MODELLING AND FORECASTING CONSUMERS’ EXPENDITURE
This assignment gives you a chance to apply the econometric theory you have Econometrics to the challenging task of modelling and forecasting consumers’ expenditure.
Using Stata and the data file Cons 1976-2019.xlsx, estimate the following initial model for the subperiod 1976–2016:
Ct = α + β1Yt + β2Ht + β3Ct-1 + εt (1)
where Ct is households’ real final consumption expenditure (£ million) in year t
Yt is households’ real disposable income (£ million) in year t
Ht is the Nationwide real house price index in quarter 2 of year t (£ per house, 2019Q2 prices)
εt is a random error term with an expected value of zero in year t
Using your log, paste in your initial results from Stata, along with a correlation matrix and summary table with means, standard deviations.
Offer a brief rationale for the inclusion of the explanatory variables in model (1). Comment on your findings, referring particularly to the signs, values and statistical significance of the estimated coefficients. Be sure to explain how to interpret the values of the regression coefficients. How satisfactory is your initial model? You should play close attention to any problems of serial correlation, multicollinearity and heteroscedasticity, as well as to possible specification errors. Illustrate your answer with appropriate graphic output from Stata.
Now consider how you might refine your initial model. You should present the results for four models in a summary table (including your initial one in the first column) and explain how and why you refined your initial model. You should follow the ‘general-to-specific’ (g-to-s) modelling methodology. You can use the Cons 1976-2019.xlsx data file as a source of data on additional variables. For consistency, all models must be fitted to data for 1976–2016. In your summary table, show standard errors in brackets beneath the regression coefficients, followed by t ratios in bold type and then p values. For each model, report R2,
, D.W. and χ2 test statistics for serial correlation and heteroscedasticity (along with p values).
Using your preferred model, forecast consumers’ expenditure from 2017 to 2019. How accurate are the forecasts? Provide a plot of actual and fitted values of Ct. Try to account for any discrepancies between observed and forecasted expenditure.
Outline what improvements you think could be made to your preferred model if you had more time to devote to the modelling of consumers’ expenditure in the UK.
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