We expand Nakamura’s (2005) neural network based inflation forecasting experiment to an alternative non-linear model; a Markov switching autoregressive (MS-AR) model. The two non-linear models perform approximately on par and outperform the linear autoregressive model on short forecast horizons of one and two quarters. Furthermore, the MS-AR model is the best performer on longer horizons of three and four quarters
In this paper, the exchange rate forecasting performance of neural network models are evaluated agai...
Following recent non-linear extensions of the present-value model, this paper examines the out-of-sa...
Linear models reach their limitations in applications with nonlinearities in the data. In this paper...
This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a ...
This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a ...
The purpose of this study is to contrast the forecasting performance of two non-linear models, a reg...
The purpose of this study is to contrast the forecasting performance of two non-linear models, a reg...
Linear models reach their limitations in applications with nonlinearities in the data. In this paper...
Forecasting methods of the neural network, ARIMA, ARIMA-GARCH, exponential smoothing and others are ...
This paper investigates whether a specific type of a recurrent neural network, in particular Jordan ...
MCom (Statistics), North-West University, Mafikeng CampusThe purpose of this study is to determine t...
The aim of this paper is to analyze the forecasting performance of alternative model for the US inf...
Prediction of inflation is needed by policy makers, investors and companies to plan economic strateg...
In this study the prediction capabilities of Artificial Neural Networks and typical econometric meth...
The article contains a review of inflation forecasting models, including the most popular class of m...
In this paper, the exchange rate forecasting performance of neural network models are evaluated agai...
Following recent non-linear extensions of the present-value model, this paper examines the out-of-sa...
Linear models reach their limitations in applications with nonlinearities in the data. In this paper...
This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a ...
This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a ...
The purpose of this study is to contrast the forecasting performance of two non-linear models, a reg...
The purpose of this study is to contrast the forecasting performance of two non-linear models, a reg...
Linear models reach their limitations in applications with nonlinearities in the data. In this paper...
Forecasting methods of the neural network, ARIMA, ARIMA-GARCH, exponential smoothing and others are ...
This paper investigates whether a specific type of a recurrent neural network, in particular Jordan ...
MCom (Statistics), North-West University, Mafikeng CampusThe purpose of this study is to determine t...
The aim of this paper is to analyze the forecasting performance of alternative model for the US inf...
Prediction of inflation is needed by policy makers, investors and companies to plan economic strateg...
In this study the prediction capabilities of Artificial Neural Networks and typical econometric meth...
The article contains a review of inflation forecasting models, including the most popular class of m...
In this paper, the exchange rate forecasting performance of neural network models are evaluated agai...
Following recent non-linear extensions of the present-value model, this paper examines the out-of-sa...
Linear models reach their limitations in applications with nonlinearities in the data. In this paper...