ISSN 2284-7995, ISSN Online 2285-3952


Published in Scientific Papers. Series "Management, Economic Engineering in Agriculture and rural development", Vol. 16 ISSUE 4

The aim of this study was to forecast sheep numbers in Bulgaria on 01.11.2016 and on 01.11.2017, using a vector error correction model (VECM). A vector error correction model was constructed to forecast sheep numbers in Bulgaria for 2016 and 2017. The model was developed on the basis of 3 time series for the period 2000 – 2015 year. The time series were: Sheep numbers in Bulgaria on 01 November, Number of sheep farms on 01 November and Consumption of lamb and goat meat for 1 year per member of household (kg). Sources of information were annual data from the Ministry of agriculture and food, Republic of Bulgaria and the data from the National Statistical Institute. The stationarity of the variables was tested with Augmented Dickey-Fuller Unit Root Test. The cointegration of the three variables was estimated with Johansen Cointegration Test. VECM was constructed with lag length 1 and 1 cointegrated vector. An intercept was included in the model. Granger causality test was performed with the help of Wald Test in order to check the short-run causal relationship, running from the independent variables to the dependent variable. In order to forecast the number of sheep on 01 November 2016 and on 01 November 2017, one of the equations of the VECM was estimated with the method of least squares. The standard error of the regression, the coefficient of determination, the Adjusted R-squared of regression, F-statistic of regression, Jarque – Bera Test of Normality, Breusch-Godfrey Serial Correlation LM Test and Autoregressive Conditional Heteroscedasticity Test of Residuals were calculated. To check the stability of the model, the cumulative sum and cumulative sum of square had been represented. The forecasted sheep numbers in Bulgaria on 01.11.2016 are 1,313,796; and on 01.11.2017 are 1,306,403.

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© 2019 To be cited: Scientific Papers. Series “Management, Economic Engineering in Agriculture and Rural Development“.

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