Recent studies suggest that migration to Indian cities from rural regions (MIG) can be explained by Quality of life (QL), state income as a ratio of Aggregate Indian income (Y) and the ratio of state employment to overall employment in India (E). Using data for the 29 Indian states, the following model is estimated: MIG^=−4.2+1.2QL−0.6Y−0.8E, with standard errors (0.9), (0.8), (0.05), (0.02) reported below the respective coefficients. The t-statistic for the null hypothesis that the quality of life (QL) index does not impact migration is
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Recent studies suggest that migration to Indian cities from rural regions (MIG) can be explained by Quality of life (QL), state income as a ratio of Aggregate Indian income (Y) and the ratio of state employment to overall employment in India (E). Using data for the 29 Indian states, the following model is estimated: M̂ÎĜ=-4.2+1.2 QL-0.6 Y-0.8 E, with standard errors (0.9), (0.8), (0.05), (0.02) reported below the respective coefficients. The t-statistic for the null hypothesis that the quality of life (QL) index does not impact migration is (A) 0.8 (B) 0.9 (C) 1.5 (D) Insufficient information to calculate
Also written as: Recent studies suggest that migration to Indian cities from rural regions (MIG) can be explained by Quality of life (QL), state income as a ratio of Aggregate Indian income (Y) and the ratio of state employment to overall employment in India (E). Using data for the 29 Indian states, the following model is estimated: MIG_hat=-4.2+1.2 QL-0.6 Y-0.8 E, with standard errors (0.9), (0.8), (0.05), (0.02) reported below the respective coefficients. The t-statistic for the null hypothesis that the quality of life (QL) index does not impact migration is
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What this question tests
This question is on t-tests, F-tests and testing restrictions, part of Linear regression and OLS in Econometrics.
Testing a single coefficient, several coefficients, or a linear restriction.
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