USING INTERPOLATION FOR GENERATING INPUT DATA FOR THE GROSS DOMESTIC PRODUCT MONTE CARLO SIMULATION

Abstract

Input modelling is a complex task within the Monte Carlo simulation, especially when the systems and processes under investigation reveal the non-linear behavior of several interdependent variables. Commonly used approaches for Monte Carlo simulation input modelling include selecting probability distributions and fitting them to existing data; resampling random variates from historical data; and using real-world data as an input model, which in the age of big data becomes more feasible. Each of the approaches comes with its own set of drawbacks. This note aims to describe a new method of input modelling for GDP Monte Carlo simulation based on interpolation of the GDP historical records. Also, this method has been implemented as a publicly available online tool using the Microsoft Azure Machine Learning Studio. A similar approach can be applied to other macroeconomic indicators, e.g., consumer price index (inflation) or current employment statistics. This note is intended for economists, data scientists, and operations research analysts interested in the GDP Monte Carlo simulation. It can also be used by academics, researchers, and practitioners in a broad subject area for generating input data for Monte Carlo simulation. Specifically, it can be of interest for Ph.D. candidates (VAC specialty 5.2.6) performing development of theory and methods of decision-making in economic and social systems, and application of artificial intelligence and big data methods in management.