Estimation of Long-Term Profitability of Startups: An Experimental Analysis

Abstract

The objective is to assess the performance of different methods to derive an estimate of internal rate of return (IRR) for startups. Koyck transformation is first used to estimate the parameters of a distributed revenue lag model which are then used to derive IRR. For estimation different scenarios of artificial time series of expenditure and revenue are constructed to describe the early years of startups. These scenarios are based on different parameter values of the distributed lag function and are classified into nine experiments. The performance of the following six different estimation methods are compared with each other in these nine experiments: unrestricted OLS, OLS through the origin (RTO), restricted OLS, Least Absolute Deviation (LAD), Ridge Regression (RR), and restricted Maximum Likelihood (ML). The experimental results indicate that the most efficient estimation method is the Ordinary Least Squares (OLS) method where the regression is forced through the origin (RTO). The least efficient method is the unrestricted OLS, which emphasizes the importance of RTO.