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We provide a theory of rational stock price bubbles in production economies with infinitely-lived agents. Firms meet stochastic investment opportunities and face endogenous credit constraints. They are not fully committed to repaying debt. Credit constraints are derived from incentive constraints in optimal contracts which ensure default never occurs in equilibrium. Stock price bubbles can emerge through a positive feedback loop mechanism and cannot be ruled out by transversality conditions. These bubbles command a liquidity premium and raise investment by raising the debt limit. Their collapse leads to a recession and a stock market crash.

Preferences that accommodate aversion to subjective uncertainty and its potential misspecification in dynamic settings are a valuable tool of analysis in many disciplines. By generalizing previous analyses, we propose a tractable approach to incorporating broadly conceived responses to uncertainty. We illustrate our approach on some stylized stochastic environments. By design, these discrete time environments have revealing continuous time limits. Drawing on these illustrations, we construct recursive representations of intertemporal preferences that allow for penalized and smooth ambiguity aversion to subjective uncertainty. These recursive representations imply continuous time limiting Hamilton–Jacobi–Bellman equations for solving control problems in the presence of uncertainty.

We propose a novel generalized recursive smooth ambiguity model which permits a three-way separation among risk aversion, ambiguity aversion, and intertemporal substitution. We apply this utility model to a consumption-based asset-pricing model in which consumption and dividends follow hidden Markov regime-switching processes. Our calibrated model can match the mean equity premium, the mean risk-free rate, and the volatility of the equity premium observed in the data. In addition, our model can generate a variety of dynamic asset-pricing phenomena, including the procyclical variation of price-dividend ratios, the countercyclical variation of equity premia and equity volatility, the leverage effect, and the mean reversion of excess returns. The key intuition is that an ambiguity-averse agent behaves pessimistically by attaching more weight to the pricing kernel in bad times when his continuation values are low.

We estimate the value creation for the stocks purchased by the Chinese government between the period starting with the market crash in mid-June of 2015 and the market recovery in September. We find that the government intervention increased the value of the rescued non-financial firms by RMB 206 billion after netting out the average purchase cost, which is about 1% of the Chinese GDP in 2014. The shortterm value creation came from the increased stock demand, the reduced default probabilities, and the increased liquidity. The intervention may come at a long-run cost of creating moral hazard, preventing price discovery, creating more uncertainty, and damaging government credibility.

This paper presents an infinite-horizon model of production economies in which firms face idiosyncratic productivity shocks and are subject to endogenous credit constraints. Credit-driven stock price bubbles can arise which can relax credit constraints and reallocate capital more efficiently among firms. The collapse of bubbles causes a fall of total factor productivity.

This paper provides a competitive equilibrium model of capital structure and industry dynamics. In the model, firms make financing, investment, entry, and exit decisions subject to idiosyncratic technology shocks. The capital structure choice reflects the tradeoff between the tax benefits of debt and the associated bankruptcy and agency costs. The interaction between financing and production decisions influences the stationary distribution of firms and their survival probabilities. The analysis demonstrates that the equilibrium output price has an important feedback effect. This effect has a number of testable implications. For example, high growth industries have relatively lower leverage and turnover rates.

The relationship between glycemic variability (GV) and mortality in critically ill trauma patients remains unclear. We evaluated whether GV, quantified by the coefficient of variation (CV) of glucose during the ICU stay, is associated with short- and long-term mortality in this population. Using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, we identified 4,009 adult trauma patients admitted to the intensive care unit and assessed the association between CV and 30-day and 1-year mortality with Cox proportional hazards models, Kaplan–Meier analysis, restricted cubic splines, subgroup analyses, and mediation models to explore the potential mediating role of sepsis. Higher GV was independently associated with increased 30-day (adjusted HR 1.50, P  < 0.001) and 1-year mortality (adjusted HR 1.29, P  < 0.001). Nonlinear analyses showed a J-shaped relationship between CV and mortality, with risk rising steeply above a CV of approximately 12.2%, particularly in younger patients and those without diabetes. Mediation analyses suggested that sepsis accounted for about 50.7% of the association with 30-day mortality and 70.5% with 1-year mortality. In critically ill trauma patients, higher GV is independently associated with both short- and long-term mortality, with an apparent threshold effect at mid-teen CV values, and sepsis appears to be a key pathway linking GV to mortality, although these mediation findings should be interpreted as exploratory and hypothesis-generating.

Since the founding of the People’s Republic of China, the advantages of logistics are neglected, and the scale operation and the welfare in the industry are difficult to achieve due to the influence of the economic system and social environment. Therefore, a new intelligent logistics distribution management system based on machine vision and visual sensor image processing technology is constructed to respond to the shortcomings of the traditional system, including slow efficiency, huge cost, complex data, and low degree of informatization. Through the analysis and research on the visual sensor image processing technology and the order processing, receipt management, distribution management, scheduling management, and return management that affect logistics distribution, a simulation experiment is used to verify that the visual sensor image processing technology is rigorous, intelligent, and efficient and has high precision. The intelligent logistics distribution management system can effectively solve the problems existing in the traditional logistics distribution management. The experimental results show that the visual sensor image processing technology can collect and analyze the target image and effectively track and monitor it in the logistics distribution process. The average distribution precision of the intelligent logistics distribution management system reaches more than 99.5%, which is greatly improved compared with 90% of the traditional logistics distribution. And it can greatly improve the distribution efficiency, which increases by about 26.5%. The study realizes the information management of the logistics system and automatically completes all the work according to the designed program, so that the real-time dynamic distribution can be transmitted to the urban logistics distribution at any time.

We present an estimated dynamic stochastic general equilibrium model of stock market bubbles and business cycles using Bayesian methods. Bubbles emerge through a positive feedback loop mechanism supported by self-fulfilling beliefs. We identify a sentiment shock that drives the movements of bubbles and is transmitted to the real economy through endogenous credit constraints. This shock explains most of the stock market fluctuations and sizable fractions of the variations in real quantities. It generates the comovement between stock prices and the real economy, and is the dominant force behind the internet bubbles and the Great Recession.

River basin cities are areas with remarkable conflicts between the human activity and the ecological environment. They are also important targets for policy implementation of sustainable and high-quality development (HD) in various countries around the world. This article exploits the panel data of 99 cities located in the Yellow River Basin (YRB) from 2006 to 2019 to empirically analyze the spatial effect of financial growth on HD. Spatial weights participated econometric models are utilized to analyze this spatial effect. Empirical results reveal that: (1) the HD in the YRB shows a strong positive spatial autocorrelation. (2) Financial growth exerts an N-shaped curve effect on the HD from a long-term perspective. When this influence spills out to the surroundings, it exhibits an inverted U-shaped characteristic. (3) Green innovation can be an important intermediary factor in the influence of financial growth on HD. (4) The influence of financial growth on HD appears stronger in regions with higher economic levels, where N-shaped effects can be transmitted to the surrounding regions. However, the backward economic development in low-economy regions prevents the spatial spillover of N-shaped effects. This study can be instrumental for countries to formulate financial policies that aim to promote HD in river basin cities.