Use of Machine Learning in Econometrics
Machine learning, one of the most significant technological innovations of current types, has taken rapid strides in application and adaptability in recent times. Today, sophisticated machine learning algorithms are extensively used across a plethora of industries like e-commerce. Retail, pharma, petrochemical, etc., to drive productivity and reduce costs. Since there are several facets of machine learning, such as automation of processes, self–correcting algorithms, increased data security, etc., it is safe to say that the implementation of machine learning has become a necessity rather than a luxury.
Types of Machine Learning for Econometrics
Machine learning workflows and algorithms can be broadly classified as supervised and unsupervised. The various categories of machine learning algorithms are discussed as follows:
· Supervised Machine Learning
As the name suggests, in this technique is data is first classified and labeled and then fed into the algorithm. Hence as the data is cleansed and sometimes highly structured, the algorithm is only supposed to perform analytical operations to generate results. For such a system to work, the algorithm must handle such data, or the model is pre-fed into the algorithm for it to carry out the desired operations. The algorithm can also perform corrections on the data set and suggest the range, scope, or quantification of the data set to gain the desired results.
· Unsupervised Machine Learning
In this technique, the data fed to the algorithm is neither labeled nor classified. Hence the outcome is entirely dependent upon how the algorithm perceives and analyzes the data. It might use any experience to draw inference from the data or develop an entirely new model to interpret and process the data. In this method, the output can vary to a great deal and might even be garbage or junk value. On the flip side, unsupervised machine learning algorithms can throw up new solutions and open up a new perspective in terms of handling that data set.
· Semi-supervised Machine Learning
As inferred by the name in this scenario, certain aspects of data or specific sections of the data set are labeled or classified. This leads to a kind of semi-controlled environment and can lead to results on a predictable line. In fact, for most algorithms, the semi-supervised method is followed as classifying, and labeling data has two issues. First, it is time-consuming, and secondly, it incorporates a sense of bias depending upon how the data is labeled and classified. Such a preference might lead to a biased result, thus defeating the purpose of building an algorithm.
However, on the other hand, having a wholly unlabeled and unclassified dataset might lead to erroneous results, thus resulting in a dead-end. Hence caught between the devil and deep blue sea, semi-supervised techniques help create a middle path and ensure that the algorithm is developed, operated, and conducted fruitfully. Though not wholly error-proof, it does reduce the chance of error while ensuring that in further iterations, the remaining fallacies are removed.
Machine Learning for Econometrical Models
In econometrics that is concerned with developing models that can relate certain variables to predict given outcomes, machine learning is extensively used. It is used to create predictive and self-correcting models. Given that machine learning allows multiple iterations with predictions getting better with each iteration, it is used in econometrics to build on empirical models and give a whole structure to the hypothesis and assumptions. There is two different types of econometrical models which are derived out of machine learning and its applications.
· Predictive Econometric Model using Machine Learning
In econometric, the model must give tangible, feasible, and applicable predictions. A successful econometric model must highlight the changes in the outcome if one or some of the variables are altered in a calibrated manner. So, for example, based on the price of the commodity, a firm wants to predict the consequent demand for it. Using machine learning, the algorithm on the econometric model can be built. It can take into account various factors affecting the need for the product. The machine-learning algorithm based on the data set can predict the influence of the price of the demand for the product. It can then be used to predict how the fluctuations in price would affect the market. Hence machine learning makes the econometric models more responsive and accurate.
· Self-correcting Econometric Models
In econometric, it is not always possible to determine the impact of each variable on the outcome. Moreover, sometimes, the interdependence and co-relation among variables are also not evident and require extensive testing. In such a scenario, the result might be skewed as certain factors might have been wrongly incorporated, or there was some bias in the data set. Machine learning serves a panacea for such scenarios as it pints the error in the economic model and how the same can be bettered. Using past examples and data set, the econometric model can be self-corrected. This would also gauge the extent of each variable's impact on the outcome and help assign the right weightage to each factor.
For example, a firm might think that price is the most crucial factor in determining the demand for its product or services. However, after individual iterations, the machine learning algorithm suggests that other factors like the price of substitutes, bundling of products, etc., are equally important, and the company should not focus on pricing alone. Hence the machine learning algorithm can help in course correction by making the econometric model more inclusive.
It might also help in figuring the quantitative importance of price in determining the demand for its product and service. Hence in econometric terms, machine learning can lead to optimum utilization of resources and identification of the right economic variables in deciding the outcome. Using machine learning, the econometric model can highlight other factors that impact the outcome proportionately.
Machine Learning has become an indispensable part of econometrical modeling. It is very useful and has a wide range of applications. Hence machine learning algorithms and their various application form the very heart of building a more predictive, accurate, analytical, and self-correcting econometric model.
Author: Frank Taylor