Tests for relative predictive accuracy have become a widespread addendum to forecast comparisons. Many empirical research reports conclude that the difference between the entertained forecasting models is 'insignificant'. This paper collects arguments that cast doubt on the usefulness of...

Recent years have seen a growing literature on the environmental Kuznets curve (EKC) that resorts in a large part to cointegration techniques. The EKC literature has failed to acknowledge that such regressions involve unit root nonstationary regressors and their integer powers (e.g. GDP and GDP...

We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention during the first and second waves of the epidemic in the United States. We find three main...

In recent years many empirical studies of environmental Kuznets curves employing unit root and cointegration techniques have been conducted for both time series and panel data. When using such methods several issues arise: the effects of a short time dimension, in a panel context the effects of...

The asymptotic distributions of the recursive out-of-sample forecast accuracy test statistics depend on stochastic integrals of Brownian motion when the models under comparison are nested. This often complicates their implementation in practice because the computation of their asymptotic...

We suggest a theoretical basis for the comparative evaluation of forecasts. Instead of the general assumption that the data is generated from a stochastic model, we classify three stages of prediction experiments: pure non-stochastic prediction of given data, stochastic prediction of given data,...

This paper uses Monte Carlo techniques to assess the loss in terms of forecast accuracy which is incurred when the true DGP exhibits parameter instability which is either overlooked or incorrectly modelled. We find that the loss is considerable when a FCM is estimated instead of the true TVCM,...

For many economic time-series variables that are observed regularly and frequently, for example weekly, the underlying activity is not distributed uniformly across the year. For the aim of predicting annual data, one may consider temporal aggregation into larger subannual units based on an...

We investigate whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test-based procedure, which assigns non-zero weights to candidate models that add information not...

A two-regime self-exciting threshold autoregressive process is estimated for quarterly aggregate GDP of the fifteen countries that compose the European Union, and the forecasts from this nonlinear model are compared, by means of a Monte Carlo simulation, with those from a simple autoregressive...