Search results

To provide an overview of research published in the management accounting literature on methods for cost management in new product development, such as a target costing, life cycle costing, component commonality, and modular design.

The structured literature search covered papers about 15 different cost management methods published in 40 journals in the period 1990–2013.

The search yielded a sample of 113 different papers. Many contained information about more than one method, and this yielded 149 references to specific methods. The number of references varied strongly per cost management method and per journal. Target costing has received by far the most attention in the publications in our sample; modular design, component commonality, and life cycle costing were ranked second and joint third. Most references were published in Management Science; Management Accounting Research; and Accounting, Organizations and Society. The results were strongly influenced by Management Science and Decision Science, because cost management methods with an engineering background were published above average in these two journals (design for manufacturing, component commonality, modular design, and product platforms) while other topics were published below average in these two journals.

The scope of this review is accounting research. Future work could review the research on cost management methods in new product development published outside accounting.

The paper centers on methods for cost management, which complements reviews that focused on theoretical constructs of management accounting information and its use.

This chapter analyzes the empirical relationship between the pricesetting/consumption behavior and the sources of persistence in inflation and output. First, a small-scale New-Keynesian model (NKM) is examined using the method of moment and maximum likelihood estimators with US data from 1960 to 2007. Then a formal test is used to compare the fit of two competing specifications in the New-Keynesian Phillips Curve (NKPC) and the IS equation, that is, backward- and forward-looking behavior. Accordingly, the inclusion of a lagged term in the NKPC and the IS equation improves the fit of the model while offsetting the influence of inherited and extrinsic persistence; it is shown that intrinsic persistence plays a major role in approximating inflation and output dynamics for the Great Inflation period. However, the null hypothesis cannot be rejected at the 5% level for the Great Moderation period, that is, the NKM with purely forward-looking behavior and its hybrid variant are equivalent. Monte Carlo experiments investigate the validity of chosen moment conditions and the finite sample properties of the chosen estimation methods. Finally, the empirical performance of the formal test is discussed along the lines of the Akaike's and the Bayesian information criterion.

The authors discuss the econometric underpinnings of Barro (2006)'s defense of the rare disaster model as a way to bring back an asset pricing model “into the right ballpark for explaining the equity-premium and related asset-market puzzles.” Arbitrarily low-probability economic disasters can restore the validity of model-implied moment conditions only if the amplitude of disasters may be arbitrary large in due proportion. The authors prove an impossibility theorem that in case of potentially unbounded disasters, there is no such thing as a population empirical likelihood (EL)-based model-implied probability distribution. That is, one cannot identify some belief distortions for which the EL-based implied probabilities in sample, as computed by Julliard and Ghosh (2012), could be a consistent estimator. This may lead to consider alternative statistical discrepancy measures to avoid the problem with EL. Indeed, the authors prove that, under sufficient integrability conditions, power divergence Cressie-Read measures with positive power coefficients properly define a unique population model-implied probability measure. However, when this computation is useful because the reference asset pricing model is misspecified, each power divergence will deliver different model-implied beliefs distortion. One way to provide economic underpinnings to the choice of a particular belief distortion is to see it as the endogenous result of investor's choice when optimizing a recursive multiple-priors utility a la Chen and Epstein (2002). Jeong et al. (2015)'s econometric study confirms that this way of accommodating ambiguity aversion may help to address the Equity Premium puzzle.

This chapter is designed to outline how current methods in formal policy analysis have evolved to better respect limits to an analyst's knowledge. These limits are referred to as model uncertainty both in order to capture the idea that formal policy analysis is predicated on mathematically precise formulations that embody assumptions on the part of an analyst and because model uncertainty, which represents a recognition of the potential for these assumptions to produce unsound analyses, has been an active area of research in economics and statistics for the last 15 or so years. The argumentation in this chapter is not original and is admittedly selective. For Austrian economists, the paper will hopefully be of interest in indicating how empirical work is evolving in a way that better respects limits to a social scientist's knowledge. I certainly do not mean to suggest that these arguments should eliminate the objections that have been raised by some Austrian economists to formal empirical work. Rather, the intent of this chapter is to indicate the possibility of dialog and debate between Austrian and non-Austrian economists on the role of formal empirical work. In several contexts, I have introduced arguments concerning the limits of formal econometric analysis by Hayek and von Mises to both illustrate how the perspectives in this chapter relate to their views in order to suggest why, in my judgment, some of their skepticism is unwarranted.

This paper surveys the treatment of expectations in estimated Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models.

A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods.

A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations.

I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This paper provides a theoretical framework for application of Chance-Constrained Programming (CCP) in situations where the coefficient matrix is random and its elements are not normally distributed. Much of the CCP literature proceeds to derive deterministic equivalent in computationally implementable form on the assumption of “normality”. However, in many applications, such as air pollution control, right skewed distributions are more likely to occur. Two types of models are considered in this paper. One assumes an exponential distribution of matrix coefficients, and another one uses an empirical approach. In case of exponential distributions, it is possible to derive exact “deterministic” equivalent to the chance-constrained program. Each row of the coefficient matrix is assumed to consist of independent, exponentially distributed random variables and a simple example illustrates the complexities associated with finding a numerical solution to the associated deterministic equivalent. In our empirical approach, on the other hand, simulated data typically encountered in air pollution control are provided, and the data-driven (empirical) solution to the implicit form of deterministic equivalent is obtained. Post-optimality analyses on model results are performed and risk implications of these decisions are discussed. Conclusions are drawn and directions for future research are indicated.