The problem of using accounting semi-identity-based (ASI) models in Econometrics can be severe in certain circumstances, and estimations from OLS regressions in such models may not accu-rately reflect causal relationships. This dataset was generated through Monte Carlo simulations, which allowed for precise control of a causal relationship. The selected model for testing the in-fluence of the ASI problem is the Fazzari, Hubbard, and Petersen (1988) model, which seeks to establish a relationship between a company’s investments and its cash flows, and which is an ASI as well. The dataset included randomly-generated independent variables (cash flows and Tobin’s Q) to analyse how they influence the dependent variable (cash flows). The Monte Carlo meth-odology in Stata enabled repeated sampling to assess how ASIs affect regression models, high-lighting their impact on variable relationships and the unreliability of estimated coefficients. The purpose of this paper is twofold: its first goal is to provide a deeper explanation of the syntax in the related article, offering more insights into the ASI problem. The openly available dataset supports replication and further research on ASIs' effects in economic models and can be adapted for other ASI-based analyses, as the information comprised in the reusability examples prove. Second, our aim is to encourage research supported by Monte Carlo simulations, as they enable the modeling of a comprehensive ecosystem of economic relationships between variables. This allows researchers to address a variety of issues, such as partial correlations, heteroskedasticity, multicollinearity, autocorrelation, endogeneity, and more, while testing their impact on the true value of coefficients.