Real and financial cycles: estimates using unobserved component models for the Italian economy

Bulligan, Guido; Burlón, Lorenzo; Monache, Davide Delle; Silvestrini, Andrea

doi:10.1007/s10260-019-00453-1

Cited by 10 publications

(9 citation statements)

References 44 publications

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“…Our outcomes confirm earlier findings of large medium-term cycles in credit volumes and house prices as well as the strong correlation between the medium-term cycles of house price and credit cycles (see e.g. Bulligan et al, 2019;Yan and Huang, 2020). Our study sheds new light on the estimation of both short and medium term cycles in one joint framework, and the co-cyclicality of GDP, house prices and credit.…”

Section: Discussionsupporting

confidence: 90%

Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies*

2021

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We discuss a model-based simultaneous decomposition of multiple time series in shortterm and medium-term cyclical dynamics. We associate short-term dynamic features with the business cycle and medium-term dynamic features with the financial cycle. For eight advanced economies, we analyse a set of macroeconomic and financial time series data. A strong and common finding among all economies is the co-cyclicality of medium-term cycles, especially those corresponding to house price and gross domestic product variables. We also find empirical evidence that the house price is partly driven by the credit cycle. Most cyclical movements in the country-specific time series appear to be driven by domestic rather than global factors.

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Section: Discussionsupporting

confidence: 90%

Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies*

2021

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“…( 2013 ), Basistha ( 2007 ), Bulligan et al. ( 2019 ), Busetti et al. ( 2016 ), González-Astudillo et al.…”

Section: Problem Statementmentioning

confidence: 99%

Estimation of the potential GDP by a new robust filter method

Gyurkovics

Takács

2023

Cent Eur J Oper Res

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The first purpose of this paper is to propose a theoretically new robust filter method to estimate non-observable macroeconomic indicators. The second purpose is to apply the proposed method to estimate the Hungarian potential GDP in 2000–2021. The novelty of the proposed filter method is that — unlike papers published so far — it does not require the stability of the dynamic model, only a partial stability condition must be satisfied. Moreover, such time-dependent uncertainties and nonlinearities can arise in the model that satisfy a general quadratic constraint. An important advantage of the proposed robust filter method over the traditional Kalman filter is that no stochastic assumptions is needed that may not be valid for the problem at hand. The proposed filter method has never been applied to estimate the potential GDP. To estimate the Hungarian potential GDP, the proposed method is applied using uni-, bi- and trivariate models. Estimations up to 2021 has not been published yet for the Hungarian economy. The examined period includes both the financial world crisis and the Covid-19 crisis. The results of the different models are consistent. It turned out that the economic policy was very procyclical after 2012, and the GDP gap was still positive during and also after the Covid-19 crisis.

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“…In the same strand, Bulligan et al . (7) employ univariate and multivariate trend‐cycle decomposition based on unobserved component models to study the relationship between business and financial cycle. Our paper adds to this strand of literature by inspecting the relationship between business and financial cycles in ‘normal’ times other than during economic turmoils.…”

Section: Literature Reviewmentioning

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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Real and financial cycles: estimates using unobserved component models for the Italian economy

Cited by 10 publications

References 44 publications

Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies*

Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies*

Estimation of the potential GDP by a new robust filter method

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

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