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dc.contributor.author
Kormazou, Christina- Eleni- Triada
en
dc.date.accessioned
2017-03-24T13:38:26Z
dc.date.available
2017-03-25T01:00:19Z
dc.date.issued
2017-03-24
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/15201
dc.rights
Default License
dc.title
Ethanol and gasoline price in Brazil: A long-run time series and panel approach
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heal.type
masterThesis
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heal.secondaryTitle
A long-run time series and panel approach
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heal.classification
Energy Management
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heal.classification
Econometrics
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heal.keywordURI.LCSH
Alcohol as fuel--Economic aspects--Brazil
heal.keywordURI.LCSH
Biomass energy--Economic aspects--Brazil
heal.keywordURI.LCSH
Gasoline--Prices--Brazil
heal.language
en
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heal.access
free
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heal.license
http://creativecommons.org/licenses/by-nc/4.0
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heal.references
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Testing for stationarity in heterogeneous panel data. The Econ- ometrics J ournal , 3 (2) , 148 – 161. 26. Im, K.S. , Pesaran , M.H., & Shin , Y. ( 2003 ) . Te sting for unit roots in heterogene- ous panels. Journal of Econometrics , 115 (1) , 53 – 74. 27. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: Journal of the Econo- metric Society , 1551 - 1581 . 28. Juselius , K. (1992). Testing structural h ypotheses in a multivariate cointegration a nalysis of the PPP and the UIP for UK. Journal of Econometrics, 53 (1 - 3), 211 - 244. 29. Kao, C. (1999). Spurious regression and residual - based tests for cointegratio n in panel data. Journal of Econometrics, 90 (1) , 1 – 44. 30. Karaman , Örsal , D. (2007 ). Comparison of panel cointegration t ests (No. SFB649DP2007 - 029) . Humboldt University , Collaborative Research Center 649. 31. Koizumi, T. (2003). The Brazili an ethanol program : impacts on world ethanol and sugar markets . FAO Commodity a nd Trade Policy Research Working Paper No. 1 . Food and Agriculture Organization of the United Nations, 24. 79 32. Levin, A., Lin, C.F. , & Chu , C.S.J. ( 2002 ) . Unit root tests in panel data: asymp- totic and finite - sample properties. Journal of Econometrics , 108 (1) , 1 – 24. 33. Liitkepohl , H. (1993). An introduction to m ul tiple time series a nalysis, Berlin et al. 34. Maddala, G.S., & Wu , S. (1999). A comparative study of unit root tests with pa nel data and a new simple test. Oxford Bulletin of Economics and Statistics , 61 (S1) , 631 - 6 52. 35. Moraes, M.A.F.D. , Bacchi , M.R.P., & Caldarelli , C.E. (2016). Accelerated growth of the sugarcane, sugar, and ethanol sectors in Brazi l (2000 - 2008): Ef- fects on municipal gross domestic product per capita in the south - central region . Biomass and Bioenergy , 91 , 116 - 125 . 36. Moreira, J.R. , & Goldemberg , J. (1997). The alcohol p rogram. Energy p olicy , 27 (4) , 229 - 245 . 37. Nappo, M. (2007). A demanda por gasolina no Brasil: uma a valiação de suas Elas- ticidade s após a introdução dos carros b i combustíveis . 38. Nuñez, H.M. , & Otero , J. (2015). Integration in g asoline and ethanol markets in B razil over time and space under the flex - fuel technology . In 2015 AAEA & WAEA Joint Annual Meeting, July 26 - 28, San Francisco, California (No. 204306). Agricultural and Applied Economics Association & Western Agricul- tural Economics Association . 39. Pacini , H . , & Silveira , S. ( 2011). Consumer choice between ethanol and g asoline: Lessons from Brazil and Sweden. Energy Policy , 39 (11) , 6936 - 6942. 40. Pedroni, P. (1999). Critical Values for Cointegration Tests in Heterogeneous Pan- els with Multiple Regressors. Oxford Bulletin of Economics and Statistics , 61 (S1) , 653 - 670. 41. Perron, P. ( 1990 ). Testing for a unit root in a ti me series with a changing mean. Journal of Business & Economic Statistics , 8 (2) , 153 - 62. 42. Pesaran, H.H. , & Shin , Y. (1998). Generalized impulse response analysis in linear multivariate models . Economics Lett ers , 58 ( 1 ), 17 – 29 . 43. Phillips, P.C., & Hansen, B.E. (1990). Statistical infer ence in instrumental varia- bles r egression with I (1) processes. The Review of Economic Studies , 57 (1) , 99 - 125 . 44. Phillips, P.C.B. and P . Perron ( 1988 ). Testing for a Unit Root in Time Series Re- gression. Biometrica , 75 , 335 - 346. 45. Ramirez , M.D. (2007 ). A panel unit root and panel cointegration t est of the com- plementarity h ypothesis in the m exican c ase, 1960 - 2001 . Atlantic Economic Jour- nal, 35 (3), 343 - 356. 46. Rapsomanikis, G. , & Hallam, D. (2006). Threshold cointegration in the sugar - ethanol - oil price system in Brazil: evidence from nonlinear vecto r error correction models. FAO commodity and trade policy research working p aper , ( 22 ) . 47. Roppa, B. F . (2005) . Evolução do c onsumo d e gasolina no Bra sil e suas e lasti- cidades: 1973 a 2003. UFRJ, Rio de Janeiro . 