Abstract
Most previous studies investigate theoretical variables which affect the capital structure of a firm; however, these latent variables are unobservable and generally estimated by accounting items with measurement errors. The use of these observed accounting variables as theoretical explanatory latent variables will cause error-in-variable problems during the analysis of the factors of capital structure. Since Titman and Wessels (Journal of Finance 43, 1–19, 1988) first utilize LISREL system to analyze the determinants of capital structure choice based on a structural equation modeling (SEM) framework, Chang et al. (The Quarterly Review of Economic and Finance 49, 197–213, 2009) and Yang et al. (The Quarterly Review of Economics and Finance 50, 222–233, 2010) extend the empirical work on capital structure research and obtain more convincing results by using multiple indicators and multiple causes (MIMIC) model and structural equation modeling (SEM) with confirmatory factor analysis (CFA) approach, respectively.
In this chapter, we employ structural equation modeling (SEM) in LISREL system to solve the measurement errors problems in the analysis of the determinants of capital structure and find the important factors consistent with capital structure theory by using date from 2002 to 2010. The purpose of this chapter is to investigate whether the influences of accounting factors on capital structure change and whether the important factors are consistent with the previous literature.
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Appendix: Codes of Structure Equation Modeling (SEM) in LISREL System
Appendix: Codes of Structure Equation Modeling (SEM) in LISREL System
SEM Model-Titman and Wessels Paper
Observed Variables:
LT_MVE ST_MVE C_MVE LT_BVE ST_BVE C_BVE GTA CE_TA RD_S SE_S D_TA NDT_TA INT_TA IGP_TA LnS OI_TA OI_S SIGOI IDUM
Covariance Matrix from File TW0904.COV
Asymptotic Covariance Matrix from File TW0904.ACM
Sample Size: 125
Latent Variables: Growth Uniqueness Non_Debt_Tax_Shields Asset_Structure Size Profitability Volatility Industry_Dummy
Relationships:
LT_MVE = Growth Uniqueness Non_Debt_Tax_Shields Asset_Structure Size Profitability Volatility Industry_Dummy
ST_MVE = Growth Uniqueness Non_Debt_Tax_Shields Asset_Structure Size Profitability Volatility Industry_Dummy
C_MVE = Growth Uniqueness Non_Debt_Tax_Shields Asset_Structure Size Profitability Volatility Industry_Dummy
LT_BVE = Growth Uniqueness Non_Debt_Tax_Shields Asset_Structure Size Profitability Volatility Industry_Dummy
ST_BVE = Growth Uniqueness Non_Debt_Tax_Shields Asset_Structure Size Profitability Volatility Industry_Dummy
C_BVE = Growth Uniqueness Non_Debt_Tax_Shields Asset_Structure Size Profitability Volatility Industry_Dummy
Growth = GTA CE_TA RD_S
Uniqueness = RD_S SE_S
Non_Debt_Tax_Shields = D_TA NDT_TA
Asset_Structure = INT_TA IGP_TA
Size = LnS
Profitability = OI_TA OI_S
Volatility = 1.0*SIGOI
Industry_Dummy = 1.0*IDUM
Set the Error Variance of SIGOI to 0.0
Set the Error Variance of IDUM to 0.0
LISREL Output: PS = SY,FR TD = DI,FR ND = 3 SL = 0.05 SC SE SS TV AL EF RS MI
Path Diagram
End of Problem
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Lee, CF., Tai, T. (2015). Determination of Capital Structure: A LISREL Model Approach. In: Lee, CF., Lee, J. (eds) Handbook of Financial Econometrics and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7750-1_60
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DOI: https://doi.org/10.1007/978-1-4614-7750-1_60
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