Publicação
Credit cycle identification: A Markov-switching application
| Resumo: | This project aims to study credit dynamics and to identify phases of credit cycles at the country level. We applied a Markov-switching (MS) autoregressive framework and a MS with regime-invariant macroeconomic variables to a broad concept of credit, domestic credit. We used a sample of 10 developed countries. MS identification power is assessed using smooth probabilities of low growth states, collected as a by-product of models estimation, against historical databases of crisis events. Conclusions support that MS is accurate in identifying credit cycle phases, and that domestic credit is a good variable for such identification. Additionally, Credit Gap, excess growth over GDP and Broad Money contribute positively to the MS predictions. |
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| Autores principais: | Santos, João Ramiro Rodrigues Simões dos |
| Assunto: | Credit cycles Phase identification Markov-switching |
| Ano: | 2014 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This project aims to study credit dynamics and to identify phases of credit cycles at the country level. We applied a Markov-switching (MS) autoregressive framework and a MS with regime-invariant macroeconomic variables to a broad concept of credit, domestic credit. We used a sample of 10 developed countries. MS identification power is assessed using smooth probabilities of low growth states, collected as a by-product of models estimation, against historical databases of crisis events. Conclusions support that MS is accurate in identifying credit cycle phases, and that domestic credit is a good variable for such identification. Additionally, Credit Gap, excess growth over GDP and Broad Money contribute positively to the MS predictions. |
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