The Failure to Predict the Great Recession-a View Through the Role of Credit

Rivas, María Dolores Gadea; Pérez‐Quirós, Gabriel

doi:10.1111/jeea.12122

Cited by 42 publications

(19 citation statements)

References 42 publications

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“…A credit boom during the run‐up to a recession is associated, on average, with a larger downward revision of PO. In line with the results presented by Gadea Rivas and Perez‐Quiros (2015), the connection between credit booms and post‐recession PO revisions disappears if we exclude data from the Great Recession (see Appendix ), suggesting that this factor is only relevant if a recession is accompanied by the end of a pronounced financial cycle. Overall, based on the LASSO (OLS) estimates, the explanatory variables explain 41 percent (68 percent) of the variation of PO revisions for the first year of a recession 11…”

Section: Resultssupporting

confidence: 85%

Recessions and Potential Output: Disentangling Measurement Errors, Supply Shocks, and Hysteresis Effects*

Dovern

Zuber

2020

Scandinavian J Economics

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This paper investigates expert revisions of potential output (PO) estimates following recessions. Using data from the Organisation for Economic Cooperation and Development (OECD), we show that downward revisions are substantial, permanent, and mostly driven by supply shocks. In contrast, PO estimates do not significantly react to demand shocks. Revisions are also partly caused by mismeasurement of PO before recessions. In particular, we show that the length of the preceding boom and pre-recession values of the current account balance and credit volumes are correlated with post-recession PO revisions. Our results call for improved methods for estimating PO and provide evidence against the existence of substantial hysteresis following demand shocks.

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Section: Resultssupporting

confidence: 85%

Recessions and Potential Output: Disentangling Measurement Errors, Supply Shocks, and Hysteresis Effects*

Dovern

Zuber

2020

Scandinavian J Economics

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“…The credit-to-GDP gap was found to have no significant contribution to the likelihood of going into a financial stress regime during the out-of-sample period consistent with the indication made by Gadea-Rivas and Perez-Quiros (2015). Another similar study is that of Abiad (2007) which assessed the signalling capability of the MS models during the Asian crises and compared it to the outcomes of the standard binary early predicting warning models.…”

Section: Literature Reviewmentioning

confidence: 78%

“…This current study however aims to examine the low and high periods of the financial market regime. Likewise, Gadea-Rivas and Perez-Quiros (2015) examined the function of credit in forecasting major recessionary episodes by assessesing the effect of credit on a business cycle's dynamic turning points. In the current study, we examine several key forecasting indicators in predicting the entry into and exit from chronological episodes of financial market regime of stress.…”

Section: Literature Reviewmentioning

confidence: 99%

Transition Assessment of the Bangladeshi Financial Market Stress Regimes: A Markov Switching Modeling Approach

Afreen

2021

IJSSER

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Purpose of this study: In view of the global financial crises and the ensuing consequences, this research presents the utility of demonstrating an assessment that can forecast the Bangladeshi financial market’s well-being by analysing episodes of economic crises which may prevent market distress. By graphically demonstrating eventual economic episodes in the financial sector, this study sets out to illustrate the chronological scenario of economic turning points. The scope of this research is to study the vulnerable aspects of financial instability in Bangladesh and seek possible remedies. Methodology: The Bangladeshi financial market regimes will be constructed based on Hamilton's Markov Switching Model (1989). This paper is the first attempt in utilising a standardised methodology found in business cycle literatures so as to determine the turning points of economic episodes in the Bangladeshi financial dynamic cycle. Main Findings: This study examines the financial crises and economic distress experienced by banks as forms of economic vulnerabilities. Thus, it describes the financial regimes of transition period movements in the context of the vulnerability of the Bangladeshi financial market sector using the Markov Switching Modeling (MSM) Approach and shows ways to possibly achieve recovery. Research Limitations/Implications: This research focuses on the current financial episodes of the economic sector’s dynamic movements in a condensed area, while the selection of a broad financial arena of parameters results in more significant and robust outcomes. Novelty/Originality: Further studies are needed to define and measure the financial cycle concept and its relationship with business cycles, as well as to delineate dynamic models that can offer substantial probabilistic assessments regarding changes in financial cycle regimes. This can significantly develop the capability of the financial market supervisory authorities to forecast macro-prudential systemic risks and to avoid or reduce the consequences of economic crises. This current study provides a platform for future studies in similar fields.

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“…Gadea Rivas and Perez‐Quiros (15) doubt the ability of credit variables to track the turning points of the economy effectively, being the relationship between business and credit cycle mostly affected by the Great Recession. We propose a real‐time exercise to overcome this criticism.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Credit variables are suitable for the conjunctural analysis because they are released a few days after the reference month and they are not affected by huge historical revisions as the official estimates of many macroeconomic aggregates, notoriously. The real‐time perspective helps to overcome the criticism by Gadea Rivas and Perez‐Quiros (15), who claim that all the research on the role of credit to predict macroeconomic fluctuations does not take into account that recession dating is uncertain in real time . The authors find that the contribution of credit to forecast turning points is distorted by the Great Recession, which explains most of the covariance between credit and economic aggregates.…”

Section: Introductionmentioning

confidence: 99%

Using Credit Variables to Date Business Cycle and to Estimate the Probabilities of Recession in Real Time

Aprigliano

Liberati

2019

The Manchester School

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Following the debate on the relationship between business and financial cycle rekindled in the last decade since the global financial crisis, we assess the ability of some financial indicators to track the Italian business cycle. We mostly use credit variables to detect the turning points and to estimate the probability of recession in real time. A dynamic factor model with Markov-switching regimes is used to handle a large dataset and to cope with the nonlinear evolution of the business cycle. The in-sample results strongly support the capacity of credit variables to estimate the probability of recessions and the implied coincident indicator proves their ability to fit the business cycle. Also in real time the contribution of credit is not negligible compared to that of the industrial production, currently used for the conjunctural analysis

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The Failure to Predict the Great Recession-a View Through the Role of Credit

Cited by 42 publications

References 42 publications

Recessions and Potential Output: Disentangling Measurement Errors, Supply Shocks, and Hysteresis Effects*

Recessions and Potential Output: Disentangling Measurement Errors, Supply Shocks, and Hysteresis Effects*

Transition Assessment of the Bangladeshi Financial Market Stress Regimes: A Markov Switching Modeling Approach

Using Credit Variables to Date Business Cycle and to Estimate the Probabilities of Recession in Real Time

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