Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos 3 doc

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_2 pot

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_2 pot

... 9.9 13. 3 11 .3 13. 3 Min 3. 7 −2.0 Max 11 .3 13. 3 N 16 16 Q 1 4 .3 (4th) = 0.8 4 .3 (4th) = 2.1 Q 3 12.8 (13th) = 9.9 12.8 (13th) = 9.5 IQR 9.1 7.4 µ 4.9 5.8 σ 2 23. 0 17.8 σ 4.8 4.2 CV 0.98 0. 73 Source: ... α + βx (4.1) Real estate analysis: statistical tools 69 25 20 15 10 5 0 –5 –10 –15 47 931 139 185 231 277 32 3 36 9 415 461 507 558 599 645 691 737 7 83 829 875 921 967...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_3 doc

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_3 doc

... by ˆ β and adding ˆα will 84 Real Estate Modelling and Forecasting 20 (yoy %) Actual Fitted (%) 15 10 5 0 1979 1981 19 83 1985 1987 1989 1991 19 93 1995 1997 1999 2001 20 03 2005 1979 1981 19 83 1985 1987 1989 1991 19 93 1995 1997 1999 2001 20 03 2005 −5 −10 −15 −20 (a) ... MacGregor and White, 2002). Employment in business and finance is a proxy for business conditions...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_4 ppt

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_4 ppt

... case of (5 .31 ) and (5 .32 ), the relevant encompassing model would be y t = γ 1 + γ 2 x 2t + γ 3 x 3t + w t (5 .33 ) where w t is an error term. Formulation (5 .33 ) contains both (5 .31 ) and (5 .32 ) as ... but γ 3 is not. In this case, (5 .33 ) collapses to (5 .31 ), and the latter is the preferred model. (2) γ 3 is statistically significant but γ 2 is not. In this case, (5 .3...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_6 potx

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_6 potx

... ρx 2t−1 ) +β 3 (x 3t − ρx 3t−1 ) +ρy t−1 + v t (6A.8) Expanding the brackets around the explanatory variable terms gives y t = (1 − ρ)β 1 + β 2 x 2t − ρβ 2 x 2t−1 + β 3 x 3t − ρβ 3 x 3t−1 + ρy t−1 + ... denoted by T 1 (even though it may come second). The test statistic is given by test statistic = RSS −RSS 1 RSS 1 × T 1 − k T 2 (6.61) 190 Real Estate Modelling and Forecast...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_7 doc

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_7 doc

... Bera–Jarque test) and the form of the equation with the RESET test. Normality test: BJ = 33  0.15 2 6 + (3. 42 − 3) 2 24  = 0 .37 214 Real Estate Modelling and Forecasting is determined by Akaike’s ... 13 13 27 14 13 F -statistic 1.00 1.56 Crit. F(5%) F (3, 20) at 5% ≈ 3. 10 F (3, 21) at 5% ≈ 3. 07 Notes: The dependent variable is RRg t ; cell entries are coefficient...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_9 doc

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_9 doc

... 533 .1 1 20 03 0.056 5.7 −18.00 −26.60 1 31 .20 39 .79 1241.4 1 2004 0.618 3. 4 − 13. 30 −22.14 1 −26.49 35 .33 935 .8 1 2005 0.8 93 0.1 3. 64 − 13. 19 1 −16. 83 −26 .38 444.0 1 2006 2 .37 8 −0.2 −4.24 4 .37 ... −F Naive 20 03 −18.01 −19. 93 1.92 1.92 3. 69 32 4 .36 39 7.20 −12 .37 31 .81 2004 − 13. 30 −16.06 2.76 2.76 7.62 176.89 257.92 −12 .37 0.88 2005 3. 64 −9.77 6. 13 6. 13 3...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_11 ppt

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_11 ppt

... primarily 34 6 Real Estate Modelling and Forecasting Table 11.1 VAR lag length selection AIC value: AIC value: Lag ARPRET equation system 1 3. 397 −6.574 2 3. 395 −6.6 23 3 3. 381 −6.611 4 3. 372 −6.587 8 ... 81,705 90,5 63 3.6 21 ,30 2 90 20,5 43 185 0.81 2Q05 2.28 83, 568 90,499 3. 1 21 ,36 6 64 20,694 151 0.45 3Q05 2.46 85,625 90,564 2.7 21,415 49 20, 832 138 0 .32 4Q05 2.5...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_12 pptx

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_12 pptx

... −0.07 − 2.64 − 2.60 − 2.55 − 2.59 − 2.56 − 2.51 RES t−1 1 .31 1 .31 1 .31 RES t−1 0.40 0.40 0.40 10.80 10.71 10.61 3. 32 3. 29 3. 27 RES t−2 −0.99 −0.99 −0.99 RES t−2 −0 .37 −0 .37 −0 .37 −5. 43 −5 .38 −5 .31 3. 17 3. 14 3. 07 RES t 3 0.72 0.72 ... autocorrelated, but would be so if there was 37 6 Real Estate Modelling and Forecasting 30 25 20 15 10 5 0 –5 1 40 118 1...
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Real Estate Modelling and Forecasting By Chris Brooks_1 doc

Real Estate Modelling and Forecasting By Chris Brooks_1 doc

... 3, 790 4,149 3, 465 3, 761 3, 241 Haringey 3, 966 4,662 4,248 4, 836 4, 238 4,658 4, 534 3, 765 4, 233 3, 347 Islington 2,516 3, 2 43 3 ,34 7 3, 935 3, 075 3, 407 3, 365 2,776 2,941 2,900 Kensington and Chelsea 4,797 ... 3, 121 3, 689 3, 2 83 City of London 288 32 9 440 558 437 37 9 37 4 468 30 7 299 Hackney 2,221 2,968 3, 107 3, 266 2,840 3, 252 3, 570 2,711 3, 1...
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Real Estate Modelling and Forecasting By Chris Brooks_2 potx

Real Estate Modelling and Forecasting By Chris Brooks_2 potx

... 11 .3 5.4 5.8 7.1 2002 4.0 5.6 6.2 7 .3 20 03 2.6 5.7 9.6 8.0 2004 3. 7 2.1 9.9 9.5 2005 0.8 4.7 10.4 10.1 2006 9.6 8.0 11.1 11.7 2007 9.9 13. 3 11 .3 13. 3 Min 3. 7 −2.0 Max 11 .3 13. 3 N 16 16 Q 1 4 .3 ... α + βx (4.1) Real estate analysis: statistical tools 69 25 20 15 10 5 0 –5 –10 –15 47 931 139 185 231 277 32 3 36 9 415 461 507 558 599 645 691 737 7 83 829 875 921 967 10 13...
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