Should the federal reserve have responded to asset prices?

Determining strategies for taking into account movements in asset prices is a perennially important issue for central banks. In this paper, an analysis is provided to address this issue for the U.S. economy. To do so, an empirical model of the U.S. economy is constructed and estimated, and the estimated model is simulated with a set of alternative monetary policy rules. Comparing the stabilization performance of the rules, it is found that: i) by responding to a larger set of policy indicators and taking a more aggressive stance toward inflation and output gap in particular, the Federal Reserve could have achieved a much higher degree of stabilization; ii) had the Federal Reserve responded to its historical policy indicators differently, it could have conducted a near-optimal policy rule, even without taking into account movements in housing and stock prices; iii) the Federal Reserve could have likewise achieved close-to-optimal stabilization results by properly responding to movements in asset prices, on top of its historical policy scheme; and iv) stock price inflation contains more useful information that helps further stabilize the economy than does housing price inflation

Decomposing G7 Business Cycle

In this paper, we have estimated a model that incorporates two key features of business cycles, co-movement among economic variables and switching between regimes of expansion and recession, to aggregate quarterly data for the G7 countries. Two common factors, interpreted as reflecting the permanent and transitory components of the business cycle in the region, and estimates of turning points from one regime to the other were extracted from the data by using the Kalman filter and maximum likelihood estimation approach of Kim(1994). Estimation results confirm a fairly typical stylized fact of business cycles - recessions are steeper and shorter than recoveries, and both co-movement and regime switching are found to be important features of the business cycle in those countries as a whole. The two common factors produce sensible representations of the trend and cycle, and the estimated turning points agree quite well with independently determined chronologies. It also turns out that the degree of synchronization between the G7 and the Korean economy has significantly increased after the Asian currency crisis of 1997

Can Housing Prices be Justified by Economic Fundamentals? Evidence from Regional Housing Markets in Korea

In this study, we use a present-value approach to examine the dynamics of six regional housing markets in Korea. The large upswing in the price–rent ratio accompanied by intermittent ups and downs, which are typical features of the Korean housing market since the mid-1980s, is captured by a periodically collapsing bubble incorporated into an otherwise standard present-value model. The movements in the actual price–rent ratio are then decomposed into movements explained by the expectations of housing market fundamentals (i.e., rent growth, risk-free interest rate, and excess returns from housing investment) and the speculative bubble. In all the six regional markets, most of the variations in the fundamental part of the price–rent ratio are explained by the expected risk premium of housing investment and the expected risk-free returns, whereas the expected rent growth account for relatively small fractions of the variations. The bubble has continuously accumulated since the early 2000s in all the six regions and has reached as high as 70% of house price by the end of 2017.Financial support from the Center for Distributive Justice in the Institute of Economic Research of Seoul National University is gratefully acknowledged

Population Aging in Korea: Implications for Fiscal Sustainability

Adverse demographics in Korea impinges on its growth potential and fiscal outlook. Accordingly, this study examines the current demographic situation and recent projections related to the impacts of population aging in Korea, particularly on the looming fiscal imbalance. The focal conclusion is that a two-way effect exists from population aging. First is the anticipated stress placed on government finances due to increasing welfare expenditure for the elderly. Second is sluggish economic growth and thus the inability to collect sufficient government revenues. The prospect of large and growing deficits is therefore immediate and potentially long lasting as governments will be faced with rising spending demands and sluggish tax revenues arising simultaneously from an aging population

D\"aRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation

Neural radiance fields (NeRF) shows powerful performance in novel view synthesis and 3D geometry reconstruction, but it suffers from critical performance degradation when the number of known viewpoints is drastically reduced. Existing works attempt to overcome this problem by employing external priors, but their success is limited to certain types of scenes or datasets. Employing monocular depth estimation (MDE) networks, pretrained on large-scale RGB-D datasets, with powerful generalization capability would be a key to solving this problem: however, using MDE in conjunction with NeRF comes with a new set of challenges due to various ambiguity problems exhibited by monocular depths. In this light, we propose a novel framework, dubbed D\"aRF, that achieves robust NeRF reconstruction with a handful of real-world images by combining the strengths of NeRF and monocular depth estimation through online complementary training. Our framework imposes the MDE network's powerful geometry prior to NeRF representation at both seen and unseen viewpoints to enhance its robustness and coherence. In addition, we overcome the ambiguity problems of monocular depths through patch-wise scale-shift fitting and geometry distillation, which adapts the MDE network to produce depths aligned accurately with NeRF geometry. Experiments show our framework achieves state-of-the-art results both quantitatively and qualitatively, demonstrating consistent and reliable performance in both indoor and outdoor real-world datasets. Project page is available at https://ku-cvlab.github.io/DaRF/.Comment: Project Page: https://ku-cvlab.github.io/DaRF

Estimating Scalability Issues While Finding an Optimal Assignment for Carpooling

AbstractAn automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service (GCPMS) shall advise registered candidates on how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for negotiation success while trying to merge planned trips by carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person and trip characteristics and on the other hand the negotiation feedback. The GCPMS provides advice by maximizing the expected value for negotiation success. This paper describes possible ways to determine the optimal advice and estimates computational scalability using real data for Flanders

Analysis of the Co-routing Problem in Agent-based Carpooling Simulation

AbstractCarpooling can cut costs and help to solve congestion problems but does not seem to be popular. Behavioral models allow to study the incentives and inhibitors for carpooling and the aggregated effect on the transportation system. In activity based modeling used for travel forecasting, cooperation between actors is important both for schedule planning and revision. Carpooling requires cooperation while commuting which in turn involves co-scheduling and co-routing. The latter requires combinatorial optimization. Agent-based systems used for activity based modeling, contain large amounts of agents. The agent model requires helper algorithms that deliver high quality solutions to embedded optimisation problems using a small amount of resources. Those algorithms are invoked thousands of times during agent society evolution and schedule execution simulation. Solution quality shall be sufficient in order to guarantee realistic agent behavior. This paper focuses on the co-routing problem

Exploration of New Electroacupuncture Needle Material

Background. Electro Acupuncture (EA) uses the acupuncture needle as an electrode to apply low-frequency stimulation. For its safe operation, it is essential to prevent any corrosion of the acupuncture needle. Objective. The aim of this study is to find an available material and determine the possibility of producing a standard EA needle that is biocompatible. Methods. Biocompatibility was tested by an MTT assay and cytotoxicity testing. Corrosion was observed with a scanning electron microscope (SEM) after 0.5 mA, 60 min stimulation. The straightness was measured using a gap length of 100 mm, and tensile testing was performed by imposing a maximum tensile load. Results. Phosphor bronze, Ni coated SS304, were deemed inappropriate materials because of mild-to-moderate cytotoxicity and corrosion. Ti-6Al-4V and SS316 showed no cytotoxicity or corrosion. Ti-6Al-4V has a 70 times higher cost and 2.5 times lower conductivity than SS316. The results of both straightness and tensile testing confirmed that SS316 can be manufactured as a standard product. Conclusion. As a result, we confirmed that SS316 can be used a new EA electrode material. We hope that a further study of the maximum capacity of low-frequency stimulation using an SS316 for safe operation