Property Valuation Modeling and Forecasting 3 pptx

Property Valuation Modeling and Forecasting_3 pptx

... (1.5)(−2.5) (3. 9)−0.001EMPj(−2.6)(7.18)Adj. R2= 0. 63; F -statistic = 11. 83; AIC = 2 .38 4; T = 33 ; White’s heteroscedasticity test(χ2version): TR2= 33 × 0.25 = 8.25;criticalχ2(10) = 18 .31 .The ... (4.0) (−2.9)(7.19)Adj. R2= 0.64; F -statistic = 15 .33 ; AIC = 2 .32 4; T = 33 ; White’s heteroscedasticity test(χ2version): TR2= 33 × 0.12 = 3. 96;criticalχ2(8) = 15.51.Again, the exclusion ... LHS:yt= 3Lyt− 2.75L2yt+ 0.75L 3 yt+ ut(8 .34 )(1 − 3L + 2.75L2− 0.75L 3 )yt= ut(8 .35 )The characteristic equation is1 − 3z + 2.75z2− 0.75z 3 = 0 (8 .36 )which, fortunately, factorises...

Property Valuation Modeling and Forecasting_4 pdf

... −0.94SDUM(2Q) 0.29 3. 53 ∗∗∗SDUM(3Q) −0.21 −2.69∗∗∗AR(1) −0 .38 −1.54AR(2) −0.27 −1.05AR (3) −0.54 3. 23 ∗∗∗MA(1) 0 .33 1. 43 MA(2) 0.05 0.20MA (3) 0.71 3. 95∗∗∗Adj. R20 .33 Sample period 1Q79–4Q07Note:∗∗∗denotes ... −1.871,2 −1.95 −1.851 ,3 −1.98 −1.891,4 −1.97 −1. 83 2,1 −1.92 −1. 83 2,2 −1.92 −1.802 ,3 −1.95 −1.812,4 −1. 93 −1.77 3, 1 −1.97 −1.85 3, 2 −1.95 −1.81 3, 3 −2.18 −2.02 3, 4 −2.15 −1.964,1 −1.98 ... expression(8A .31 ),E(yt) = E(µ + φ1yt−1) (8A .32 )E(yt) = µ + φ1E(yt−1) (8A .33 )but also byyt−1= µ +φ1yt−2+ ut−1(8A .34 )So, replacing yt−1in (8A .33 ) with the RHS of (8A .34 ),E(yt)...

Property Valuation Modeling and Forecasting_6 doc

... 6245404 034 333 129VAR(2) 8075726761 635 647AR(2) 80807981 737 57471Long-termmean4 039 4 038 3 433 3 132 (b) CB Hillier Parker rents seriesVAR(1) 7666676949 434 147AR(2) 78808179 737 87774Long-termmean4241424 034 3 533 34Note: ... 867.72AR(1) 75 .39 88.24 84 .32 92.18 88.44 89.15 80. 03 87.44Long-term mean 209.55 1 63. 42 139 .88 137 .20 139 .98 1 43. 91 150.20 154.84Random walk 198.16 132 .86 1 23. 71 149.78 132 .94 148.79 149.62 158. 13 during ... −1.556 −2.652 3. 388 −4.155 −4.6 63 −4.895 −5.505AR(2) −0.595 −0.960 −1 .31 0 −1.5 63 −1.720 −1.819 −1.748 −1.876Long-term mean −2 .39 8 3. 137 3. 8 43 −4.5 73 −5.0 93 −5.520 −5.677 −6.049Random walk 0.466...

Property Valuation Modeling and Forecasting_8 potx

... −0.07−2.64−2.60−2.55−2.59−2.56−2.51RESt−11 .31 1 .31 1 .31 RESt−10.40 0.40 0.4010.80 10.71 10.61 3. 32 3. 29 3. 27RESt−2−0.99 −0.99 −0.99 RESt−2−0 .37 −0 .37 −0 .37 −5. 43 −5 .38 −5 .31 3. 17 3. 14 3. 07RESt 3 0.72 0.72 ... RESt 3 0 .36 0 .36 0 .36 3. 96 3. 93 3.89 3. 01 2.98 2.97RESt−4−0.19 −0.19 −0.19 RESt−40.29 0.29 0 .30 −1.66 −1.65 −1.62 2.57 2.51 2.52Critical 5% −1.95 −2.91 3. 49 −1.95 −2.91 3. 49Notes: ... two unit roots, and requires differencing twice to induce stationarity. 37 6 Real Estate Modelling and Forecasting 30 2520151050–51 40 118 157 2 74 31 3 35 2 39 1 430 46979 196 235 Figure 12.5Time...

