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榆林翻譯公司關(guān)鍵字：Where, Y that domestic agricultural GNP growth rate, I said that institutional factors, T said technical factors, K factors that capital, E is the land factor, L for labor, W for weather factors.Third, the data description and empirical analysisSet of indicators based on chart to collect data between 1985-2004, Table 1, Table 2 data are taken from the "Statistical Yearbook of Xinjiang", "Xinjiang five years", is calculated and made the appropriate treatment.Empirical analysis of ideas: the use of principal component analysis to be institutional factors and technical factors scores, using biased estimation method - principal component regression model (1) for quantitative analysis.Associated with the agricultural system based on six indicators, urbanization (CSHL), crop system changes (NZZB), industrial and agricultural goods comprehensive parity index (GNBJ), non-food area of ??the total sown area (FLMB), market trading volume index (JMZS) and irrigated area index (GGZS). Reflect institutional factors of the six variables in principal component analysis is a comprehensive index to the main component indicators that institutional factors. Indicators of income available after the system of linear transformation factor principal component scores (see Table 1).Similarly, in accordance with the six indicators related to agricultural technology, agricultural 科技三項(xiàng)費(fèi) with (KJ), rural electricity consumption (YD), chemical fertilizer (HF), agricultural machinery power (JD), finance for agriculture, forestry , water conservancy, meteorology and other departments operating expenses (SF) and rural junior high school education (CW). Technical factors of the six variables reflecting the principal component analysis is a comprehensive index to the main component indicators that technical factors. The indicators on income after linear transformation techniques of principal component factor scores (see Table 1).According to statistics 19 years (see Table 1) and the institutional, technical factors, principal component scores for (1) shows the model parameter estimation. Upon examination, all variables there is a serious multicollinearity, not directly using least squares method, while using a biased estimation method, the principal component regression. Is the first capital expenditure on agriculture, there is arable land, rural labor, and institutional factors score disaster area, call the shots technology component analysis factor scores, weighted according to the size of eigenvalues ??together as an indicator; then, with the weighted index and GNP to do regression analysis; Finally the estimated regression coefficients as calculated in accordance with the load (ie, eigenvalue) decomposition in different order, measurement results shown in Table 2:
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