Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos 12 pptx

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

... vacancy or measures of demand) have confined empirical investigations to standard regression models, which make lesser demands on the data 72 50 Real Estate Modelling and Forecasting Table 3.1 Summary ... correction and we would divide by N rather than N − 1. 68 Real Estate Modelling and Forecasting 1,200 1,000 800 600 400 (a) Index of US income returns (c) All-property...

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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

... MacGregor and White, 2002). Employment in business and finance is a proxy for business conditions among firms occupying office space and their demand for office 88 Real Estate Modelling and Forecasting 4.8.2 ... OLS by setting z t = 1 x t and regressing y on a constant and z. Clearly, then, a surprisingly var- ied array of models can be estimated using OLS by making suitab...

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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

... x 2 and not x 1 .So,whereis x 1 ? In fact, it is the constant term, usually represented by a column of ones of length T : x 1 = ⎡ ⎢ ⎢ ⎢ ⎣ 1 1 . . . 1 ⎤ ⎥ ⎥ ⎥ ⎦ (5.3) 112 Real Estate Modelling and ... that in section 5.9 – i.e. the true DGP is represented by y t = β 1 + β 2 x 2t + β 3 x 3t + β 4 x 4t + u t (5.50) 128 Real Estate Modelling and Forecasting 5.8.2 Determ...

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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

... 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 Forecasting of data at hand, ... of the RESET, heteroscedasticity and autocorrelation tests. Equally, a small number of large outliers could cause non-normality and 172 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

... per cent level of significance is F 12, 20 = 2.28.The 212 Real Estate Modelling and Forecasting As a result, the equilibrium real rent varies through time with the real risk-free rate. The author ... office tenant demand, the ratio of government employment over the sum of the financial, insurance and real estate and service office tenants and the level of occupied stock...

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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

... the United Kingdom and Australia and assess the improvement over 292 Real Estate Modelling and Forecasting employment at lags 1 and 3), new construction lagged five periods and the longer leading ... employment and 274 Real Estate Modelling and Forecasting of UK office construction efficiently incorporates all available information, including that contained in the p...

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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

... of tables 10.4 and 10.6, it 350 Real Estate Modelling and Forecasting Running the causality tests, in our case, it is interesting to study whether SPY, 10Y and CBY lead ARPRET and, if so, whether ... squares ● two-stage least squares 358 Real Estate Modelling and Forecasting reflect only the variation in real estate returns. This series, denoted PROPRES, is the...

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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

... figure 12. 6 plots the value of an autoregressive process of order 1 with different values of the autoregressive coefficient as given by (12. 4). 382 Real Estate Modelling and Forecasting 12. 2 Cointegration In ... 0.00); RESET: 4 .12 (p = 0.04). 372 Real Estate Modelling and Forecasting and the trend-stationary process, so-called because it is stationary around a lin...

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Real Estate Modelling and Forecasting By Chris Brooks_1 doc

Real Estate Modelling and Forecasting By Chris Brooks_1 doc

... measurement error and revi- sions (e.g. absorption data are subject to stock and vacancy rate revisions 42 Real Estate Modelling and Forecasting Box 3.1 Time series data in real estate Series Frequency Rents ... Real Estate Modelling and Forecasting including range, quartiles, variance, standard deviation, semi-standard deviation and the coefficient of variation;...

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Real Estate Modelling and Forecasting By Chris Brooks_2 potx

Real Estate Modelling and Forecasting By Chris Brooks_2 potx

... variate can be scaled to have zero mean and unit variance by subtracting its mean and dividing by its standard deviation. 74 Real Estate Modelling and Forecasting 4.3 Regression versus correlation All ... variables Regressand Regressors Effect variable Causal variables Explained variable Explanatory variables Left-hand side (LHS) variable Right-hand side (RHS) variables 52...

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