Add to ORKGIn this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models in terms of the mean square and mean absolute forecast errors. For a set of 18 quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater
The use of linear parametric models for forecasting economic time series is widespread among practit...
The aim of the paper is to compare the forecasting performance of a class of statedependent autoregr...
With the introduction of new macroeconomic and financial indicators and the timely publication of hi...
In this paper, we compare the forecasting performance of univariate noncausal and conventional causa...
In this paper, we compare the forecasting performance of univariate noncausal and conventional causa...
In this paper, we propose a simulation-based method for computing point and density forecasts for un...
This paper is concerned with univariate noncausal autoregressive models and their potential usefulne...
This paper is concerned with univariate noncausal autoregressive models and their potential usefulne...
In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressiv...
Recently Stock and Watson (2007) showed that since the mid-1980s it has been hard for backward-looki...
In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time se...
In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregre...
Forecasting is an important tool for management, planning and administration in various fields. In t...
The main purpose of this dissertation is to compare the in-sample estimating and out-of-sample forec...
We apply the boosting estimation method in order to investigate to what extent and at what horizons ...
The use of linear parametric models for forecasting economic time series is widespread among practit...
The aim of the paper is to compare the forecasting performance of a class of statedependent autoregr...
With the introduction of new macroeconomic and financial indicators and the timely publication of hi...
In this paper, we compare the forecasting performance of univariate noncausal and conventional causa...
In this paper, we compare the forecasting performance of univariate noncausal and conventional causa...
In this paper, we propose a simulation-based method for computing point and density forecasts for un...
This paper is concerned with univariate noncausal autoregressive models and their potential usefulne...
This paper is concerned with univariate noncausal autoregressive models and their potential usefulne...
In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressiv...
Recently Stock and Watson (2007) showed that since the mid-1980s it has been hard for backward-looki...
In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time se...
In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregre...
Forecasting is an important tool for management, planning and administration in various fields. In t...
The main purpose of this dissertation is to compare the in-sample estimating and out-of-sample forec...
We apply the boosting estimation method in order to investigate to what extent and at what horizons ...
The use of linear parametric models for forecasting economic time series is widespread among practit...
The aim of the paper is to compare the forecasting performance of a class of statedependent autoregr...
With the introduction of new macroeconomic and financial indicators and the timely publication of hi...