Gupta and
Kabundi (2010) started with an interesting question on which macroeconomic
models are most likely to predict economic growth and success. Decision-makers
that have tools are better able to make current decisions that are likely to
foster greater growth in the future. The researcher used emerging markets of
South Africa but these same models may apply to economic hubs and the factors
that predict their success.
Models are
simply explanations that attempt to predict activities within the environment.
Some models are more successful than others. Success is determined through a
process of validity where multiple researchers over a period of time analyze
the same phenomenon over and over in multiple ways to determine if the model
makes sense.
Common
data points in measuring economic development include per capita growth rate,
consumer price index (CPI), inflation, the money market rate, and the growth
rate of nominal effective exchange rates. These data points often work their
way into various models in an effort to create and develop some predictability.
Bayesian
VAR (BVARs) are based upon the Bayesian Method which is a subjective
probability analysis used in a number of different fields. It is a rational
decision making regression analysis for updating beliefs. In economics, the
methods use monthly, yearly and other time based measurements to help determine
the vector and trajectory of actions. It provides a method of blending new
information with prior beliefs.
BVAR
models incorporate a greater amount of data than a number of other common
models. The authors found that the BVARs have more predictability and would be
beneficial for evaluating economic growth. Administrators that consider these
models may find an additional tool for understanding and managing economic
hubs.
Gupta, R.
& Kabundi, A. (2010). Forecasting macroeconomic variables in small open
economy: a comparison between small-and large-scale models. Journal of Forecasting, 29 (2).
