Table 4
Granger causality test results for industrial property
Δ tbi |
Δ reit |
Explanatory variable |
ΔD |
Adj. RP2P | ||
ΔIRN |
Δ S |
Δ SE | ||||
Pairwise model | ||||||
■g ω Δ ind_tbi .52 |
.00 |
.54 | ||||
■8 5 Δ ind reit .07 eg — о Й |
.45 |
.56 | ||||
Multiple variable model | ||||||
Δ indtbi .20 |
.00 |
.26 |
.99 |
.97 |
.91 |
.54 |
Δ ind reit .19 |
.34 |
.80 |
.53 |
.07 |
.11 |
.61 |
The table shows the p-values in the tests. The null hypothesis is that of no Granger causality between the
variables. The models include one lag and two dummy variables that take the value one in a single period
(1995Q2, 2008Q4) and are zero otherwise.
Table 5 Granger causality test results for offices
Δ tbi |
Δ reit |
Explanatory variable |
ΔD |
Adj. RP2P | |||
ΔIRN |
Δ S |
Δ SE | |||||
Pairwise model | |||||||
й O <υ əa й S |
Δ of_tbi .00 |
.03 |
.22 | ||||
Δ ofreit .92 |
.00 |
.32 | |||||
Multiple variable model Δ of_tbi .14 |
.00 |
.02 |
.22 |
.58 |
.03 |
.28 | |
Δ ofreit .70 |
.40 |
.70 |
.50 |
.07 |
.12 |
36 |
The table shows the p-values in the tests. The null hypothesis is that of no Granger causality between the
variables. The models include one lag and one dummy variable that takes the value one in a single period
(2008Q4) and is zero otherwise.
Table 6 Granger causality test results for retail property
Explanatory variable |
Adj RP2P | |||||||||
Δ tbi |
Δ reit |
INF |
ΔIR |
Δ S |
Δ SE |
ΔD |
Δ GDP | |||
O ð S Q |
Pairwise model | |||||||||
Δ retbi Δ re reit |
.15 .96 |
.07 .00 |
.41 .34 | |||||||
Multiple variable model | ||||||||||
Δ retbi |
.28 |
.04 |
.08 |
.64 |
.21 |
.04 |
.31 |
.01 |
.47 | |
Δ re reit |
.39 |
.50 |
.14 |
.58 |
.19 |
.51 |
.01 |
.09 |
.46 |
The table shows the p-values in the tests. The null hypothesis is that of no Granger causality between the
variables. The models include one lag and two dummy variables that take the value one in a single period
(1995Q2, 2008Q4) and are zero otherwise. Due to heteroscedasticity of the residuals in the model for Δrereit,
the p-values for Δ rereit in the multiple variable model are based on a covariance matrix that is computed
allowing for heteroscedasticity as in White (1980).
28
More intriguing information
1. Gianluigi Zenti, President, Academia Barilla SpA - The Changing Consumer: Demanding but Predictable2. A Multimodal Framework for Computer Mediated Learning: The Reshaping of Curriculum Knowledge and Learning
3. Review of “From Political Economy to Economics: Method, the Social and Historical Evolution of Economic Theory”
4. The name is absent
5. Multimedia as a Cognitive Tool
6. Voluntary Teaming and Effort
7. Party Groups and Policy Positions in the European Parliament
8. Should Local Public Employment Services be Merged with the Local Social Benefit Administrations?
9. Auctions in an outcome-based payment scheme to reward ecological services in agriculture – Conception, implementation and results
10. The Importance of Global Shocks for National Policymakers: Rising Challenges for Central Banks