Adoption of International Financial Reporting Standards: Impact on the Value Relevance of Intangible Assets

We examine whether the value relevance of reported intangibles differs between financial reporting regimes pre- and post-adoption of Australian Equivalents to International Financial Reporting Standards (AIFRS) and Australian Accounting Standards (AGAAP) respectively. Using AIFRS and AGAAP measures of goodwill and identifiable intangible assets for the same financial year and testing their association with share prices, we find evidence that AIFRS generally convey incremental useful information for investors about goodwill. For aggregated identifiable intangible assets there is no evidence that AIFRS conveys information beyond that in AGAAP. In contrast, we find evidence that AGAAP provides incremental information for investors in relation to identifiable intangibles, but not goodwill .     

Earnings and Impression Management in Financial Reports: The Case of CEO Changes

This article examines earnings management, as well as the presentational format of graphs (impression management) in the financial reports of sixty-three Australian listed public companies that changed chief executive officers (CEOs). Prior U.S. evidence generally suggests downward earnings management in the year of senior management changes and upward earnings management in the following year (Pourciau, 1993). We argue that new managers not only have incentives to manage earnings but also have similar incentives to manipulate the impressions created by graphs in financial reports. Examining earnings and impression management at the same time also provides an opportunity to distinguish between alternative explanations for any observed earnings management. In the year of CEO change, we hypothesize and find evidence of downward earnings management and some limited evidence of unfavourable impression management of the key financial variables (KFVs) graphed. As posited, we find evidence of upward earnings management and some evidence of favourable impression management in the year after a CEO change. These results are strongest for the subsample in which the CEO change was prompted by a resignation rather than a retirement.     

Robust Non‐nested Testing for Ordinary Least Squares Regression when Some of the Regressors are Lagged Dependent Variables*

The problem of testing non‐nested regression models that include lagged values of the dependent variable as regressors is discussed. It is argued that it is essential to test for error autocorrelation if ordinary least squares and the associated J and F tests are to be used. A heteroskedasticity–robust joint test against a combination of the artificial alternatives used for autocorrelation and non‐nested hypothesis tests is proposed. Monte Carlo results indicate that implementing this joint test using a wild bootstrap method leads to a well‐behaved procedure and gives better control of finite sample significance levels than asymptotic critical values.     

On the Choice Between the Stocking Rate and Time in Range Management

A long standing question in range management concerns the relative importance of the stocking rate versus the length oftime during which animals graze a particular rangeland. Weaddress this question by analyzing the problem faced by a privaterancher who wishes to minimize the long run expected net unit cost (LRENC) from range operations by choosing either the stocking rate or the length of time during which his animals graze hisrangeland. We construct a renewal-theoretic model and show that,in general, this rancher's LRENC with an optimally chosen stocking rate is lower than his LRENC with an optimally chosen grazing cycle length. From a management perspective, this means that correct stocking of the range is more important than the length of time during which animals graze the range. In addition, our research shows how to address questions concerning the desirability of temporal versus non-temporal controls in managing naturalresources such as fisheries and hunting grounds.     

A simple derivation of the limited information maximum likelihood estimator

The conventional derivation of the limited information maximum likelihood estimator is quite complicated. A simpler proof is provided in this note.     

Bootstrap HAC Tests for Ordinary Least Squares Regression*

There is a need for tests that are derived from the ordinary least squares (OLS) estimators of regression coefficients and are useful in the presence of unspecified forms of heteroskedasticity and autocorrelation. A method that uses the moving block bootstrap and quasi‐estimators in order to derive a consistent estimator of the asymptotic covariance matrix for the OLS estimators and robust significance tests is proposed. The method is shown to be asymptotically valid and Monte Carlo evidence indicates that it is capable of providing good control of significance levels in finite samples and good power compared with two other bootstrap tests.