GIVE Statistic for Goodness of Fit in Instrumental Variables Models with Application to COVID Data

Since COVID-19 outbreak, scientists have been interested to know whether there is any impact of the Bacillus Calmette-Guerin (BCG) vaccine against COVID-19 mortality or not. It becomes more relevant as a large population in the world may have latent tuberculosis infection (LTBI), for which a person may not have active tuberculosis but persistent immune responses stimulated by Mycobacterium tuberculosis antigens, and that means, both LTBI and BCG generate immunity against COVID-19. In order to understand the relationship between LTBI and COVID-19 mortality, this article proposes a measure of goodness of fit, viz., Goodness of Instrumental Variable Estimates (GIVE) statistic, of a model obtained by Instrumental Variables estimation. The GIVE helps in finding the appropriate choice of instruments, which provides a better fitted model. In the course of study, the large sample properties of the GIVE statistic are investigated. As indicated before, the COVID-19 data is analysed using the GIVE statistic, and moreover, simulation studies are also conducted to show the usefulness of the GIVE statistic.

kinkyreg: Instrument-free inference for linear regression models with endogenous regressors

In models with endogenous regressors, a standard regression approach is to exploit just-identifying or overidentifying orthogonality conditions by using instrumental variables. In just-identified models, the identifying orthogonality assumptions cannot be tested without the imposition of other nontestable assumptions. While formal testing of overidentifying restrictions is possible, its interpretation still hinges on the validity of an initial set of untestable just-identifying orthogonality conditions. We present the kinkyreg command for kinky least-squares inference, which adopts an alternative approach to identification. By exploiting nonorthogonality conditions in the form of bounds on the admissible degree of endogeneity, feasible test procedures can be constructed that do not require instrumental variables. The kinky least-squares confidence bands can be more informative than confidence intervals obtained from instrumental-variables estimation, especially when the instruments are weak. Moreover, the approach facilitates a sensitivity analysis for standard instrumental-variables inference. In particular, it allows the user to assess the validity of previously untestable just-identifying exclusion restrictions. Further instrument-free tests include linear hypotheses, functional form, heteroskedasticity, and serial correlation tests.

The Adverse Impact of Air Pollution on China’s Economic Growth

This study empirically evaluates the impact of air pollution on China’s economic growth, based on a province-level sample for the period 2002–2017. Air pollution is measured by the concentration of fine particulate matter (PM2.5), and economic growth is measured by the annual growth rate of gross domestic product (GDP) per capita. A panel data fixed-effects regression model is built, and the instrumental variables estimation method is utilized for quantitative analyses. The study reports a significant negative impact of air pollution on the macroeconomic growth of China. According to our instrumental variables estimation, holding other factors constant, if the concentration of PM2.5 increases by 1%, then the GDP per capita growth rate will decline by 0.05818 percentage points. In addition, it is found that the adverse effect of atmospheric pollution is heterogeneous across different regions. The effect is stronger in the eastern region and in provinces with smaller state-owned enterprise shares, fewer governmental expenditures for public health services, and fewer medical resources. The study results reveal that air pollution poses a substantial threat to the sustainable economic growth of China. Taking actions to abate air pollution will generate great economic benefits, especially for those regions which are heavily damaged by pollution.

GIVE Statistic for Goodness of Fit in Instrumental Variables Models with Application to COVID Data

Since COVID-19 outbreak, scientists have been interested to know whether there is any impact of the Bacillus Calmette-Guerin (BCG) vaccine against COVID-19 mortality or not. It becomes more relevant as a large population in the world may have latent tuberculosis infection (LTBI), for which a person may not have active tuberculosis but persistent immune responses stimulated by Mycobacterium tuberculosis antigens, and that means, both LTBI and BCG generate immunity against COVID-19. In order to understand the relationship between LTBI and COVID-19 mortality, this article proposes a measure of goodness of fit, viz., Goodness of Instrumental Variable Estimates (GIVE) statistic, of a model obtained by Instrumental Variables estimation. The GIVE helps in finding the appropriate choice of instruments, which provides a better fitted model. In the course of study, the large sample properties of the GIVE statistic are investigated. As indicated before, the COVID-19 data is analysed using the GIVE statistic, and moreover, simulation studies are also conducted to show the usefulness of the GIVE statistic.

Does moderate weight loss affect subjective health perception in obese individuals? Evidence from field experimental data

This paper analyzes whether moderate weight reduction improves subjective health perception in obese individuals. Besides simple regression models, in a simultaneous equation framework we use randomized monetary weight loss incentives as instrument for weight change, to address possible endogeneity bias. In contrast to related earlier work that also employed instrumental variables estimation, identification does not rely on long-term, between-individuals weight variation, but on short-term, within-individual weight variation. Yet, our result does not suggest that the simple regressions suffer from much endogeneity bias, since instrumental variables estimation yields similar—though far noisily estimated and statistically insignificant—estimates. In qualitative terms, our results do not contradict previous findings pointing to weight loss in obese individuals resulting in improved subjective health. Our results suggest that a reduction of body weight by one BMI unit is associated with an increase in the probability of reporting self-rated health to be ‘satisfactory’ or better by 3 to 4 percentage points. This finding may encourage obese individuals in their weight loss attempts, since they are likely to be immediately rewarded for their efforts by subjective health improvements.

The influence of CEO gender diversity on earnings management and corporate overinvestment: A research agenda

This research will focus on a study evoking the dilemma of the agency linking the principal to the agent. In the effects of the earnings management on the corporate overinvestment, along with the moderating role of the CEO gender, as a lever of control, our study focuses on a panel of 130 French companies over a period of four years, by the application of instrumental variables estimation (SLS)

The Effect of Home-Sharing on House Prices and Rents: Evidence from Airbnb

We assess the impact of Airbnb on residential house prices and rents: using a data set of Airbnb listings from the entire United States and an instrumental variables estimation strategy, we show that Airbnb has a positive impact on house prices and rents.

EFFECTS OF MEDICAID MANAGED CARE ENROLLMENT ON HOSPITALIZATION RISK AMONG DUAL-ELIGIBLES

Many state Medicaid agencies have recently expanded Medicaid managed care (MMC) to include low-income older adults that are Medicare-Medicaid dually enrolled (duals). While little evidence exists regarding the effects of these expansions on health outcomes, duals may be especially vulnerable to low-quality or low-intensity care delivered by MMC due to their high use of services and the fragmentation inherent in the two-payer system. Using difference-in-differences (DID) and instrumental variables estimation, this study provides the first national examination of the impact of MMC on hospitalization for duals using claims data. DID results indicate that managed long-term services and supports (LTSS) expansions among duals are associated with increased rates of hospitalization while comprehensive managed care results in no change in hospital use. Further analyses exploring heterogeneity shed light on how these managed care payment models affect older adults and individuals using LTSS.