In this paper, I examine the forecasting performance of a Bayesian Vector Autoregression (BVAR) model with steady-state prior and compare the accuracy of the forecasts against the forecasts of QPM model and official NBU forecasts over the period 2016q1-2020q1. My findings suggest that inflation...

We develop a vector autoregressive model with time variation in the mean and the variance. The unobserved time-varying mean is assumed to follow a random walk and we also link it to long-term Consensus forecasts, similar in spirit to so called democratic priors. The changes in variance are...

The object of this paper is to nowcast, forecast and track changes in Tunisian economic activity during normal and crisis times. The main target variable is quarterly real GDP (RGDP) and we have collected a large and varied set of monthly indicators as predictors. We use several mixed frequency...

Density forecasts have become quite important in economics and finance. For example, such forecasts play a central role in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density forecast evaluation framework as a simple alternative to other...

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly...

The forecast performance of the empirical ESTAR model of Taylor, Peel and Sarno (2001) is examined for 4 bilateral real exchange rate series over an out-of-sample eval-uation period of nearly 12 years. Point as well as density forecasts are constructed, considering forecast horizons of 1 to 22...

In this paper we suggest an approach to comparison of models' forecasting performance in unstable environments. Our approach is based on combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) suggested earlier in Welch and Goyal (2008) and the Bayesian change point...

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

Multivariate distributional forecasts have become widespread in recent years. To assess the quality of such forecasts, suitable evaluation methods are needed. In the univariate case, calibration tests based on the probability integral transform (PIT) are routinely used. However, multivariate...

This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to different sampling frequencies and publication delays. Two model classes...