Aggregation, Valuation accuracy, Price indices, Historic costs

We examine how asset structure is related to leverage in different institutional environments, using tens of thousands of firm-level observations from small, privately held, emerging market firms that are likely to face financing constraints. Our empirical analysis indicates that the linkage between asset tangibility (fixed assets as a portion of total assets) and leverage (measured as long-term debt over total assets) varies, such that in countries with fewer restrictions on collateral (land transferability), the relationship between these variables is much tighter. This also applies to the linkage between tangibility and debt maturity structure (measured as long-term debt over total debt). We find no evidence that industry concentration in different countries or changing composition of firms over time is driving our findings. The results are robust to using firm-level fixed effects specifications, to clustering error terms at the country level, and to using an alternative proxy for collateral law regime.

[Prior research has developed some econometric properties of accounting valuation rules as linear aggregations of prices and quantities. This study evaluates these proposed properties using actual price data and published price indexes for industrial machinery and equipment for the time period of 1973-1980. Current order prices for 4,875 new industrial machinery and equipment assets were obtained from commercially published pricing guides. The order prices were then used to construct a set of composite price indexes. The Bureau of Labor Statistics (BLS) Producer Price Indexes were also obtained. Various sized asset portfolios were simulated, and the changes in their values were estimated using both the constructed and the BLS price indexes. The changes in estimated and actual values of the simulated portfolios were compared to generate both Bias and mean-squared-error (MSE) measures resulting from the use of price indexes under various experimental conditions. These error measures were then used to evaluate the econometric properties proposed by earlier studies. In addition, total valuation errors were decomposed into movement and measurement components. Movement error is the valuation error attributable to deviations of the relative weights of assets used in constructing price indexes from those of the firm's asset portfolio. Measurement error is the valuation error arising from pricing errors, substitution bias, and inadequate adjustments for technological or quality changes in constructing price indexes. The main results indicate that (1) movement error Bias is zero, but measurement error Bias is nonzero; (2) both MSE movement error and MSE measurement error decline with the use of increasingly fine index systems and with the holding of more diversified asset portfolios, but increase as the asset holding period lengthens; and (3) measurement error tends to be the major source of total error. These findings are based on an assumption that the order prices taken from the commercial pricing guides are void of significant measurement error. Based on these results, it appears that additional effort should be devoted to enhancing price index construction techniques so that measurement error is decreased. Because portfolio diversification appears to reduce all components of error, asset valuations based on a price-index methodology should, perhaps, be restricted to cases involving larger groups of assets rather than very specialized strata of assets. Also, it may be appropriate to restrict the use of price indexes to assets of certain ages, as the study findings indicate that longer asset holding periods are associated with larger errors.]

Evaluates several analytical results developed in earlier studies and investigates the absolute econometric properties of valuation rules using date from a specific economic environment. Statistical theory of valuation; Effects of valuation error on current cost data; Relationship between portfolio diversification and valuation error.

[This paper reports the results of an investigation into the relationships between certain CPA examination candidates' attributes and these subjects' performance for 280 first-time candidates writing the November 1977 and May 1978 examinations in Texas. Findings indicate that scholastic aptitude test scores, accounting GPA, accounting hours completed, school attended, hours of self-study, and completion of a CPA review course have consistently significant associations with examination performance. Attributes lacking significant associations with examination performance include candidates' work experience, age, and completion of an audit course.]

Asset valuation, Valuation accuracy, Composite price index, Asset classes

This paper reports the results of an investigation into the relationships between certain CPA examination candidates' attributes and these subjects' performance for 280 first-time candidates writing the November 1977 and May 1978 examinations in Texas. Findings indicate that scholastic aptitude test scores, accounting GPA, accounting hours completed, school attended, hours of self-study, and completion of a CPA review course have consistently significant associations with examination performance. Attributes lacking significant associations with examination performance include candidates' work experience, age, and completion of an audit course.

T HE most frequently used estimating technique for applied economic research has been ordinary least squares (OLS). There are two theoretical justifications for its use. First, the Gauss-Markov theorem suggests that OLS estimators will outperform all other techniques in the class of linear unbiased estimators.1 Secondly, under the assumption that the error terms are normally distributed, OLS estimators can be derived as the maximum likelihood estimates. Frequently when OLS is introduced in elementary texts, the method of minimizing the sum of absolute deviations (MAD) is presented for comparison purposes, but rarely is it given serious consideration for applied uses.) Certainly one reason for such behavior stems from previous evaluations of OLS versus MAD estimators. Asher and Wallace (1963) found that the use of MAD meant one should be prepared to give up considerable efficiency 3 More recently Glahe and Hunt (1970), suggested several estimators derived under the general minimization criterion. In contrast to the Asher-Wallace study, the Glahe-Hunt model was a two-equation linear simultaneous system. Their results show that neither form of absolute deviation estimator outperformed either OLS or two-stage least squares.4 The purpose of this paper is to suggest that in at least one aspect these comparisons have given OLS a differential advantage. That is, both of these studies employed errors for their hypothesized models which were drawn from normal distributions. Consequently the OLS estimators are both maximum likelihood and best linear unbiased estimators (BLUE) under such circumstances. Since recently published works by Zeckhauser and Thompson (1970), Fama (1965) and others have called into question the assumption of normally distributed error terms, attention has begun to shift to other alternatives.5 Blattberg and Sargent (1971) have examined three techniques including both OLS and MAD when the errors are drawn from a stable Paretian distribution. Their findings indicate that the MAD estimator . performs sufficiently well that it deserves further study and elaboration. G Accordingly we have chosen to explore the relative merits of OLS and MAD for a single equation model whose errors are drawn from a double exponential parent distribution. This distribution was chosen because both estimators will exhibit theoretically desirable properties. OLS remains the BLUE estimator, while MAD is the maximum likelihood estimator. Furthermore, this distribution is one member of the power distribution suggested by Zeckhauser and Thompson as an alternative to the normal. This paper is divided into, three sections. The first describes the design of the experiments. Section II presents the empirical results and the last summarizes the primary findings of the paper.