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The time series of the federal funds rate has recently been extended back to 1928, now including several episodes during which interest rates remained near the lower bound of zero. This series is analyzed, using the method of indirect inference, by applying recent research on bounded time series to estimate a set of bounded parametric diffusion models. This combination uncouples the specification of the bounds from the law of motion. Although Louis Bachelier was the first to use arithmetic Brownian motion to model financial time series, he has often been criticized for this proposal, since the process can take on negative values. Most researchers favor processes such as geometric Brownian motion (GBM), which remains positive. Under this framework, Bachelier's proposal remains valid when specified with bounds and is shown to compare favorably when modeling the federal funds rate.

Security is one of the major challenges in the design and implementation of protocols for mobile ad hoc networks (MANETs). In such systems, the cooperation between nodes is one of the important principles being followed in the current research works to formulate various security protocols. Many existing works assume that mobile nodes will follow prescribed protocols without deviation. However, this is not always the case, because these networks are subjected to a variety of malicious attacks. Since there are various models of attack, trust routing scheme can guarantee security and trust of the network. The purpose of this paper is to propose a novel trusted routing model for mitigating attacks in MANETs.

The proposed model incorporates the concept of trust into the MANETs and applies grey relational analysis theory combined with fuzzy sets to calculate a node’s trust level based on observations from neighbour nodes’ trust level, these trust levels are then used in the routing decision-making process.

In order to prove the applicability of the proposed solution, extensive experiments were conducted to evaluate the efficiency of the proposed model, aiming at improving the network interaction quality, malicious node mitigation and enhancements of the system’s security.

The proposed solution in this paper is a new approach combining the fundamental basics of fuzzy sets with the grey theory, where establishment of trust relationships among participating nodes is critical in order to enable collaborative optimisation of system metrics. Experimental results indicate that the proposed method is useful for reducing the effects of malicious nodes and for the enhancements of system’s security.

This chapter examines the limit properties of information criteria (such as AIC, BIC, and HQIC) for distinguishing between the unit-root (UR) model and the various kinds of explosive models. The explosive models include the local-to-unit-root model from the explosive side the mildly explosive (ME) model, and the regular explosive model. Initial conditions with different orders of magnitude are considered. Both the OLS estimator and the indirect inference estimator are studied. It is found that BIC and HQIC, but not AIC, consistently select the UR model when data come from the UR model. When data come from the local-to-unit-root model from the explosive side, both BIC and HQIC select the wrong model with probability approaching 1 while AIC has a positive probability of selecting the right model in the limit. When data come from the regular explosive model or from the ME model in the form of 1 + n^α/n with α ∈ (0, 1), all three information criteria consistently select the true model. Indirect inference estimation can increase or decrease the probability for information criteria to select the right model asymptotically relative to OLS, depending on the information criteria and the true model. Simulation results confirm our asymptotic results in finite sample.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

The purpose of this research is to investigate how biofortification claims impact consumer food taste inference and purchase intention. Based on the halo effect, the authors propose that food products with biofortification claims are inferred to taste better than regular foods. Due to this inference, biofortification claims subsequently improve purchase intention.

To examine these predictions, the authors conducted three between-subject design lab experiments featuring three staple foods: corn soup (β-carotene biofortification claim present or not), cooked rice (zinc biofortification claim present or not) and uncooked rice (zinc biofortification claim present or not). Participants were randomly assigned to one of two bioproduction claim conditions (present vs absent). Then, taste inference, purchase intention, consumer characteristics and confounding variables were measured.

In Experiment 1, the results showed that biofortification claims indeed appeared to evoke a heuristic halo effect, in which foods with biofortification claims were inferred to taste better than regular food. In Experiment 2, the results showed that participants had more intention to purchase foods with biofortification claims than regular food. The mediation effect of taste inference between biofortification claims and purchase intention was examined. In Experiment 3, the data further showed that this halo effect was more pronounced when consumers held a higher preference (vs lower preference) for the enriched nutritional element.

Biofortification claims have commonly been viewed solely as information about nutrition value for consumers. However, little is known about how biofortification claims impact hedonic consumer expectations. In this paper, the authors find that biofortification claims alone can impact consumer food taste inference, as nutritional information is not related to actual food taste. These findings extend the authors’ understanding of the psychological mechanism behind consumer attitudes towards biofortification.

Plus Factors have long played an important role in inferring a price agreement from the totality of the evidence. In response to changes in the case law, economists have proposed two alternative paths for the future of price fixing analysis. This paper evaluates the suggested approaches and recommends retaining the enhanced Plus Factor methodology.

By carefully defining the Plus Factor concept, three key components of the analysis emerge: (1) information on communications associated with the alleged agreement, (2) economic considerations affecting market competition, and (3) characteristics that serve to differentiate explicit from tacit collusion.

Developments rationalizing the Plus Factor concept show promise, as the methodology is not more closely related to economic theory. On the other hand, replacement of the Plus Factor methodology with one focused on market performance seems problematic. By abandoning the Plus Factor concept, the economist loses a key institutional constraint on over-aggressive enforcement.

Until advocates can address the difficulties associated with using performance evidence to identify price fixing, the standard Plus Factor concept appears more appropriate. Thus, antitrust analysts should continue to use the Plus Factor methodology to infer agreements in price fixing investigations, as long as the economic rationalization of the specific Plus Factor is clearly presented.

The paper synthesizes a number of recent contributions to the price fixing literature and addresses key issues of interest to the enforcement community. By providing a critique of the proposed policy shift to use performance evidence to infer price fixing liability, the study serves to justify continued application of the Plus Factor methodology.

This study aims to investigate how judgments of firms’ underlying motives are affected by corporate social responsibility (CSR) communication which features percentages of profit allocations to CSR causes. It also examines how firm size interacts with CSR spending allocations affecting motive attributions for firms of different sizes.

Two experiments were carried out manipulating CSR spending allocations (smaller vs larger percentage of profit) and firm size (small vs large firm).

A larger percentage of profits allocated to CSR enhanced value-driven motives and inhibited inferences of manipulative intent, which produced lower egoistic-driven motives. Large firms allocating smaller percentages to CSR were judged as less value-driven and were more prone to elicit manipulative intent.

Two routes of motive attributions were identified – a direct route, contingent on CSR spending allocations and firm size; and an indirect route via inferences of manipulative intent, which inhibited favorable motives and prompted unfavorable ones. Both routes resulted from numerical cognition associated with the processing of numbers. Managerial implications include suggestions for firms wishing to overcome negative consumer bias arising from communication featuring CSR spending allocations and firm size.