Essays In Heterogeneity In Macroeconomics

This dissertation consists of two chapters that explore how micro-level heterogeneity helps us understand the dynamics of macroeconomic variables. Chapter 2 shows that the evolving likelihood of marriage and divorce is an essential factor in accounting for the changes in housing decisions over time in the United States. I build and estimate a life-cycle model of single and married households who face exogenous age-dependent marital transition shocks and then conduct a decomposition analysis between 1970 and 1995. The results show that household formation shocks could account for about 30% of the increase in the single\u27s homeownership rate and play a crucial role in generating the observed sign of change in portfolio share of married households. The extended analysis on recent years after 1995 shows that the continuing decrease in marriage prospects contributed to push up the single\u27s homeownership rate during the housing boom in the mid 2000s. Chapter 3 develops a state-space model with a state-transition equation that takes the form of a functional vector autoregression and stacks macroeconomic aggregates and a cross-sectional density. The measurement equation captures the error in estimating log densities from repeated cross-sectional samples. The log densities and the transition kernels in the law of motion of the states are approximated by sieves, which leads to a finite-dimensional representation in terms of macroeconomic aggregates and sieve coefficents. We illustrate how the model works based on the simulation of the Krusell-Smith economy and conduct an empirical analysis on the joint dynamics of technology shocks, per capita GDP, employment rates, and the earnings distribution

Heterogeneity and Aggregate Fluctuations

We develop a state-space model with a state-transition equation that takes the form of a functional vector autoregression and stacks macroeconomic aggregates and a cross-sectional density. The measurement equation captures the error in estimating log densities from repeated cross-sectional samples. The log densities and the transition kernels in the law of motion of the states are approximated by sieves, which leads to a nite-dimensional representation in terms of macroeconomic aggregates and sieve coefficients. We use this model to study the joint dynamics of technology shocks, per capita GDP, employment rates, and the earnings distribution. We nd that the estimated spillovers between aggregate and distributional dynamics are generally small, a positive technology shock tends to decrease inequality, and a shock that raises the inequality of earnings leads to a small but not signifi cant increase in GDP

Oxidase-Coupled Amperometric Glucose and Lactate Sensors with Integrated Electrochemical Actuation System

Unpredictable baseline drift and sensitivity degradation during continuous use are two of the most significant problems of biosensors including the amperometric glucose and lactate sensors. Therefore, the capability of on-demand in situ calibration/diagnosis of biochemical sensors is indispensable for reliable long-term monitoring with minimum attendance. Another limitation of oxidase enzyme-based biosensors is the dependence of enzyme activity on the background oxygen concentration in sample solution. In order to address these issues, the electrolytic generation of oxygen and hydrogen bubbles were utilized 1) to overcome the background oxygen dependence of glucose and lactate sensors and 2) to demonstrate the feasibility of in situ self-calibration of the proposed glucose and lactate sensors. Experimental data assure that the proposed techniques effectively establish the zero calibration value and significantly improve the measurement sensitivity and dynamic range in both glucose and lactate sensors

Glucose Oxidase (GOD)-Coupled Amperometric Microsensor with Integrated Electrochemical Actuation System

Recent developments for biosensors have been mainly focused on miniaturization and exploratory use of new materials. It should be emphasized that the absence of a novel in-situ self-calibration/diagnosis technique that is not connected to an external apparatus is a key obstacle to the realization of a biosensor for continuous use with minimum attendance. In order to address this issue, a novel solid-state glucose oxidase-coupled amperometric biosensor with integrated electrochemical actuation system has been designed and validated. There are two key components of the proposed glucose biosensor: solid-state GOD-coupled thin-lm amperometric sensing element and O2 depleting/saturating built-in electrochemical actuator. The actuator can be used to accomplish in-situ 1-point self-calibration by depleting O2 (i.e., by simulating glucose-free environment). Also, it can be used at the same time to extend the proposed sensor\u27s linear detection range by in ating O2 (i.e., by enhancing glucose sensitivity). A prototype sensor was fabricated and a series of lab experiments was conducted. Collected data assures that the proposed sensor effectively establishes the zero calibration point and signi cantly enhances its measurement sensitivity and con dence

Detecting variability in massive astronomical time-series data. II. Variable candidates in the Northern Sky Variability Survey

We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method, which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves having data points at more than 15 epochs as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight-dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated with IRAS, AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GALEX objects, as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database, which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10% of our entire NSVS sample. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSV

Automated Oxidase-Coupled Amperometric Microsensor with Integrated Electrochemical Actuation System for Continuous Sensing of Saccharoids

Recent developments for biosensors have been mainly focused on miniaturization and exploratory use of new materials. It should be emphasized that the absence of a novel in-situ self-calibration/diagnosis technique that is not connected to an external apparatus is a key obstacle to the realization of a biosensor for continuous use with minimum attendance. To address this deficiency, a novel needle-type biosensor system with fully automated operations is being developed, in which a novel oxidase-coupled amperometric sensor with oxygen depleting/generating actuator is interfaced with an electrochemical instrument and a perfusion system. Labview virtual instrument has been also developed to oversee the automatic control of the prototype sensor. Using the proposed system, a large amount of data can be rapidly collected for more effective sensor characterization and more advanced sensor designs. Autonomous and continuous sensing and self-calibration with minimal human intervention is also envisioned

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.Comment: 21 pages, 4 figure

Innovative Cases of Organic Agriculture in Asia

Under a 10-year cooperation agreement between Xichong County, PR China and IFOAM Asia, the “Xichong International Organic Innovation Summit” is to be held every two years while the Asia Organic Agriculture Technology, Research & Development Xichong Center is responsible for collection, dissemination and publication of innovations in organic agriculture. To this end, the Xichong Organic Innovation Committee has been formed consisting of 9 experts from different Asian countries or regions. After the efforts made by the experts of the committee as well as the assistance of the IFOAM Asia head office staff, the first collection of the innovation technologies and cases have been compiled for publication and dissemination