Property Valuation Modeling and Forecasting_9 ppt

... 07 1 13. 79 109.57 1 23. 80 −0.41 −1.09 −1 .36 −5.44Aug. 07 1 13. 66 109.24 1 23. 58 −0. 13 −0 .33 −0.22 −5.66Sep. 07 1 13. 68 109. 13 1 23. 60 0.02 −0.10 0.02 −6.08Oct. 07 1 13. 77 109.17 1 23. 67 0.09 0. 03 0.07 ... −0.11 53 × (−1.40) =−0.22HenceASAug−07= 1 13. 79 −0. 13 = 1 13. 66EUAug−07= 109.57 −0 .33 = 109.24USAug−07= 1 23. 80 −0.22 = 1 23. 58ECTAug−07= 87.7559 +1 13. 66 + 3. 337 1 × 109.24 − 4.6255 × 1 23. 58=−5.66This ... +0. 032 6 × (−1 .36 ) +0.0779 × (−1.62) + 0. 232 0×(−1.09) −0.0241 × (−1.40) =−0 .33 USAug−07= 0.0977 +0.0027 × (−5.44) +0.0216 × (−0.41) − 0.0229×(−0.85) −0.0 233 × (−1 .36 ) +0.0 532 × (−1.62) + 0 .38 54×(−1.09)...

Property Valuation Modeling and Forecasting_10 doc

... 32 3, 32 6, 33 0derived demand 5deterministic trend 37 5–6, 39 2development market 6Dickey–Fuller (DF) test 37 8–81, 38 7– 93 augmented (ADF) 38 0, 38 7– 93 critical values 37 9differencing 34 0, 37 3, ... 282, 34 4–5, 38 5shocks 239 , 35 2–6, 36 1, 36 9– 73, 37 8sign predictions 288–9, 299, 30 1signiﬁcance level 59–64, 95–101, 124simulations 32 0–2, 32 9, 33 2, 33 4, 37 9Singapore commercial space 30 –1, ... 67, 2 93, 426, 438 Investment Property Forum (IPF) 2 93 8, 418,425Johansen test 39 9–4 13 KPSS test 38 1kurtosis 51 3, 167–9lag length 158, 197, 201, 232 3, 32 7, 34 6, 35 1lag operator 231 3, 37 7lagged...

Tài liệu overview of data modeling and database design pptx

... topslide.Answer: 1:1 Husband and wife; taxi and driver; machine and operator; ship and dock.M:1 Orders and items; auto and occupants; plane and passengersM:M Employees and skills; products and warehouses.Have ... 101 101 102ITEM_ID 1 2 1 3 1PRODUCT_ID 10011 100 13 30 421 41010 20180 ID1001011021 03 CUSTOMER_ID 204 205 206 208DATE_ORDERED 31 -AUG-92 31 -AUG-9201-SEP-9202-SEP-92 Primary ... physical, and static. Thestandard definition of an FK is that it refers to a PK or UK.Introduction to Oracle: SQL and PL/SQL Using Procedure Builder8 32 Overview of Data Modeling and Database...

Tài liệu Useful Modeling Techniques part 3 pptx

... Example 9- 13 Generation of Positive and Negative Numbers by $random Task reg [ 23: 0] rand1, rand2; rand1 = $random % 60; //Generates a random number between -59 and 59 rand2 = {$random} % 60; ... $fopen("file1.out"); //handle1 = 32 'h0000_0002 (bit 1 set) handle2 = $fopen("file2.out"); //handle2 = 32 'h0000_0004 (bit 2 set) handle3 = $fopen("file3.out"); //handle3 = 32 'h0000_0008 ... descriptor integer handle1, handle2, handle3; //integers are 32 -bit values //standard output is open; descriptor = 32 'h0000_0001 (bit 0 set) initial begin handle1 = $fopen("file1.out");...

Currency Strategy A Practitioner s Guide To Currency Investing Hedging And Forecasting Wiley_3 pptx

... conforms to the 5 3 wave pattern and secondly that whenwe add up these sub-waves we come to 21 impulsive and 13 corrective waves, making 34 intotal. Once again, 13, 21 and 34 are all Fibonacci ... twoprevious numbers:1, 1, 2, 3, 5, 8, 13, 21, 34 , 55, 89, 144, 233 and so on . . .To some, these numbers may seem more or less random. In fact, they are actually far fromrandom, containing important ... used in other technical indicators, such as in movingaverages — e.g. 5, 13 and 21 moving averages, 21, 34 and 55 or 31 , 55 and 144. Within theﬁnancial markets, the most widely used application...

Real Estate Modelling and Forecasting Hardcover_14 pptx

... −0.690.67−0.52−2.90−5.80−5. 83 −10. 23 EUt−10 .33 0 .33 0 .31 EUt−1−0. 13 −0. 13 4.70 4.72 4.42 −1.55 −1.46EUt−20.00 0.01 −0.00 EUt−2−0. 13 −0. 13 0.00 0.08 −0.02 −1. 83 −1.78EUt 3 0. 13 0. 13 0.121.77 1.84 ... 156.4 0.8 0. 83 2Q07 87.5 157.2 0.8 2 .34 3Q07 89.1 158.2 1.0 1. 83 4Q07 89.7 159.2 −2.95 1.0 0.671Q08 90.1 160.0 −2.98 0.8 0.440 0.492Q08 90 .3 160.8 3. 34 0.8 0.115 0. 13 3Q08 90.9 161.6 3. 17 0.8 ... 0.58 3. 51 Intercept – 0.11 0.12– 0.80 1.95 – 2 .30 1. 23 Trend – – 0.00 Trend – – −0.00–– 1.78 ––−0.10USt−10.00 −0.00 −0. 03 USt−1−0.90 −0. 93 −0. 93 2. 43 −0.64−1.89−12.91− 13. 25− 13. 21Europe...

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