oil revolution in the United States, using a Global VAR model estimated for 38 countries/regions over the period 1979Q2 to 2011Q2. Set-identification of the U.S. oil supply shock is achieved through...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, as well as classical and Bayesian quantile regressions) and also different...

A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in...