Predicting the causes and phases of business cycles in India a macro econometric analysis

dc.contributor.guideArora, Nitin
dc.coverage.spatialEconomics
dc.creator.researcherSumanpreet Kaur
dc.date.accessioned2019-08-27T09:35:48Z
dc.date.available2019-08-27T09:35:48Z
dc.date.awardedn.d.
dc.date.completed2018
dc.date.registered24/10/2013
dc.description.abstractThe present study has been undertaken to date the business cycles, to determine its causes, to test the sensitivity of Indian business cycles and to finally predict the probability of recession. The Bry and Boschan dating algorithm has been applied for identifying business cycle chronology and the sources of business cycles were identified through SVAR modeling. For analysing the business cycle synchronisation between India and its trading associates, the regression relationships amongst the variables of interest are estimated. Finally, probit regression is also estimated for comparing the actual and predicted phases and for obtaining the probability of recession for the future forecast horizon. The empirical findings suggested that 2 classical cycles, 13 growth cycles, and 16 growth newlinerate cycles were identified in IIP with an average duration of 215, 38 and 32 months respectively. The innovation accounting results revealed that the responses of IIPcy to the monetary sector, fiscal situation, and the commodity market prices were strong and significant in comparison to other shocks. The business cycle correlations and root mean squared analysis confirm the presence of business cycle co-movements between India and its partners except for Iraq. The relation between business cycle synchronisation and trade integration for India is found to be significant with Australia, China, Iran, Saudi Arabia, Singapore and the USA. Finally, the CILI was constructed as a weighted average of all the scrutinised component series. The average duration of lead for peak and trough is reported to be 6.4 months and 7.8 months respectively in case of growth cycles and 5 and 3 months for growth rate cycles. Next, a probit model was estimated for evaluating the predictive power of CILI which reflected an insignificant difference between the actual and predicted phases of the business cycle. And the probability of witnessing a recession in growth and growth rate cycles with forecast horizons of 3, 6, 9 and 12 months, glided around 0.4.
dc.description.noteBibliography and References p.208-232, and Appendix Tables p.I-XXIII
dc.format.accompanyingmaterialCD
dc.format.dimensions-
dc.format.extent232p.
dc.identifier.urihttp://hdl.handle.net/10603/255669
dc.languageEnglish
dc.publisher.institutionDepartment of Economics
dc.publisher.placeChandigarh
dc.publisher.universityPanjab University
dc.relation-
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordBry Boschan Dating Algorithm
dc.subject.keywordBusiness Cycles
dc.subject.keywordBusiness Cycle Synchronisation
dc.subject.keywordProbit Regression
dc.subject.keywordSocial Sciences,Economics and Business,Economics
dc.subject.keywordSVAR Modeling
dc.titlePredicting the causes and phases of business cycles in India a macro econometric analysis
dc.title.alternative-
dc.type.degreePh.D.

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