Given several studies (inputs) of some phenomenon of interest, each input presents an estimate of a key parameter with an associated estimated precision. The random-effects model used in meta-analysis estimates this parameter based on a decomposition of the error term into within-input noise and...

This paper presents one of the inflation forecasting models used by the Magyar Nemzeti Bank in its recent inflation forecasts. The model attempts to integrate all the properties of the former models considered by the author as being advantageous and desirable into a unified framework. Thus, this...

This paper examines the forecasting performance of DSGE models with and without banking intermediation for the US economy. Over the forecast period 2001-2013, the model augmented with a banking sector leads to an improvement of point and density forecasts for inflation and the short term...

PD curve calibration refers to the transformation of a set of rating grade level probabilities of default (PDs) to another average PD level that is determined by a change of the underlying portfolio-wide PD. This paper presents a framework that allows to explore a variety of calibration...

The rough path-dependent volatility (RPDV) model (Parent 2022) effectively captures key empirical features that are characteristic of volatility dynamics, making it a suitable choice for volatility forecasting. However, its complex structure presents challenges when it comes to estimating the...

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting...

We frequently observe that one of the aims of time series analysts is to predict future values of the data. For weakly dependent data, when the model is known up to a finite set of parameters, its statistical properties are well documented and exhaustively examined. However, if the model was...

This paper examines whether the preliminary releases of GDP incorporate efficiently all available information or whether the preliminary estimates contain information that can be useful in predicting forthcoming GDP data revisions. Forecast rationality tests are applied to distinguish between...

Forecasting using factor models based on large data sets have received ample attention due to the models' ability to increase forecast accuracy with respect to a range of key macroeconomic variables in the US and the UK. However, forecasts based on such factor models do not uniformly outperform...