Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

There is strong evidence of structural changes in macroeconomic time series, and the
forecasting performance is often sensitive to the choice of estimation window size. This …

This article provides guidance on how to evaluate and improve the forecasting ability of
models in the presence of instabilities, which are widespread in economic time series …

The paper establishes a quasi-Bayesian local likelihood (QBLL) estimation methodology for
a multivariate model with time varying parameters. The asymptotic validity of the resulting …

Spatial and/or temporal biases in biodiversity data can directly influence the utility,
comparability, and reliability of ecological and evolutionary studies. While the effects of …

We improve the performance of stock return forecasts using predictive regressions with
ordinary least squares (OLS) estimates weighted by a class of time-dependent functions …

This paper provides clear cut evidence that economic recession and distressed financial
conditions, as well as political instability constitute the key factors for mortgage default …

High dimensional predictive regressions are useful in wide range of applications. However,
the theory is mainly developed assuming that the model is stationary with time invariant …

Professional forecasters failed to anticipate the sharp fall in inflation in the euro area in 2013–
2014. We investigate whether this forecasting failure can be partly attributed to a break in the …

In this paper we introduce a nonparametric estimation method for a large Vector
Autoregression (VAR) with time‐varying parameters. The estimators and their asymptotic …