Measuring the probability of a business cycle turning point by using a multivariate qualitative hidden Markov model

A two‐step procedure to produce a statistical measure of the probability of being in an accelerating or decelerating phase of economic activity is proposed. It consists of, first, an extraction of the individual linear innovations of a set of relevant macroeconomic variables whose signs are accumulated into a qualitative vector process and, second, of a factor analysis applied to this vector. The factor process is a two‐state Markov process of order one whose states are described as favourable and unfavourable. Estimated on French business surveys, this measure appears to be a competitive coincident indicator. Copyright © 2000 John Wiley & Sons, Ltd.     

Pre‐recession pattern of six economic indicators in the USA

This paper applies a tightly parameterized pattern recognition algorithm, previously applied to earthquake prediction, to the problem of predicting recessions. Monthly data from 1962 to 1996 on six leading and coincident economic indicators for the USA are used. In the full sample, the model performs better than benchmark linear and non‐linear models with the same number of parameters. Subsample and recursive analysis indicates that the algorithm is stable and produces reasonably accurate forecasts even when estimated using a small number of recessions. Copyright © 2000 John Wiley & Sons, Ltd.     

基于概念词的特征提取方法

为解决因未考虑语义关联造成的VSM描述不准确的问题,基于知网本体库计算词语间的语义相似度,采用识别完全子图的方式生成概念词列表,再用概念词替换存在密切语义关联的词语.实验表明,该方法在改进文档特征提取效果的同时也明显降低了向量空间的维度.与不经概念词处理的特征提取方法相比,该方法在分类识别率上有一定提升.     

Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model

A recent study by Rapach, Strauss, and Zhou (Journal of Finance, 2013, 68(4), 1633–1662) shows that US stock returns can provide predictive content for international stock returns. We extend their work from a volatility perspective. We propose a model, namely a heterogeneous volatility spillover–generalized autoregressive conditional heteroskedasticity model, to investigate volatility spillover. The model specification is parsimonious and can be used to analyze the time variation property of the spillover effect. Our in‐sample evidence shows the existence of strong volatility spillover from the US to five major stock markets and indicates that the spillover was stronger during business cycle recessions in the USA. Out‐of‐sample results show that accounting for spillover information from the USA can significantly improve the forecasting accuracy of international stock price volatility.     

Forecasting inflation using economic indicators: the case of France

In order to provide short‐run forecasts of headline and core HICP inflation for France, we assess the forecasting performance of a large set of economic indicators, individually and jointly, as well as using dynamic factor models. We run out‐of‐sample forecasts implementing the Stock and Watson (1999) methodology. We find that, according to usual statistical criteria, the combination of several indicators—in particular those derived from surveys—provides better results than factor models, even after pre‐selection of the variables included in the panel. However, factors included in VAR models exhibit more stable forecasting performance over time. Results for the HICP excluding unprocessed food and energy are very encouraging. Moreover, we show that the aggregation of forecasts on subcomponents exhibits the best performance for projecting total inflation and that it is robust to data snooping. Copyright © 2007 John Wiley & Sons, Ltd.     

Detecting and Predicting Economic Accelerations,Recessions, and Normal Growth Periods in Real‐Time

The dichotomous characterization of the business cycle in recessions and expansions has been central in the literature over the last 50 years. However, there are various reasons to question the adequacy of this dichotomous, recession/expansion approach for our understanding of the business cycle dynamics, as well as for the prediction of future business cycle developments. In this context, the contribution of this paper to the literature is twofold. First, since a positive rate of growth at the level of economic activity can be considered as the normal scenario in modern economies due to both population and technological growth, it proposes a new non‐parametric algorithm for the detection and dating of economic acceleration periods, trend or normal growth periods, and economic recessions. Second, it uses an ordered probit framework for the estimation and forecasting of these three business cycle phases, applying an automatized model selection approach using monthly macroeconomic and financial data on the German economy. The empirical results show that this approach has superior out‐of‐sample properties under real‐time conditions compared to alternative probit models specified individually for the prediction of recessions and/or economic accelerations. Copyright © 2016 John Wiley & Sons, Ltd.