This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession
The debate on the forecasting ability of non-linear models has a long history, and the Great Recessi...
Abstract This paper aims to find empirical evidence of nonlinearity in unemployment rates in OECD co...
textabstractTo enable answering the question in the title, we introduce a bivariate censored latent ...
This paper studies linear and nonlinear autoregressive leading indicator models of business cycles i...
This paper studies linear and nonlinear autoregressive leading indicator models of business cycles i...
We develop nonlinear leading indicator models for GDP growth, with the interest rate spread and grow...
This paper estimates Logistic Smooth Transition Autoregressive (LSTAR) models to analyze nonlinearit...
This paper examines the role of the Office for National Statistics Composite Longer Leading Indicato...
During the past few years investigators have found evidence indicating that various time-series repr...
The purpose of this paper is two-fold. First, we compare the accuracy of previous studies that analy...
This paper examines possible nonlinearities in growth rates of nine U.K. macroeconomic time series, ...
The authors use first differenced logged quarterly series for the GDP of 29 countries and the euro a...
The possible nonlinearity of business cycles is an old topic in eco-nomics. In this paper, I adopt t...
This paper empirically models the relationship between quarterly business cycle movements in the US ...
This paper studies the dynamic behaviour of US and Spanish GDP constructing final form (univariate) ...
The debate on the forecasting ability of non-linear models has a long history, and the Great Recessi...
Abstract This paper aims to find empirical evidence of nonlinearity in unemployment rates in OECD co...
textabstractTo enable answering the question in the title, we introduce a bivariate censored latent ...
This paper studies linear and nonlinear autoregressive leading indicator models of business cycles i...
This paper studies linear and nonlinear autoregressive leading indicator models of business cycles i...
We develop nonlinear leading indicator models for GDP growth, with the interest rate spread and grow...
This paper estimates Logistic Smooth Transition Autoregressive (LSTAR) models to analyze nonlinearit...
This paper examines the role of the Office for National Statistics Composite Longer Leading Indicato...
During the past few years investigators have found evidence indicating that various time-series repr...
The purpose of this paper is two-fold. First, we compare the accuracy of previous studies that analy...
This paper examines possible nonlinearities in growth rates of nine U.K. macroeconomic time series, ...
The authors use first differenced logged quarterly series for the GDP of 29 countries and the euro a...
The possible nonlinearity of business cycles is an old topic in eco-nomics. In this paper, I adopt t...
This paper empirically models the relationship between quarterly business cycle movements in the US ...
This paper studies the dynamic behaviour of US and Spanish GDP constructing final form (univariate) ...
The debate on the forecasting ability of non-linear models has a long history, and the Great Recessi...
Abstract This paper aims to find empirical evidence of nonlinearity in unemployment rates in OECD co...
textabstractTo enable answering the question in the title, we introduce a bivariate censored latent ...