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econ2206 project solution

# econ2206 project solution - ECON2206/ECON3290 Introductory...

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ECON2206/ECON3290: Introductory Econometrics Session 1, 2010 Course Project Solution Guide Each question is worth 1 mark - and there are 20 questions in total. Note Instruction (d): ‘Remember that when performing statistical tests, always state the null and alternative hypotheses, the test statistic and it’s distribution under the null hypothesis, the level of signi fi cance and the conclusion of the test.’ Full credit cannot be given if this is ignored. A print-out of the SHAZAM output must be attached at the end of the answers (otherwise 5 marks are to be deducted). (1) What is the average, minimum, and maximum value and standard deviation for each of the variables in the HPRICEN.RAW sample ?        23926 2 5394 5 4474 6 3615 3 8835 37 756  5000 0 006 3 850 3 560 1 130 18 70  50001 88 976 8 710 8 780 12 130 71 100  9414 8 7 1497 1 1699 0 7322 2 1206 14 916 (2) Would you expect the correlation between  and  to be positive or negative ? Would you expect the correlation between  and  to be positive or negative ? Explain. What is the correlation between  and  and  and  , in the sample ? I would expect the correlation between  and  to be negative - that prices will be higher in areas where there is less pollution or lower  re fl ecting consumer’s demand. People would be willingness to pay more for cleaner air and a nice environment, which in turn implies a negative association between  and  - which is a measure of air pollution. By similiar reasoning, I would also expect a negative association between  and  - which is a local area ’bad.’ High crime areas are undesirable, so people would be willing to buy houses in higher crime areas only if the price is lower. From the sample of data, the raw correlations are  (   ) = 0 35732 , and  (   ) = 0 34937 . Simple Regression Model (3) Consider the simple regression model: log(  ) = 0 + 1 log(  ) + (1) What is the in- terpretation of the coe cient 1 in the model ? What is the interpretation of the coe cient in (1) ? Explain. The coe cient 1 represents the change in expected log(  ) due to a one unit change in log(  ) . Given the log-log functional form, 1 is also the elasticity of expected  with respect to  . The coe cient 0 is expected log(  ) when log(  ) is equal to 0 [note: it is not d log(  ) when  = 0 ].

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