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This paper aims to study whether noisy public information that investors receive about the expected aggregate dividend growth rate can help better understand the large average equity premium and stock return volatility in the US financial market.

This paper considers a dynamic asset pricing model with a representative agent, who cannot observe the expected growth rate of dividends and must learn its value by using noisy information. In addition, this paper presents a simple model for noisy information calibration.

With a coefficient of relative risk aversion below 10 and the time impatience parameter between 0 and 1, the calibrated model is able to yield an average risk-free interest rate, equity premium and stock return volatility that are close to the stylized facts in the US financial market.

First, this paper presents a different equilibrium model with a simple “catching up with the Joneses” preference and noisy information. Second, this paper develops a simple calibration procedure to calibrate the information process to study whether the calibrated model can help explain the large average equity premium and stock return volatility in the US financial market data.

The purpose of this paper is to investigate the role of “soft information” in firms’ debt issue and capital structure choices, and present a new model reconciling the gap between theories of capital structure and empirical findings.

The paper develops an analytical model of debt issues under asymmetric information in a setting where, in addition to observing the amount of debt the firm issues, outside investors obtain “soft information” signals through their own information production or noisy voluntary disclosures made by the firm. This paper analyzes the benefit and cost for the firm's debt issue decisions in this setting, and specifies the effect of soft information on these decisions.

If sufficiently precise soft information is available to outside investors, the firm's debt issue behavior is significantly altered relative to that in existing models. In particular, an inverted‐U shape relationship is found between the intrinsic value of the firm and the amount of debt it issues. Moreover, there is a negative relationship between the amount of debt the firm issues and the precision of soft information. Further, it is found that firms about which outside investors receive more favorable soft information issue less debt.

The model predicts an inverted‐U shape relationship between firms’ debt ratios and operating performance. It also predicts that firms about which outside investors receive more favorable or more precise soft information have lower debt ratios on average. A rationale is provided for the existence of firms’ investor relations departments.

Firms’ capital structure choices remain a topic of significant importance. This paper incorporates the soft informations into firms’ debt issue decisions and proposes a new model of capital structure that generates insights into firms’ financing decisions and disclosure decisions, as well as information production by outside investors.

Self-localization of an underwater robot using global positioning sensor and other radio positioning systems is not possible, as an alternative onboard sensor-based self-location estimation provides another possible solution. However, the dynamic and unstructured nature of the sea environment and highly noise effected sensory information makes the underwater robot self-localization a challenging research topic. The state-of-art multi-sensor fusion algorithms are deficient in dealing of multi-sensor data, e.g. Kalman filter cannot deal with non-Gaussian noise, while parametric filter such as Monte Carlo localization has high computational cost. An optimal fusion policy with low computational cost is an important research question for underwater robot localization.

In this paper, the authors proposed a novel predictive coding-biased competition/divisive input modulation (PC/BC-DIM) neural network-based multi-sensor fusion approach, which has the capability to fuse and approximate noisy sensory information in an optimal way.

Results of low mean localization error (i.e. 1.2704 m) and computation cost (i.e. 2.2 ms) show that the proposed method performs better than existing previous techniques in such dynamic and unstructured environments.

To the best of the authors’ knowledge, this work provides a novel multisensory fusion approach to overcome the existing problems of non-Gaussian noise removal, higher self-localization estimation accuracy and reduced computational cost.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

The purpose of this paper is to discover the causes of the devastation of New Orleans and the Mississippi Gulf Coast and how it may be ameliorated.

Economic analysis of the prior conditions causing susceptibility to flooding, and of the subsequent events involving long‐term assets: how assets had been selected and what changes have occurred in the evaluation and selection process.

The devastation caused by the hurricanes was far exceeded by: the prior governmental misleading of entrepreneurs and property owners regarding the actual level of flood protection provided to New Orleans by the bureaucratic Army Corps “flood protection system”; the resulting Rothbardian “cluster of entrepreneurial error” which allowed the devastation of New Orleans capital goods; the Hayekian unintended consequences of government actions and pronouncements following the storm, which interfered with market signals, increased subjective risk, reduced return expectations of entrepreneurs for capital assets, reducing net present values below zero; and the Misesian bureaucratic inefficiency of the corps and other governmental agents both before and after the storm. A sharp increase in their perception of flooding risk caused market participants now to see that no improvement in flood control can be achieved under the present bureaucratic structure, and they have permanently increased their perceived risk and discount rates, thereby reducing the pace of asset emplacement. Replacing the system of Lachmanian heterogeneous capital assets and their communications connections destroyed by Katrina cannot be accomplished in the present situation. New government actions and regulations are continually changing, noisy, and have altered property rights. The interactive efficiency of the asset system has been decreased. Incorrect assets are being built, necessary assets are being neglected, and the communications network between assets is not being replaced.

