Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Berge, Travis J.

doi:10.1002/for.2345

Cited by 71 publications

(35 citation statements)

References 70 publications

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“…Another out‐of‐sample evaluation methodology which is often used (see, for example, Berge, ) is the success ratio, i.e. the number of times our probabilistic forecasts predict the right classification of recession versus expansion.…”

Section: Resultsmentioning

confidence: 99%

“…Recently there have been examples of works that use large datasets in forecasting recession, without relying on factor models. One notable example is Berge (), where the author uses forecasts average and a boosting algorithm to predict recessions based on 19 indicators. Moreover, Ng () also uses boosting to consider 132 potential predictors to forecasts business cycle turning points.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting US Recessions with a Large Set of Predictors

Fornaro

2016

Journal of Forecasting

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In this paper, I use a large set of macroeconomic and financial predictors to forecast U.S. recession periods. I adopt Bayesian methodology with shrinkage in the parameters of the probit model for the binary time series tracking the state of the economy. The in-sample and out-of-sample results show that utilizing a large cross-section of indicators yields superior U.S. recession forecasts in comparison to a number of parsimonious benchmark models. Moreover, data rich models with shrinkage manage to beat the forecasts obtained with the factor-augmented probit model employed in past research.JEL Classification: C11, C25, E32, E37

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting US Recessions with a Large Set of Predictors

Fornaro

2016

Journal of Forecasting

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“…In the wake of the Great Recession, considered by the International Monetary Fund the worst global recession since World War II (IMF, ), the usefulness of economic models in forecasting recessions has been questioned (Gadea & Perez‐Quiros, ). By way of background to the recent increase in interest in this topic, a large body of literature has tried to find leading indicators of U.S. economic activity since the late‐1980s (Berge, ; Berge & Jordà, ; Estrella & Hardouvelis, ; Estrella & Mishkin, ; Giacomini & Rossi, ; Hamilton & Kim, ; Harvey, , ; Levanon, Manini, Ozyildirim, Schaitkin, & Tanchua, ; Liu & Moench, ; Stock & Watson, ). Despite the great volume of papers on this topic, accurately predicting business‐cycle turning points is still a pertinent research topic, and increasingly so since the largely unpredicted Great Recession.…”

Section: Introductionmentioning

confidence: 99%

Do leading indicators forecast U.S. recessions? A nonlinear re‐evaluation using historical data

Plakandaras

Cuñado

Gupta

et al. 2017

International Finance

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This paper analyses to what extent a selection of leading indicators is able to forecast U.S. recessions, by means of both dynamic probit models and Support Vector Machine (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models predict U.S. recession periods more accurately than SVM models up to six months ahead, while the SVM models are more accurate over longer horizons. Furthermore, SVM models appear to distinguish between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models are those that include oil, stock returns and the term spread as leading indicators.

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“…Their results indicate that the interest rate spread, the real effective exchange rate as well as some monetary indicators and some survey indicators can help to predict turning points of the German business cycle. Berge (2015) focuses in his study on recession forecasting with the help of leading indicators. He argues that predictors that describe a real economic activity provide the clearest signal of recession at very short horizons.…”

Section: Introductionmentioning

confidence: 99%

The Composite Leading Indicator for German Business Cycle

Tkáčová¹,

Gavurová²,

Behún³

2017

JOC

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Monitoring and predicting economic cycles have returned to the awareness of economists with the impact of the economic crisis in 2007/2008. To determine the current and future state of the country's economic cycle, Composite Leading Indicators (CLI) can be used. Their structure is being dealt with by institutions at the national and international level (OECD, Eurostat). Correct predictions of public finance development and the entrepreneurship sphere are very important for competitiveness of the country. The aim of the paper is to propose a new Composite Leading Indicator (CLI) to monitor and predict the German economy. The analysis of 140 quantitative and qualitative indicators of industry, services, retail, construction, foreign trade, labor market, money aggregates, stock indices, confidence indicators, consumer expectations was performed for the needs of the indicator. As the reference series represents the German economic cycle, the GDP indicator is selected at constant prices for 2010. All selected quarterly time series are applied with seasonal index methods, the Hodrick-Prescott filter (HP filter) in the R program, cross-correlation with time shifts, methods selection and scoring, data standardization, identification of the same and different data and the subsequent construction of the CLI of the German economic cycle. The generated CLI can predict the German economy cycle two quarters ahead with a cross-correlation value of 0.867. The forecasting capabilities of the assembled indicator were better than the prediction capabilities of OECD, Eurostat and IFO indicator.

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Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Cited by 71 publications

References 70 publications

Forecasting US Recessions with a Large Set of Predictors

Forecasting US Recessions with a Large Set of Predictors

Do leading indicators forecast U.S. recessions? A nonlinear re‐evaluation using historical data

The Composite Leading Indicator for German Business Cycle

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