Hostname: page-component-84b7d79bbc-4hvwz Total loading time: 0 Render date: 2024-07-26T19:19:40.088Z Has data issue: false hasContentIssue false

Analyse chronologique des prix des produits vivriers au Burundi Interprétation statistique et économique

Published online by Cambridge University Press:  17 August 2016

Get access

Extract

Le Burundi, pays enclavé dans l’Afrique de l’Est, s’étend sur une superficie d’environ 28.000 km2. Avec une population de près de 4,5 millions d’habitants, il n’est pas étonnant d’apprendre que la densité démographique dépasse 300 habitants au km2 dans certaines régions du pays.

Par contre, le taux d’urbanisation est resté très modeste jusqu’à présent et la population dans les villes ne dépasse pas 6 % de la population totale.

Dans ces conditions, il est assez normal que l’agriculture burundaise soit essentiellement une agriculture de subsistance qui a permis à la population de continuer à croître à un rythme proche de 3 % par an sans qu’elle n’ait à souffrir trop ouvertement des effets des disettes inévitables durant les périodes de soudure entre deux saisons culturales.

Cette agriculture de subsistance a été par ailleurs favorisée par le relief et les conditions climatiques qui ont encouragé les habitants à se disperser afin d’occuper les endroits qui offraient les conditions de vie les plus agréables (salubrité, accès à l’eau, fertilité raisonnable de la terre, saison sèche relativement courte).

Type
Research Article
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1987 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES BIBLIOGRAPHIQUES

Akaike, H. (1974), A New Look at the Statistical Model Identification, IEEE Transaction on Automatic Control, AC-19, p. 716723.Google Scholar
Ansley, C. (1979), An Algorithm for the Exact Likelihood of a mixed Autoregressive-Moving, Average Process, Biometrika, 66, p. 59.Google Scholar
Bartlett, M.S. (1955), Stochastic Processes, Cambridge University Press, Cambridge.Google Scholar
Beach, C.M. and MacKinnon, J.G. (1978), A Maximum Likelihood Procedure for Regression with Autocorrelated Errors, Econometrica, 46, p. 5158.Google Scholar
Box, G.E.P. and Jenkins, G.M. (1976), Time Series Analysis Forecasting and Control, Holden Day, San Francisco.Google Scholar
Box, G. and Pierce, D. (1970), Distribution of Residual Autocorrelation in Autoregressive Integrated Moving Average Time-Series Models, Journal of the American Statistical Association, 65, p. 15091529.Google Scholar
Chow, G.C. (1983), Econometrics, International Student Edition, McGraw-Hill, Tokyo.Google Scholar
Cochrane, D. and Orcutt, G. (1949), Applications of Least Squares Regression to Relationships Containing Autocorrelated Error Terms, Journal of the American Statistical Association, 44, p. 3261.Google Scholar
Davies, N., Triggs, C.M. and Newbold, P. (1977), Significance Levels of the Box-Pierce Portmanteau Statistic in Finite Samples, Biometrika, 64, p. 517522.Google Scholar
Degand, J. et D’Haese, L. (1983), Etude économique du marché des produits vivriers au Burundi. Analyse chronologique des prix du haricot, Tropicultura, vol. 1, n° 3.Google Scholar
Degand, J. D’Haese, L. et Ndimira, P. (1985), Etude économique du marché des produits vivriers au Burundi. Analyse chronologique des prix de la pomme de terre, Tropicultura, vol. 3, n° 1.Google Scholar
Durbin, J. (1960), The Fitting of Time Series Models, Rev. Int. Inst. Stat., 28, p. 233.Google Scholar
Durbin, J. and Watson, G.S. (1950), Testing for Serial Correlation in Least Squares Regression I, Biometrika, 37, p. 409428.Google Scholar
Durbin, J. and Watson, G.S. (1951), Testing for Serial Correlation in Least Squares Regression II, Biometrika, 38, p. 159178.Google Scholar
Gallant, A.R. and Goebel, J.J. (1976), Nonlinear Regression with Autoregressive Errors, Journal of the American Statistical Association, 71, p. 961967.Google Scholar
Gouriéroux, C. et Monfort, A. (1983), Cours de séries temporelles, Economica, Paris.Google Scholar
Hald, A. (1952), Statistical Theory with Engeneering Applications, John Wiley & Sons, New-York.Google Scholar
Harvey, A.C. and Mac Avinchey, I.D. (1978), The Small Sample Efficiency of Two-Step Estimators in Regression Models with Autoregressive Disturbances, Discussion Paper N°78–10, University of British Columbia, April, 1978.Google Scholar
Harvey, A.C. and Phillips, G.D.A. (1979), Maximum Likelihood Estimation of Regression Models with Autoregressive-Moving Average Disturbances, Biometrika, 66, p. 4958.Google Scholar
Hildreth, C. and Lu, Y.Y. (1960), Demand Relations with Autocorrelated Disturbances, Research Bulletin 276, Michigan State University Agricultural Experiment Station.Google Scholar
Kiviet, J. (1980), Effects of ARMA Errors on Tests for Regression Coefficients: Comments on Vinod’s Article, Journal of the American Statistical Association, 75, p. 353358.Google Scholar
Ljung, G.M. and Box, G.E.P. (1978), On a Measure of Lack of Fit in Time Series Models, Biometrika, 65, p. 297303.Google Scholar
Pearlman, J.G. (1980), An Algorithm for the Exact Likelihood of a High-Order Autoregressive-Moving Average Process, Biometrika, 67, p. 232233.Google Scholar
Priestley, M.B. (1981), Spectral Analysis of Time Series, Volume 1: Univariate Series, Academic Press, London.Google Scholar
Schwartz, G. (1978), Estimating the Dimension of a Model, Annals of statistics, 6, p. 461464.Google Scholar
Thomas, J.J. and Wallis, K.F. (1971), Seasonal Variation in Regression Analysis, Journal of the Royal Statistical Society A, 134, p. 5772.Google Scholar
Vinod, H. (1976), Effects of Arma Errors on the Significance Tests for Regression Coefficients, Journal of the American Statistical Association, 71, p. 929933.Google Scholar
Wallis, K.F. (1974), Seasonal Adjustment and Relations between Variables, Journal of the American Statistical Association, 69, p. 1831.Google Scholar
Watson, G. (1955), Serial Correlation in Regression Analysis I, Biometrika, 42, p. 327341.Google Scholar
Watson, G. and Hannan, E. (1956), Serial Correlation in Regression Analysis II, Biometrika, 43, p. 436448.Google Scholar