80 48. Rosillo - Calle, F. , & Cortez , L.A. (1998). Towards ProAlcool II - a review of the Brazilian bioethanol program. Biomass and Bioenergy 14 (2) , 115 - 124. 49. Salvo , A . , & Huse , C. (2011). Is arbitrage tying the price of ethanol to that of gasolin e? Evidence from the uptake of flexible - fuel technology. Energy Journal , 32 (3), 119 - 148. 50. Santos , G. F. (2013). Fuel demand in Brazil in a dynamic panel data approach. Energy Economics , 36, 229 - 240. 51. Santos, A.I . , & Colomer , M. (2014). The elasti city of demand for gasoline in B ra- zil with the introduction of the flex - fuel fleet . 52. Silva, A.S., Vasconcelos , C.R.F., Vasconcelos , S.P., & de Mattos , R.S. (2014). Symmetric transmission of prices in the retail gasoline market in Brazil. Energy Economics , 43 , 11 - 21. 53. Silva , G.F. , Tiryaki , G.F. & Pontes , L.A.M. (2009). The impact of a growing eth- anol market on the demand elasticity for gasoline in Brazil. 54. Schün emann, L. ( 2007 ) . A demanda de gasolina automotiva no Brasil: o impac- tonas elasticidades de curto e longoprazo da expansão do gnv e dos carros flex. Faculdade s Faculdade de Economia e Finanças Ibmec, Rio de Janeiro
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heal.recordProvider
School of Science and Technology, MSc in Energy Management
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heal.publicationDate
2017-03-24
heal.abstract
This dissertation was written as a part of the MSc in Energy Management at the International Hellenic University. The aim of the study is to investigate possible integration of hydrous ethanol and gasoline price in Brazil at state level for the period 2002-2015. Initially, the historical framework of the domestic ethanol market is described towards overall understanding of the topic. Then by employing a Vector Autoregressive (VAR) model of all 27 Brazilian states, Johansen cointegration test investigates possible cointegration of both price series at state level. Impulse Response Functions are implemented within the pairwise time series analysis. A Vector Error Correction Model (VECM) provides long-run elasticities of ethanol price with respect to gasoline price for the cointegrated states. Within a panel data context panel unit root tests examine stationarity at levels and first differences. Finally, panel cointegration is examined through Pedroni and Kao panel cointegration tests and FMOLS on aggregate level. Regarding results, Johansen test provides evidence of co-movement between the price series in 10 out of the 27 Brazilian states. The long-run elasticities of ethanol price with respect to gasoline price for the cointegrated states range from 1.07% to 1.66% providing an average value of 1.33%. Impulse response functions reveal a relatively overall higher response of log_price_ethanol to a unitary shock in log_price_gasoline than the reverse case, with a levelling-off process within the twelve-month horizon of 2016, except for the case of Tocantins. Regarding panel data analysis, IPS and ADF Fisher panel unit root tests prove that both price series are I(1) at all states and I(0) when first differenced again at all states. Pedroni and Kao panel cointegration test output is if favor of panel cointegration. Finally, FMOLS provide an aggregate long-run elasticity estimate of 1.30 that is almost identical to the value provided by the time series approach.
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heal.tableOfContents
ABSTRACT CONTENTS LIST OF TABLES LIST OF FIGURES VIII 1 INTRODUCTION 2 HISTORICAL OVERVIEW PROÁLCOOL PROGRAM 3 LITERATURE REVIEW 3.1 HISTORICAL ERA DIVISION APPROACH 3.2 ECONOMIC APPROACH 3.3 TECHNICAL APPROACH 3.4 REGIONAL APPROACH 3.5 ECONOMETRIC APPROACH 3.5.1 Cointegration Analysis 3.5.2 Panel Data Analysis 3.5.3 Other Approaches 4 MODELLING STRATEGY 4.1 TIME SERIES ANALYSIS 4.1.1 Unit Root Hypothesis 4.1.2 Lag - Length Selection - Information Criteria 4.1.3 Cointegration Tests 4.1.4 Impulse Responses 4.2 PANEL DATA ANALYSIS 4.2.1 Panel Unit Root Tests 4.2.2 Panel Cointegration Tests 4.2.3 Pool Panel Analysis 5 DATA - EMPIRICAL ANALYSIS 5.1 TIME SERIES ANALYSIS 5.1.1 Lag - Length Selection - Information Criteria 5.1.2 Johansen Cointegration Test 5.1.3 VECM - Long - Run Elasticity 5.1.4 VECM - Speed of Adjustment 5.1.5 Impulse Responses 5.2 PANEL DATA ANALYSIS 5.2. 1 Unit Root Tests 5.2.2 Panel Cointegration Tests 5.2.3 Pool Panel Analysis 6 CONCLUSIONS REFERENCES AND BIBLIOGRAPHY APPENDIX
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heal.advisorName
Panagiotidis, Theodoros
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heal.committeeMemberName
Panagiotidis, Theodoros
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heal.academicPublisher
IHU
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heal.academicPublisherID
ihu
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heal.numberOfPages
91
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heal.spatialCoverage
Brazil
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heal.temporalCoverage
2002 - 2015
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