Numerical issues in threshold autoregressive modelling of time series

This paper analyses the contribution of various numerical approaches to making the estimation of threshold autoregressive time series more efficient. It relies on the computational advantages of QR factorizations and proposes Givens transformations to update these factors for sequential LS problems. By showing that the residual sum of squares is a continuous rational function over threshold intervals it develops a new fitting method based on rational interpolation and the standard necessary optimality condition. Taking as benchmark a simple grid search, the paper illustrates via Monte Carlo simulations the efficiency gains of the proposed tools

A Principal Components Approach to Cross-Section Dependence in Panels

The use of GLS to deal with cross-section dependence in panels is not feasible where N is large relative to T since the disturbance covariance matrix is rank deficient. Neither is it the appropriate response if the dependence results from omitted global variables or common shocks correlated with the included regressors. These can be proxied by the principal components of the residuals from a baseline regression. It is shown that the OLS estimates from a regression augmented by these principal components are unbiased and consistent using sequential limits for large T, large N. Simulations show that this leads to a substantial reduction in bias even for relatively small T and N panels. An empirical application indicates that the impact of cross section dependence seems to strengthen the case for long run PPP.Factor analysis; global shocks; omittted variable bias

Rethinking the forward premium puzzle in a non-linear framework

The forward premium puzzle needs to be reformulated since extant studies address the negative slopes associated with the long dollar swings of the 1980s. By contrast the insignificant coefficients from recent data spans can be explained by an unbalanced regression problem caused by asymmetries in spot returns. These stem from market frictions such as transaction costs and are associated with overshooting of spot rates. Monte Carlo experiments show that asymmetries and overshooting effects produce widely dispersed and statistically insignificant slope coefficients whose small sample mean is close to zero

Is news related to GDP growth a risk factor for commodity futures returns?

Expectations about future economic activity should theoretically affect the demand for inventory holdings and therefore commodity spot and futures prices. Consistent with these predictions, we find that news related to future GDP growth is a significant factor that is priced in the cross-section of commodity futures sorted by percentage net basis. The latter is highly correlated with inventories. In particular, it establishes that commodity futures with high inventory levels provide a hedge against risk associated with future GDP growth so that investors are willing to accept lower return. By contrast, those commodity futures with low inventory levels are inversely related to the GDP-related factor so that investors require a higher return. Such results suggest that commodity futures excess returns are a compensation for risk