Forecasting House Prices in the 50 States Using Dynamic Model Averaging and Dynamic Model Selection

2014 ◽  
Author(s):  
Lasse Bork ◽  
Stig Vinther MMller
Keyword(s):  
Model Selection ◽  
Dynamic Model ◽  
House Prices ◽  
Model Averaging ◽  
Dynamic Model Averaging

scholarly journals Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices

2018 ◽  
Vol 10 (8) ◽  
pp. 2801 ◽  
Author(s):  
Krzysztof Drachal
Keyword(s):  
Model Selection ◽  
Dynamic Model ◽  
Bayesian Model ◽  
Model Averaging ◽  
Time Varying ◽  
Model Combination ◽  
Explanatory Variables ◽  
Time Varying Parameters ◽  
Dynamic Model Averaging ◽  
Commodities Prices

Forecasting commodities prices on vividly changing markets is a hard problem to tackle. However, being able to determine important price predictors in a time-varying setting is crucial for sustainability initiatives. For example, the 2000s commodities boom gave rise to questioning whether commodities markets become over-financialized. In case of agricultural commodities, it was questioned if the speculative pressures increase food prices. Recently, some newly proposed Bayesian model combination scheme has been proposed, i.e., Dynamic Model Averaging (DMA). This method has already been applied with success in certain markets. It joins together uncertainty about the model and explanatory variables and a time-varying parameters approach. It can also capture structural breaks and respond to market disturbances. Secondly, it can deal with numerous explanatory variables in a data-rich environment. Similarly, like Bayesian Model Averaging (BMA), Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model (MED) start from Time-Varying Parameters’ (TVP) regressions. All of these methods were applied to 69 spot commodities prices. The period between Dec 1983 and Oct 2017 was analysed. In approximately 80% of cases, according to the Diebold–Mariano test, DMA produced statistically significant more accurate forecast than benchmark forecasts (like the naive method or ARIMA). Moreover, amongst all the considered model types, DMA was in 22% of cases the most accurate one (significantly). MED was most often minimising the forecast errors (28%). However, in the text, it is clarified that this was due to some specific initial parameters setting. The second ”best” model type was MED, meaning that, in the case of model selection, relying on the highest posterior probability is not always preferable.

scholarly journals Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?

2019 ◽  
Vol 12 (2) ◽  
pp. 93 ◽  
Author(s):  
Camilla Muglia ◽  
Luca Santabarbara ◽  
Stefano Grassi
Keyword(s):  
Model Selection ◽  
Dynamic Model ◽  
Good Predictor ◽  
Model Averaging ◽  
Direct Impact ◽  
Alternative Models ◽  
Density Forecast ◽  
Dynamic Model Averaging

The paper investigates whether Bitcoin is a good predictor of the Standard & Poor’s 500 Index. To answer this question we compare alternative models using a point and density forecast relying on Dynamic Model Averaging (DMA) and Dynamic Model Selection (DMS). According to our results, Bitcoin does not show any direct impact on the predictability of Standard & Poor’s 500 for the considered sample.

Forecasting Interconnections in International Housing Markets: Evidence from the Dynamic Model Averaging Approach

2020 ◽  
Vol 42 (1) ◽  
pp. 37-103
Author(s):  
Hardik A. Marfatia
Keyword(s):  
Dynamic Model ◽  
Model Averaging ◽  
Housing Markets ◽  
The United States ◽  
Forecasting Model ◽  
Change Over Time ◽  
The United Kingdom ◽  
Novel Approach ◽  
Dynamic Model Averaging ◽  
Over Time

In this paper, I undertake a novel approach to uncover the forecasting interconnections in the international housing markets. Using a dynamic model averaging framework that allows both the coefficients and the entire forecasting model to dynamically change over time, I uncover the intertwined forecasting relationships in 23 leading international housing markets. The evidence suggests significant forecasting interconnections in these markets. However, no country holds a constant forecasting advantage, including the United States and the United Kingdom, although the U.S. housing market's predictive power has increased over time. Evidence also suggests that allowing the forecasting model to change is more important than allowing the coefficients to change over time.

scholarly journals Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Siqi Xu ◽  
Yifeng Zhang ◽  
Xiaodan Chen
Keyword(s):  
Carbon Dioxide ◽  
Dynamic Model ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Predictive Power ◽  
Carbon Dioxide Emission ◽  
Model Averaging ◽  
Predictive Ability ◽  
Time Varying ◽  
Dynamic Model Averaging

Although energy-related factors, such as energy intensity and energy consumption, are well recognized as major drivers of carbon dioxide emission in China, little is known about the time-varying impacts of other macrolevel nonenergy factors on carbon emission, especially those from macroeconomic, financial, household, and technology progress indicators in China. This paper contributes to the literature by investigating the time-varying predictive ability of 15 macrolevel indicators for China’s carbon dioxide emission from 1982 to 2017 with a dynamic model averaging (DMA) method. The empirical results show that, firstly, the explanatory power of each nonenergy predictor changes significantly with time and no predictor has a stable positive/negative impact on China’s carbon emissions throughout the whole sample period. Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. Interestingly, those Chinese household features, such as Engel’s coefficient and household savings rate, play very important roles in the prediction of China’s carbon emission. In addition, we find that IP are losing its predictive power in recent years, while the proportion of value-added of the service sector in GDP presents not only a leading forecasting weight, but a continuous increasing prediction power in recent years. Finally, the dynamic model averaging (DMA) method can produce the most accurate forecasts of carbon emission in China compared to other commonly used forecasting methods.

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