Impact of Inflation, Dollar Exchange Rate and Interest Rate on Red Meat Production in Turkey： Vector Autoregressive （VAR） Analysis

In this study, impact of inflation （WPI--Wholesale Price Index）, exchange rate, and interest rate on the production of red meat in Turkey was examined using the vector autoregressive （VAR） model. The model consisting of variables of dollar exchange rate, inflation rate, interest rate, beef, buffalo meat, mutton, and goat meat production amounts has been estimated for the period from 1981 to 2014. It has been detected that there is a tie among the dollar exchange rate, inflation rate, interest rate, and the amount of red meat production in Turkey. In order to determine the direction of this relation, Granger causality test was conducted. A one-way causal relation has been observed between： the goat meat production and dollar exchange rate; the buffalo meat production and the mutton production; and the beef production and the mutton production. To interpret VAR model, the impulse response function and variance decomposition analysis was used. As a result of variance decomposition, it has been detected that explanatory power of changes in the variance of dollar exchange rate, inflation rate, and interest rate in goat meat production amount is more than explanatory power of changes in the variances of mutton, beef, and buffalo meat variables.

Examination of Causal Relationship Among Consumer Goods Price Index, Bovine, and Water Buffalo Milk Price in Turkey With Vector Error Correction Model

In this study, causalities among consumer goods price index （CPI）, cattle milk price, and buffalo milk price are researched for Turkey using data covering the period from January 2005 to December 2014. The cointegration analysis and vector error correction model （VECM） are used the casualty relationship among CPI, cattle milk piece, and buffalo milk piece in estimating. According to unit root test, results indicate that each of series is not stationary, when the variables are defined in levels; but that each of series is stationary, when the variables are defined in first differences. Johansen＇s cointegration test results show that there exists a long-run equilibrium relationship among CPI, cattle milk piece, and buffalo milk piece. It is concluded that there are three cointegration vectors in the data. Since the series are found to be cointegrated, it used VECM model to test the existence of causality. According to the VECM, there has been no long-term relationship among the variables. In the results of cointegration analysis, its relation between cattle milk piece and CPI is found to be negative. However, a positive relationship has been found between cattle milk price and buffalo milk piece.

Investigation of Red Meat Production in Turkey by ANOM Test

When comparing more than two groups means, the analysis of means （ANOM） can be used as an alternative to the one-way analysis of variance （ANOVA） F test. The ANOM provides a ＂confidence interval type of approach＂ that allows it to determine which, if any, of the x groups has a significantly different mean from the overall average of all the group means combined. The aim of this study was to show the application of a statistical technique （ANOM）, mostly used by agriculture, to determine differences between red meat production by period in Turkey. The production quantities of goat meat, mutton, buffalo meat and beef between the years 2010 and 2016 according to the period were taken from Turkish Statistical Institute （TSI）. As a result, the difference between meat productions was found to be significant.

Estimation of Number Of Small Cattle Through ARIMA Models in Turkey

In this study, the number of sheep and goats in Turkey were analysed by time series analysis method, and the number of great cattle for next years predicted through the most appropriate time series model.Time series was formed using the data on the number of sheep and goats belonging to the period between 1930 and 2014 in Turkey It was determined through autocorrelation function graphic that the series weren＇t stationary at first, but they became stationary after their first difference were calculated. A stagnancy test was performed through extended Dickey-Fuller test. So as to determine the suitability of the model, it was reviewed if autocorrelation and partial autocorrelation graphs were white noise series and also the results of Box-Ljung test were reviwed. Through the ＂tested models, the model estimations, of which parameter estimates were significant and Akaike information criterion （AIC） was the smallest, were performed. The most appropriate model in terms of both the number of sheep and goats is first-level integrated moving average model stated as ARIMA（0,1,1）. In this model, it was estimated that there would be an increase in the number of sheep and goats in Turkey between the years of 2015 and 2020, however, the increase in the number of sheep would be more than the increase in the number of goats.