VAR models with non-Gaussian shocks

We introduce a Bayesian VAR model with non-Gaussian disturbances that are modelled with a finite mixture of normal distributions. Importantly, we allow for regime switching among the different components of the mixture of normals. Our model is highly flexible and can capture distributions that are fat-tailed, skewed and even multimodal. We show that our model can generate large out-of-sample forecast gains relative to standard forecasting models, especially during tranquil periods. Our model forecasts are also competitive with those generated by the conventional VAR model with stochastic volatility

A new approach for detecting shifts in forecast accuracy

Forecasts play a critical role at inflation-targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. However, commonly-used statistical procedures implicitly place a lot of weight on type I errors (or false positives), which results in a relatively low power of the tests to identify forecast breakdowns in small samples. We develop a procedure which aims to capture the policy cost of missing a break. We use data-based rules to find the test size that optimally trades off the costs associated with false positives with those that can result from a break going undetected for too long. In so doing, we also explicitly study forecast errors as a multivariate system. The covariance between forecast errors for different series, although often overlooked in the forecasting literature, not only enables us to consider testing in a multivariate setting, but also increases the test power. As a result, we can tailor our choice of the critical values for each series not only to the in-sample properties of each series, but also to the way in which the series of forecast errors covary

Corporate decision making in the presence of political uncertainty: The case of corporate cash holdings

Using a quarterly panel of U.S. corporations over the period 1985 – 2014 we show that corporate managers respond to political uncertainty and economic policy uncertainty shocks in different ways. We proxy for political uncertainty using the Partisan Conflict Index and employ a prevalent empirical macroeconomic methodology to construct structural shocks that are orthogonal to shocks captured by the Economic Policy Uncertainty Index. Following a political uncertainty shock, corporations increase cash but do not adjust investment. Alternatively, following an economic policy uncertainty shock, firms appear to draw on cash and reduce capital spending to increase R&D spending

Partisan conflict, policy uncertainty and aggregate corporate cash holdings

This paper distinguishes political uncertainty from policy uncertainty shocks and uncovers new empirical facts about how each impacts the aggregate cash holdings of US firms. Our baseline structural vector autoregression model shows that an exogenous one standard deviation shock to political and economic policy uncertainty is followed by 1 and 1.8% increase in aggregate corporate cash-to-total assets after five and eight quarters, respectively. The baseline result also shows that policy uncertainty shocks tend to raise financial market volatility while political uncertainty shocks tend to lower financial market volatility. Moreover, we find evidence that political uncertainty exerts asymmetric effects on aggregate corporate cash holdings, with a shock tending to raise cash holdings under normal financial conditions and lower cash holdings under tight financial conditions. Our main results are robust against a wide range of shock identification schemes as well as against parametric and non-parametric model estimations