By Giuseppe Arbia (auth.)
This booklet goals at assembly the starting to be call for within the box through introducing the fundamental spatial econometrics methodologies to a wide selection of researchers. It presents a realistic advisor that illustrates the potential for spatial econometric modelling, discusses difficulties and options and translates empirical results.
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Additional info for A Primer for Spatial Econometrics: With Applications in R
2. Suppose we have a variable y = regional consumption, and variable x = regional income, both expressed in thousands of dollars and assume further the regression model y = bx + e. What are the consequences on the OLS estimator of b (say bˆ) of expressing the income The Classical Linear Regression Model 23 in millions of dollars instead? What happens if the consumption is also expressed in millions of dollars? 3. What are the crucial elements to consider if we want to judge the goodness of fit of the empirically observed data to our theoretical model?
3. What are the crucial elements to consider if we want to judge the goodness of fit of the empirically observed data to our theoretical model? 4. In Instrumental Variable (IV) estimation how can we prove that an instrument is relevant? 5. In time series regression modeling we test autocorrelation in the residuals using the Durbin-Watson test statistics defined by d = ∑ t =2 [ et − et −1 ] T 2 ∑ t =1 et2 T t = 1,…, T. Such a test exploits the natural ordering of the time observations which is evident in the expression observing that the time index, t ranges between 1 and T.
Compare the results obtained for model 1, model 2 and model 3. Which is the preferred model in terms of fit to the empirical data? What elements did you take into consideration when choosing the preferred model? 4. Regress labor productivity on business birth rate (model 4). 5. Calculate the residuals of model 1. 6. Test the hypothesis of normality and of homoscedasticity of model 1 under the hypothesis that the innovations are not correlated. 7. Observe the geographical distribution of the residuals of model 1 with reference to the map shown in the figure here below.