The selection of a certain methodology should pass the same quality tests as developing economic theories and selecting samples. Without strong intuition, researchers should choose the methodology that needs the least amount of human inputs. A good example is the machine-learning method that uses computerized algorithms to discover the knowledge (pattern or rule) inherent in data. Advances in modeling technology such as artificial intelligence, neural network, and genetic algorithms fit in this category. The beauty of this approach is its vast degree of freedom. There are none of the restrictions, which are often explicitly specified in traditional, linear, stationary models.

Of course, researchers should not rely excessively on the power of the method itself. Learning is impossible without knowledge. Even if you want to simply throw data into an algorithm and expect it to spit out the answer, you need to provide some background knowledge, such as the justification and types of input variables. There are still numerous occasions that require researchers to make justifiable decisions. For example, a typical way of modeling stock returns is using the following linear form,



  • ERit = excess return for the ith security in period t
  • Fjit−1 = jth factor value for the ith security at the beginning period t
  • bkt = the market-wide payoff for factor ...

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