The Classification of Economic Activity into Expansions and Recessions

The Business Cycle Dating Committee (BCDC) of the National Bureau of Economic Research provides a historical chronology of business cycle turning points. This paper investigates three central aspects about this chronology: (1) How skillful is the BCDC in classifying economic activity into expansions and recessions? (2) Which indices of business conditions best capture the current but unobservable state of the business cycle? And (3) Which indicators predict future turning points best and at what horizons? We answer each of these questions in detail with methods novel to economics designed to assess classification ability. In the process we clarify several important features of business cycle phenomena.business cycle turning points, receiver operating characteristic curve

The Yield Curve as a Recession Leading Indicator. An Application for Gradient Boosting and Random Forest

Most representative decision-tree ensemble methods have been used to examine the variable importance of Treasury term spreads to predict US economic recessions with a balance of generating rules for US economic recession detection. A strategy is proposed for training the classifiers with Treasury term spreads data and the results are compared in order to select the best model for interpretability. We also discuss the use of SHapley Additive exPlanations (SHAP) framework to understand US recession forecasts by analyzing feature importance. Consistently with the existing literature we find the most relevant Treasury term spreads for predicting US economic recession and a methodology for detecting relevant rules for economic recession detection. In this case, the most relevant term spread found is 3-month–6-month, which is proposed to be monitored by economic authorities. Finally, the methodology detected rules with high lift on predicting economic recession that can be used by these entities for this propose. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods are useful for interpretation comparing many alternative algorithms and we discuss the interpretation for our result and propose further research lines aligned with this work

Dating and Forecasting the G7 Business Cycle

openThe two recent global recessions triggered by the Global Financial Crisis in 2007 and the pandemic in 2020 have put business cycle analysis at the forefront of economic research. An important aspect relates to the identification of turning points. Following the methodology proposed by Stock and Watson (2010) to date turning points in the United States, this thesis uses a disaggregated dataset of economic indicators for the G7 to identify turning points in the global business cycle. A machine learning algorithm XGBoost is used to evaluate the new chronology and compares it to OECD reference chronology. Moreover, the algorithm selects the best indicators of global recessions

A chronology of turning points in economic activity: Spain 1850-2011

This paper codifies in a systematic and transparent way a historical chronology of business cycle turning points for Spain reaching back to 1850 at annual frequency, and 1939 at monthly frequency. Such an exercise would be incomplete without assessing the new chronology itself and against others —this we do with modern statistical tools of signal detection theory. We also use these tools to determine which of several existing economic activity indexes provide a better signal on the underlying state of the economy. We conclude by evaluating candidate leading indicators and hence construct recession probability forecasts up to 12 months in the future.Business cycles ; Spain

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach

The literature on using yield curves to forecast recessions customarily uses 10-year--three-month Treasury yield spread without verification on the pair selection. This study investigates whether the predictive ability of spread can be improved by letting a machine learning algorithm identify the best maturity pair and coefficients. Our comprehensive analysis shows that, despite the likelihood gain, the machine learning approach does not significantly improve prediction, owing to the estimation error. This is robust to the forecasting horizon, control variable, sample period, and oversampling of the recession observations. Our finding supports the use of the 10-year--three-month spread