A finer level of detail could be investigated, focusing on smaller sub‐systems and interactions.

Greatest improvement in asset rebuilding would follow the elimination of all government regulations and regulatory agencies impeding the decision process, and private companies should be contracted to replace the destroyed wetlands and emplace flood controls.

The paper employs an inter‐disciplinary analysis of events using several different theoretical tools combined in an innovative way to examine why systematic errors were made and are continuing, and how errors can be stopped. The paper is of greatest value to those charged with repair of the damaged infrastructure of southeast Louisiana and Mississippi.

The Inflation Expectations Survey of Households, conducted by the Reserve Bank of India (RBI), indicates that there is considerable disparity in inflation expectations across cities in India. The purpose of this paper is to investigate why different cities exhibit heterogeneous inflation expectations despite coming under a central monetary policy umbrella.

First, the correspondence between city-level inflation expectations and city-specific economic characteristics is mapped. Second, how the disagreement in inflation expectations across cities, measured by dispersion, behaves over the business cycle is investigated. Finally, using seemingly unrelated regression technique, the economic factors that play a role in explaining inflation expectations heterogeneity across cities are estimated.

Cities with higher economic activity and cost of living have higher inflation expectations. Disagreement across cities regarding inflation expectations rise with an increase in output gap and inflation. Information friction plays an important role in explaining the disparity in inflation expectations across cities, and the effects of macro-level factors vary across cities, thereby accentuating expectations dispersion.

Monetary policy-related communication by the RBI (toward the general public) should increase in order to address information friction, which, in turn, would temper down the extent of inflation expectations heterogeneity across cities in India.

This is a novel application of the data from the monetary policy perspective. Heterogeneity in inflation expectations across cities or regions is an unexplored area. The use of nightlights as a proxy for city-level economic activity in India (in absence of data on city-level income) is another original contribution.

Investors are now able to analyse more noise-free news to inform their trading decisions than ever before. Their expectation that more information means better performance is not supported by previous psychological experiments which argue that too much information actually impairs performance. The purpose of this paper is to examine whether the degree of information explicitness improves stock market performance.

An experiment is conducted in a computer laboratory to examine a trading simulation manipulated from a real market-shock. Participants’ performance efficiency and effectiveness are measured separately.

The results indicate that the explicitness of information neither improves nor impairs participants’ performance effectiveness from the perspectives of returns, share and cash positions, and trading volumes. However, participants’ performance efficiency is significantly affected by information explicitness.

The novel approach and findings of this research add to the knowledge of the impact of information explicitness on the quality of decision making in a financial market environment.

The purpose of this paper is to investigate the optimal allocation of authority within “chain” organizations and to show when partial centralization becomes dominant in the sense of organizational performance.

This paper takes an incomplete contract approach and uses an information transmission framework to investigate the optimal governance structure, in which non-contractible decisions must be adapted to local operating conditions, and also coordinated with the upstream and downstream divisions. We also use simulation analysis to numerically show the theoretical mapping between the underlying parameters (i.e. coordination need) and the dominant organizational structures.

Partial decentralization will arise as the optimal governance structure only when the information in the middle branch is relatively concentrated or dispersive, so as to exploit the underlying information structure in the “chain” organizations. Specifically, when information is highly concentrated, direct control of the middle branch can improve coordination within firms. When the information is highly dispersive, to delegate authority to the middle branch only can improve communication.

This paper characterizes the optimal governance structure in “chain” organizations. The findings may give some enlightenment on real authority driven by ex ante asymmetric information structures and have implications on asymmetric delegation within firms.

This study examines the cross‐autocorrelation of size‐based portfolio returns in a sample of 15 major European markets using daily data from January 1990 through December 1999. Previous studies have primarily used U.S. data. This study extends previous research by considering results in multiple European exchanges. We examine whether a difference in size‐based portfolios exists by testing cross‐autocorrelation, granger‐causality, and asymmetric responses in the European markets. The results confirm that large stock portfolio returns lead small stock portfolio returns in most European countries, and that cross‐autocorrelation is present both within and between European financial markets.