A Cautionary Note on the Use of Accounting Semi-Identity-Based Models

Francisco Javier Sánchez-Vidal ()
Additional contact information
Francisco Javier Sánchez-Vidal: Departamento de Economía, Contabilidad y Finanzas, Facultad de Ciencias Empresariales, Universidad Politécnica de Cartagena, 30201 Cartagena, Spain

JRFM, 2023, vol. 16, issue 9, 1-14

Abstract: This study employs a Monte Carlo simulation to see whether accounting identity problems are present in the Fazzari, Hubbard, and Petersen model (1988). The Monte Carlo simulation generates 50,000 random cash flows, Tobin’s Q, and error term variables, which shape an investment variable that is dependent on them. Cash flows and investments are linked by a partial accounting identity, also known as an accounting semi-identity (ASI). An accounting identity is, for example, an equality between the left and right sides of a balance sheet. An ASI is not a complete one since one or more components of the accounting identity are missing. The estimated coefficients of an ASI do not represent reality, according to the OLS estimations. The regression tells us less about causality the closer the data are to the accounting identity. This is the first time that the biases of OLS estimations in an ASI-based model have been demonstrated.

Keywords: Monte Carlo simulation; accounting identities; accounting semi-identities; investment-cash flow sensitivity (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/16/9/389/pdf (application/pdf)
https://www.mdpi.com/1911-8074/16/9/389/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:16:y:2023:i:9:p:389-:d:1229472

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().