We use TVP models and real-time data to describe the evolution of the leading properties of the yield spread for output growth in five European economies and in the US over the last decades and until the third quarter of 2010. We evaluate the predictive performance of benchmark term-structure...

We develop a small-scale dynamic factor model for the Swiss economy allowing for non-linearities by means of a two-state Markov-chain. The selection of an appropriate set of indicators utilizes a combinatorial algorithm. The model's forecasting performance is as good as that of peers with richer...

This study aims to construct a new monthly leading indicator for Tunisian economic activity and to forecast Tunisian quarterly real GDP (RGDP) using several mixed-frequency models. These include a mixed dynamic factor model, unrestricted mixed-data sampling (UMIDAS), and a threepass regression...

The aim of the paper is to compare the forecasting performance of a class of state-dependent autoregressive (SDAR) models for univariate time series with two alternative families of nonlinear models, such as the SETAR and the GARCH models. The study is conducted on US GDP growth rate using...

We evaluate the forecasting performance of six different models for short-term forecasting of Macedonian GDP: 1) ARIMA model; 2) AR model estimated by the Kalman filter; 3) model that explains Macedonian GDP as a function of the foreign demand; 4) small structural model that links GDP components...

In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in...

This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. In particular, the mixed data sampling (MIDAS) regression model is employed to incorporate both quarterly and daily frequencies while remaining parsimonious....

We develop a small-scale dynamic factor model for the Swiss economy based on an appropriately selected set of indicators. The resulting business cycle factor is in striking accordance with historical Swiss business cycle fluctuations. Our proposed model demonstrates a remarkable performance in...

Having a correct assessment of current business cycle conditions is one of the major challenges for monetary policy conduct. Given that GDP figures are available with a significant delay central banks are increasingly using Nowcasting as a useful tool for having an immediate perception of...

We propose a simple modification of Hamilton’s (2018) time series filter that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8 quarter ahead forecast errors of a simple autoregression of real GDP. While this approach yields a cyclical...