tailieunhanh - Property Valuation Modeling and Forecasting_9

Tham khảo tài liệu 'property valuation modeling and forecasting_9', tài chính - ngân hàng, đầu tư bất động sản phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Cointegration in real estate markets 395 There are similarities but also differences between the two error correction equations above. In both equations the error correction term takes a negative sign indicating the presence of forces to move the relationship back to equilibrium and it is significant at the 1 per cent level. For the rent-GDP equation the adjustment to equilibrium is per cent every quarter - a moderate adjustment speed. This is seen in figure where disequilibrium situations persist for long periods. For the rent-employment error correction equation the adjustment is higher at per cent every quarter - a rather speedy adjustment nearly 50 per cent every year . An interesting finding is that kGDP is highly significant in equation whereas kEMP in equation is significant only at the 10 per cent level. Equation has a notably higher explanatory power with an adjusted R2 of compared with for equation . The results of the diagnostic checks are broadly similar. Both equations have residuals that are normally distributed but they fail the serial correlation tests badly. Serial correlation seems to be a problem as the tests show the presence of serial correlation for orders 1 2 3 and 4 results for orders 1 and 4 only are reported here . Both equations fail the heteroscedasticity and RESET tests. An option available to the analyst is to augment the error correction equations and attempt to rectify the misspecification in the equations and in this way. We do so by specifying general models containing four lags of kGDP in equation and four lags of kEMP in equation . We expect this number of lags to be sufficient to identify the impact of past GDP or employment changes on rental growth. We subsequently remove regressors using as the criterion the minimisation of AIC. The kGDP and kEMP terms in the final model should also take the expected positive signs. For brevity we now focus on