Develop a regression model in terms of a list of explanatory variables used to explain New Housing Starts

Develop a regression model in terms of a list of explanatory variables used to explain New Housing Starts

You and your team are required to develop a regression model in terms of a list of explanatory variables used to explain New Housing Starts. The model should be robust and BLUE. Your independent variables are:
US seasonally adjusted civilian unemployment rate
US seasonally adjusted M2 money supply
New home mortgage yield
Prime Rate charged by banks
Real GDP
1. Determine the question of interest.
2. Discuss the appropriate economic theories relating to this question of interest that may explain the expected relationship between New Housing Starts and the independent variables.
3. Using the above theory express the economic and econometric model, discuss what the
differences are between these two models.
4. Data collection.

–  Define the type of data used, e.g. cross-sectional, time series, pooled cross-sectional, panel data.
–  Discuss the various limitations to using economic data of this form.

5. Regression Analysis.

–  Define and briefly explain, using references, the six Gauss-Markov Assumptions that should be in place before running a regression, why are these assumptions important?
–  Using E-Views, perform a regression analysis on the ERP data, you will need to determine the values for the parameters, calculate R2 and the adjusted R2.

–  Explain what each of the parameters indicates with reference to ‘ceteris paribus’ and the ‘partial effect’.

–  Explain what the values of R2 and the adjusted R2 indicate about our sample of data.
–  Discuss what the implications would be if we omitted one of the independent variables with reference to the estimated parameters.
–  Interpret the standard errors and t-values for each of the parameters; explain what each of these terms mean with reference to your results.
–  Interpret the F-statistic of your regression explaining suitability of your model.
–  Determine whether the error terms are normally distributed by performing the Jarque-Bera test in E-Views. Interpret your results and discuss the advantages and limitations of the test.
–  Using the appropriate test in E-Views determine whether multicollinearity is present within the ERP data. Interpret the results and discuss the advantages and limitations of the test.

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