Bayesian Model Averaging of Space Time Car Models with Application to U.S. House Price Forecasting
The housing market has been a significant contribution to U.S. GDP. Forecasting the house price growth rate helps to regulate risks associated with the housing sector and further helps to stabilize the economy. However, due to the volatility in the housing market, forecasting the house price growth rate has been a tough task. In this thesis, we built a conditional autoregressive model incorporated with bayesian model averaging (BMA-CAR) based on quarterly observations from 1976 to 1994 and tested forecasting capability over 1995 to 2012. We extended upon the results of Bork [International Journal of Forecasting, 31, 1 (2015)] to include the effects of spatial autocorrelation but inhibited the allowance for the model and coefficients shifts over time. Our model is based on a hierarchical structure that allows BMA to average out the effects from predictors along with CAR model to account for the remaining spatial structures in the data. ^
"Bayesian Model Averaging of Space Time Car Models with Application to U.S. House Price Forecasting"
Dissertations and Master's Theses (Campus Access).