Predictive power of Markovian models: Evidence from US recession forecasting

This paper provides extensions to the application of Markovian models in predicting US recessions. The proposed Markovian models, including the hidden Markov and Markov models, incorporate the temporal autocorrelation of binary recession indicators in a traditional but natural way. Considering interest rates and spreads, stock prices, monetary aggregates, and output as the candidate predictors, we examine the out‐of‐sample performance of the Markovian models in predicting the recessions 1–12 months ahead, through rolling window experiments as well as experiments based on the fixed full training set. Our study shows that the Markovian models are superior to the probit models in detecting a recession and capturing the recession duration. But sometimes the rolling window method may affect the models' prediction reliability as it could incorporate the economy's unsystematic adjustments and erratic shocks into the forecast. In addition, the interest rate spreads and output are the most efficient predictor variables in explaining business cycles.     

Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model

This paper introduces a regime switching vector autoregressive model with time‐varying regime probabilities, where the regime switching dynamics is described by an observable binary response variable predicted simultaneously with the variables subject to regime changes. Dependence on the observed binary variable distinguishes the model from various previously proposed multivariate regime switching models, facilitating a handy simulation‐based multistep forecasting method. An empirical application shows a strong bidirectional predictive linkage between US interest rates and NBER business cycle recession and expansion periods. Due to the predictability of the business cycle regimes, the proposed model yields superior out‐of‐sample forecasts of the US short‐term interest rate and the term spread compared with the linear and nonlinear vector autoregressive (VAR) models, including the Markov switching VAR model.     

A leading indicator approach to predicting short‐term shifts in demand for business travel by air to and from the UK

This paper uses the probit model to examine whether leading indicator information could be used for the purpose of predicting short‐term shifts in demand for business travel by air to and from the UK. Leading indicators considered include measures of business expectations, availability of funds for corporate travel and some well‐known macroeconomic indicators. The model performance is evaluated on in‐ and out‐of‐sample basis, as well as against a linear leading indicator model, which is used to mimic the current forecasting practice in the air transport industry. The estimated probit model is shown to provide timely predictions of the early 1980s and 1990s industry recessions and is shown to be more accurate than the benchmark linear model. Copyright © 2005 John Wiley & Sons, Ltd.     

住房金融及政策的有序Probit模型

采用有序Probit方法,建立住房金融及政策期望证实和相关诸因素之间的多元回归模型,通过改进这些政策和措施来推动住房市场发展.所用的方法不仅考虑了不同群体的消费偏好,也考虑了每个样本个体再选择因素上的差异.     

Forecasting football results and the efficiency of fixed‐odds betting

An ordered probit regression model estimated using 10 years' data is used to forecast English league football match results. As well as past match results data, the significance of the match for end‐of‐season league outcomes, the involvement of the teams in cup competition and the geographical distance between the two teams' home towns all contribute to the forecasting model's performance. The model is used to test the weak‐form efficiency of prices in the fixed‐odds betting market. A strategy of selecting end‐of‐season bets with a favourable expected return according to the model appears capable of generating a positive return. Copyright © 2004 John Wiley & Sons, Ltd.