|Artwork: Dr. Murad Abel|
Understanding trends of economic growth is a primary function of economists. Economic forecasts are an attempt to reach out and snap the future between one’s fingertips. Researcher by Hoogerheide, et. al. (2010) compares and contrasts various forms of Bayesian models and come to a conclusion about which are most accurate for national forecasts. Having accurate data and the proper forecasting tools has considerable influence on the type of decisions we make today to encourage higher probabilities of beneficial outcomes.
The researchers study helped determine which Bayesian model-average approaches are likely to produce a predictive density forecasting for likely events. As all forecasts to date have weaknesses using an average help mitigate some of the uncertainty. They incorporated data sets such as U.S. stock index returns, compounded monthly return of S&P 500 index, and T-Bill rate to create 516 observational data points.
Other researchers have used GNP and other economic data sets to try and determine the likelihood of various possibilities occurring. Most research has confirmed that using multiple levels of analysis on economic forecasting is likely to be more correct than relying on a single method (Marcellino, 2004). The analysis attempted to hedge the particular strengths and weaknesses of varying Bayesian models in order come to a rounded answer based on an average.
At its core, a Bayesian prediction is based on taking a belief of a hypothesis and updating it as new information becomes available. Originally developed by the 18th century mathematician and theologian Thomas Bayes the model has been improved many times and can now be used in complex calculations such as those found in economics. Theorists have taken the model much further and are becoming sophisticated in their logical projections.
As economic data becomes available it will either lend support to a hypothesis or detract from the hypothesis. The probability of the hypothesis being true is dependent on the quality of the data being available. In time-varying models posterior data becomes a priori when it occurs and fits within the hypothesis. The model takes into account new data without being static and unchanging thereby changing the probabilities of varying events occurring. It is this incorporate of new data that helps it be more accurate.
The researchers found that model averaging is beneficial in business cycle analysis and forecasting. The choice of models must be considered with proper care to cope with estimation efficiency and structural instability. This becomes even more important when weights are determined through regression analysis. The use of time-varying models produced the highest accuracy in prediction when compared with average model mixes. Both can work well together.
The research helps economic forecasters understand that relying on a single model is easy and convenient but may not be the most accurate. Using multiple models and incorporating new information when it becomes available encourages higher levels of economic forecasting accuracy. The economic environment is not static and the incorporation of new data helps decision-makers adjust their courses and actions to encourage those factors that will adjust the overall system to the most beneficial outcomes in the future.
Hoogerheide, L. et. al. (2010). Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights. Journal of Forecasting, 29