Christopher Dougherty EC220 - Introduction to econometrics (chapter 12) Slideshow: housing dynamics Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 12). [Teaching Resource] © 2012 The Author This version available at: Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample: Included observations: 45 ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS ============================================================ R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criter Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) ============================================================ 1 HOUSING DYNAMICS This sequence gives an example of how a direct examination of plots of the residuals and the data for the variables in a regression model may lead to an improvement in the specification of the regression model.

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample: Included observations: 45 ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS ============================================================ R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criter Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) ============================================================ 2 HOUSING DYNAMICS The regression output is that for a logarithmic regression of aggregate expenditure on housing services on income and relative price for the United States for the period 1959– The income and price elasticities seem plausible.

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample: Included observations: 45 ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS ============================================================ R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criter Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) ============================================================ 3 HOUSING DYNAMICS However, the Breusch–Godfrey and Durbin–Watson statistics both indicate autocorrelation at a high significance level. Breusch–Godfrey statistic: critical value of 2 (1), 0.1%, is 10.83

4 HOUSING DYNAMICS The residuals exhibit a classic pattern of strong positive autocorrelation.

5 HOUSING DYNAMICS The actual and fitted values of the dependent variable and the series for income and price have been added to the diagram. The price series was very flat and so had little influence on the fitted values. It will be ignored in the discussion that follows.

6 HOUSING DYNAMICS There was a very large negative residual in We will enlarge this part of the diagram and take a closer look.

7 HOUSING DYNAMICS In 1973, income (right scale) grew unusually rapidly. The fitted value of housing expenditure (left scale, with actual value) accordingly rose above its trend.

8 HOUSING DYNAMICS This boom was stopped in its tracks by the first oil shock. Income actually declined in 1974, the only fall in the entire sample period.

9 HOUSING DYNAMICS As a consequence, the fitted value of housing expenditure would also have fallen in In actual fact it rose a little because the real price of housing fell relatively sharply in 1974.

10 HOUSING DYNAMICS However, the actual value of housing maintained its previous trend in those two years, responding not at all to the short-run variations in the growth of income. This accounts for the gap that opened up in 1973, and the large negative residual in that year.

11 HOUSING DYNAMICS There was a similar large negative residual in We will enlarge this part of the diagram.

12 HOUSING DYNAMICS Income grew unusually rapidly in As a consequence, the fitted value of housing also grew rapidly. However the actual value of housing grew at much the same rate as previously. Hence the negative residual.

13 HOUSING DYNAMICS In the years immediately after 1984, income grew at a slower rate. Accordingly the fitted value of housing grew at a slower rate. But the actual value of housing grew at much the same rate as before, turning the negative residual in 1984 into a large positive one in 1987.

14 HOUSING DYNAMICS Finally, we shall take a closer look at the series of positive residuals from 1960 to 1965.

15 HOUSING DYNAMICS In the first part of this subperiod, income was growing relatively slowly. Towards the end, it started to accelerate. The fitted values followed suit.

16 HOUSING DYNAMICS However, the actual values maintained a constant trend. Because it was unresponsive to the variations in the growth rate of income, a gap opened up in the middle, giving rise to the positive residuals.

17 HOUSING DYNAMICS In this case, as in the previous two, the residuals are not being caused by autocorrelation. If that were the case, the actual values should be relatively volatile, compared with the trend of the fitted values.

18 HOUSING DYNAMICS What we see here is exactly the opposite. The actual values have a very stable trend, while the fitted values respond, as they must, to short-run variations in the growth of income. The pattern we see in the residuals is caused by the nonresponse of the actual values.

19 HOUSING DYNAMICS One way to model the inertia in the growth rate of the actual values is to add a lagged dependent variable to the regression model.

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample(adjusted): Included observations: 44 after adjusting endpoints ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS LGHOUS(-1) ============================================================ R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criter Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) ============================================================ ========= 20 HOUSING DYNAMICS We are now hypothesizing that current expenditure on housing services depends on previous expenditure as well as income and price. Here is the regression with the lagged dependent variable added to the model.

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample(adjusted): Included observations: 44 after adjusting endpoints ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS LGHOUS(-1) ============================================================ R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criter Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) ============================================================ ========= 21 HOUSING DYNAMICS The Durbin–Watson statistic, previously 0.63, is now quite close to 2. Of course, since we have a lagged dependent variable in the model, we should look at the h statistic instead.

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample(adjusted): Included observations: 44 after adjusting endpoints ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS LGHOUS(-1) ============================================================ R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criter Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) ============================================================ ========= 22 HOUSING DYNAMICS We calculated the h statistic for this regression in the previous sequence. It is 0.66, and so now we do not reject the null hypothesis of no autocorrelation at the 5% significance level (critical value 1.96). Strictly speaking, of course, the test is valid only in large samples.

============================================================ Dependent Variable: LGHOUS Method: Least Squares Sample(adjusted): Included observations: 44 after adjusting endpoints ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS LGHOUS(-1) ============================================================ R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criter Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) ============================================================ 23 HOUSING DYNAMICS The new equation indicates that current expenditure on housing services is determined only partly by current income and price. Previous expenditure is clearly very important as well.

============================================================ Dependent Variable: LGHOUS ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS ============================================================ Durbin-Watson stat Prob(F-statistic) ============================================================ Dependent Variable: LGHOUS ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS LGHOUS(-1) ============================================================ Durbin-Watson stat Prob(F-statistic) ============================================================ 24 HOUSING DYNAMICS The apparent autocorrelation exhibited by the residuals in the plot, and the resulting low value of the d statistic in the original regression, were thus attributable to the omission of an important variable, rather than to the disturbance term being subject to an AR(1) process.

============================================================ Dependent Variable: LGHOUS ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS ============================================================ Durbin-Watson stat Prob(F-statistic) ============================================================ Dependent Variable: LGHOUS ============================================================ Variable Coefficient Std. Error t-Statistic Prob. ============================================================ C LGDPI LGPRHOUS LGHOUS(-1) ============================================================ Durbin-Watson stat Prob(F-statistic) ============================================================ 25 HOUSING DYNAMICS Note that the income and price elasticities are much lower than in the original regression. We have already seen the reason for this in the sequence that discussed the dynamics inherent in a partial adjustment model.

Copyright Christopher Dougherty These slideshows may be downloaded by anyone, anywhere for personal use. Subject to respect for copyright and, where appropriate, attribution, they may be used as a resource for teaching an econometrics course. There is no need to refer to the author. The content of this slideshow comes from Section 12.4 of C. Dougherty, Introduction to Econometrics, fourth edition 2011, Oxford University Press. Additional (free) resources for both students and instructors may be downloaded from the OUP Online Resource Centre Individuals studying econometrics on their own and who feel that they might benefit from participation in a formal course should consider the London School of Economics summer school course EC212 Introduction to Econometrics or the University of London International Programmes distance learning course 20 Elements of Econometrics