This paper examines the effect of non-linearities on density forecasting. It focuses on the relationship between credit markets and the rest of the economy. The possible non-linearity of this relationship is captured by a threshold vector autoregressive model estimated on the US data using Bayesian methods. Density forecasts thus account for the uncertainty in all model parameters and possible future regime changes. It is shown that considering nonlinearity can improve the probabilistic assessment of the economic outlook. Moreover, three illustrative examples are discussed to shed some light on the possible practical applicability of density forecasts derived from non-linear models
this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among no...
We consider a method for producing multivariate density forecasts that satisfy moment restrictions i...
A density forecast of the realization of a random variable at some future time is an estimate of the...
Abstract: How can density forecasts help policymaking and economists? Earlier academic research has ...
This paper makes two contribution to the literature on density forecasts. First, we propose a novel ...
We forecast macroeconomic and financial uncertainties of the USA over the period of 1960:Q3 to 2018:...
textabstractWe propose a multivariate combination approach to prediction based on a distributional s...
We compare linear autoregressive (AR) models and self-exciting threshold autoregressive (SETAR) mode...
Linear Vector Autoregression (VAR) models provide a useful starting point for analysing multivariate...
A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks ...
The debate on the forecasting ability of non-linear models has a long history, and the Great Recessi...
We develop nonlinear leading indicator models for GDP growth, with the interest rate spread and grow...
In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among...
We systematically examine the comparative predictive performance of a number of linear and non-linea...
Many modelling issues and policy debates in macroeconomics depend on whether macroeconomic times ser...
this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among no...
We consider a method for producing multivariate density forecasts that satisfy moment restrictions i...
A density forecast of the realization of a random variable at some future time is an estimate of the...
Abstract: How can density forecasts help policymaking and economists? Earlier academic research has ...
This paper makes two contribution to the literature on density forecasts. First, we propose a novel ...
We forecast macroeconomic and financial uncertainties of the USA over the period of 1960:Q3 to 2018:...
textabstractWe propose a multivariate combination approach to prediction based on a distributional s...
We compare linear autoregressive (AR) models and self-exciting threshold autoregressive (SETAR) mode...
Linear Vector Autoregression (VAR) models provide a useful starting point for analysing multivariate...
A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks ...
The debate on the forecasting ability of non-linear models has a long history, and the Great Recessi...
We develop nonlinear leading indicator models for GDP growth, with the interest rate spread and grow...
In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among...
We systematically examine the comparative predictive performance of a number of linear and non-linea...
Many modelling issues and policy debates in macroeconomics depend on whether macroeconomic times ser...
this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among no...
We consider a method for producing multivariate density forecasts that satisfy moment restrictions i...
A density forecast of the realization of a random variable at some future time is an estimate of the...