Exploring spatiotemporal dynamics in a housing market using the spatial vector autoregressive Lasso: A case study of Seoul, Korea

2019 ◽  
Vol 24 (1) ◽  
pp. 27-43 ◽  
Author(s):  
Chanwoo Jin ◽  
Gunhak Lee
Keyword(s):  
Housing Market ◽  
Spatiotemporal Dynamics ◽  
Vector Autoregressive

scholarly journals Systemic risk and spatiotemporal dynamics of the US housing market

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Hao Meng ◽  
Wen-Jie Xie ◽  
Zhi-Qiang Jiang ◽  
Boris Podobnik ◽  
Wei-Xing Zhou ◽  
...  
Keyword(s):  
Housing Market ◽  
Systemic Risk ◽  
Spatiotemporal Dynamics ◽  
The Us

scholarly journals INTER-RELATIONSHIP BETWEEN THE HOUSING MARKET AND HOUSING FINANCE SYSTEM: EVIDENCE FROM MALAYSIA

2014 ◽  
Vol 18 (2) ◽  
pp. 138-150 ◽  
Author(s):  
Rosli Said ◽  
Alaistair Adair ◽  
Stanley McGreal ◽  
Rohayu Majid
Keyword(s):  
Housing Market ◽  
Granger Causality ◽  
Granger Causality Test ◽  
Housing Finance ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Finance System

The Malaysian housing market and associated housing finance system have expanded significantly as a result of rapid urbanisation since the late 1980s. The key aspect of this paper is to analyse the inter-relationship between the housing market and housing finance system in Malaysia. The paper employs Vector Autoregressive approach and Granger Causality test to empirically investigate this inter-relationship. In Malaysia, no housing studies has actually looked into or used this approach to identify the inter-relationship between these two elements. The key findings show that there is a strong inter-relationship between the housing market and housing finance system. The direction of causality shows that there is a bi-directional relationship between the housing market and housing finance system. These inter-relationships provide evidence that sound performance of the sub-markets within the housing finance system is a determinant prerequisite of the robustness of the housing finance system, if a healthy performance of the housing market is to be achieved.

scholarly journals Buyer search and the role of broker in an emerging housing market: A case study of guangzhou

2006 ◽  
Vol 11 (6) ◽  
pp. 675-685 ◽  
Author(s):  
Siqi Zheng ◽  
Hongyu Liu ◽  
Rebecca Lee
Keyword(s):  
Housing Market ◽  
Buyer Search

scholarly journals Comparison the Error Rate of Autoregressive Distributed Lag (ARDL) and Vector Autoregressive (VAR) (Case study: Forecast of Export Quantities in DIY)

2018 ◽  
Vol 18 (2) ◽  
pp. 167-177
Author(s):  
Dewi Kusuma Ningrum ◽  
Sugiyarto Surono
Keyword(s):  
Dynamic Model ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Model Stability ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Autoregressive Distributed Lag ◽  
Integration Test ◽  
Lag Model

Forecasting is estimating the size or number of something in the future. Regression model that enters current independent variable value, and lagged value is called distributed-lag model, if it enters one or more lagged value, it is called autoregressive. Koyck method is used for dynamic model which the lagged length is unknown, for the known lagged length it is used the Almon method. Vector Autoregressive (VAR) is a method that explains every variable in the model depend on the lag movement from the variable itself and all the others variable. This research aimed to explain the application of Autoregressive distributed-lag model and Vector Autoregressive (VAR) method for the forecasting for export amount in DIY. It takes export amount in DIY and inflation data, kurs, and Indonesias foreign exchange reserve. Forecasting formation: defining Koyck and Almon distributed-lag dynamic model, then the best model is chosen and distribution-lag dynamic forecasting is performed. After that it is performed stationary test, co-integration test, optimal lag examination, granger causality test, parameter estimation, VAR model stability, and performs forecasting with VAR method. The forecasting result shows MAPE value from ARDL method obtained is 0.475812%, while MAPE value from VAR method is 0.464473%. Thus it can be concluded that Vector Autoregressive (VAR) method is more effective to be used in case study of export amount in DIY forecasting. Keywords: Koyck; Almon; Lag; Autoregressive Distributed-Lag; Vector Autoregressive;

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