TITLE:
Estimation of Long-Term Profitability of Startups: An Experimental Analysis
AUTHORS:
Erkki K. Laitinen, Teija Laitinen
KEYWORDS:
Startups, Long-Term Profitability, IRR, Distributed Lag Model, Growth, Koyck Transformation, Experimental Data, Estimation Methods
JOURNAL NAME:
Theoretical Economics Letters,
Vol.12 No.6,
December
29,
2022
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.