Простой и сложный метод разности разностей

Panel data analysis is common in the social sciences. Fixed effects models are a favorite among sociologists because they control for unobserved heterogeneity (unexplained variation) among cross-sectional units, but estimates are biased when there is unobserved heterogeneity in the underlying time trends. Two-way fixed effects models adjust for unobserved time heterogeneity but are inefficient, cannot include unit-invariant variables, and eliminate common trends: the portion of variance in a time-varying variable that is invariant across cross-sectional units. This article introduces a general panel model that can include unit-invariant variables, corrects for unobserved time heterogeneity, and provides the effect of common trends while also allowing for unobserved unit heterogeneity, time-varying coefficients, and time-invariant variables. One-way and two-way fixed effects models are shown to be restrictive forms of this general model. Other restrictive forms are also derived that offer all the usual advantages of one-way and two-way fixed effects models but account for unobserved time heterogeneity. The author uses the models to examine the increase in state incarceration rates between 1970 and 2015.

Locally adjusted LM test for spatial dependence in fixed effects panel data models

Based on the panel data of Shanghai and Shenzhen A-shares from 2008 to 2017, this paper establishes the two-way fixed effects model to verify the long-term incentive effect of tax preferences on enterprise innovation. The results show that tax preferences are not only conducive to the incentive of current innovation, the incentive effect on future innovation increases year by year. The longer an enterprise enjoys tax preferences, the more conducive it will be to its future innovation, and tax preferences have significant long-term effects on innovation.

Buprenorphine uptake during pregnancy following the 2017 guidelines update on prenatal opioid use disorder

Reading and critically appraising systematic reviews and meta-analyses: A short primer with a focus on hepatology

Meta-analysis as a clinical tool in nephrology

Purpose - This study analyzed the correlation between economic liberalization and foreign direct investment. The purpose of this study is to seek ways to attract foreign direct investment from developing countries. Design/methodology/approach - This study analysed with observations of 19 from 2000 to 2018 using a fixed effect model, a random effect model, and a two-way fixed effect model. Findings - First, it was found that economic liberalization had a positive effect on attracting foreign direct investment in the early stages of economic liberalization. Second, it was found that economic liberalization in the deepening stage of economic liberalization had a negative effect on attracting foreign direct investment. In general, it was found that the higher the level of economic liberalization in developing countries is not accompanied by innovative changes in the industrial structure, the higher the level of economic liberalization is likely to decrease the inducement of foreign direct investment due to negative factors such as an increase in labor costs. Overall, this study approved that Economic liberalization have a non-linear (inverted U-shape) relationship with the inflow of foreign direct investment. Research implications or Originality - First, this study attempted to expand the variables for the determinants of FDI by analyzing economic factors which is a determinent of FDI. Second, economic liberalization generally has a positive effect on foreign direct investment, but it proved that it does not have only positive effects as a factor of attracting foreign direct investment in developing countries. The advantage of low wages in ASEAN countries acts as a factor for foreign direct investment, but as the degree of economic liberalization increases, the environment such as government size, guarantee of property rights, international trade freedom, fiscal soundness, and regulations change positively. On the other hand, it can be suggested that if the industrial level is less, it may lead to a loss of comparative advantage and a decrease in investment.

Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that are related to the parameter of interest (e.g., selection into treatment is based on individual growth of the outcome). In this study, we derive the bias in conventional FE models and show that fixed effects individual slope (FEIS) models can overcome this problem. This is a more general version of the conventional FE model, which accounts for heterogeneous slopes or trends, thereby providing a powerful tool for panel data and other multilevel data in general. We propose two versions of the Hausman test that can be used to identify misspecification in FE models. The performance of the FEIS estimator and the specification tests is evaluated in a series of Monte Carlo experiments. Using the examples of the marital wage premium and returns to preschool education (Head Start), we demonstrate how taking heterogeneous effects into account can seriously change the conclusions drawn from conventional FE models. Thus, we propose to test for bias in FE models in practical applications and to apply FEIS if indicated by the specification tests.

Air pollution severely threatens people’s health and sustainable economic development. In the era of the digital economy, modern information technology is profoundly changing the way governments govern, the production mode of enterprises, and the living behavior of residents. Whether digital technology can bring ecological welfare needs to be further studied. Based on panel data from 269 Chinese cities from 2006 to 2021, this study empirically examines the impact of digital technology on air pollution by using the two-way fixed effect model. The results show that digital technology will significantly reduce the concentration of fine particles in the air and help protect the atmospheric environment. The results are still valid after using the interactive fixed effect model and the two-stage least square method after the robustness test and causality identification. Digital technology can also reduce the air pollution by promoting green innovation, improving energy efficiency, and easing market segmentation. The effect of digital technology on reducing the concentration of fine particles in the air is heterogeneous. Digital technology plays a more substantial role in reducing pollution in resource-based cities and areas with a high degree of modernization of the commodity supply chain. The positive effect of digital technology in reducing air pollution is affected by the amount of air pollutants emitted. When the concentration of PM2.5 in the air is high, the role of digital technology in protecting the atmosphere will be strongly highlighted. This research is a beneficial exploration of protecting the atmospheric environment by using digital technology while building an ecological civilization society. The conclusion will help urban managers, the public, and business operators entirely use modern equipment such as 5G, remote sensing, and the Internet of Things in their respective fields to protect the atmospheric environment.

We use the universe of birth records in the United States from 2013 to 2018 to examine the effect of e-cigarette tax rates on pre-pregnancy smoking and prenatal smoking. We study these questions using two-way fixed effects models and pregnancy fixed effects models. We show that e-cigarette taxes increase pre-pregnancy smoking, increase prenatal smoking, and lower smoking cessation during pregnancy. These findings imply that e-cigarettes and traditional cigarettes are substitutes among pregnant women. We also find evidence that e-cigarette taxes reduce pre-pregnancy and third trimester e-cigarette use.

This paper discusses a method for getting a basis set of estimable functions of model parameters in a two-way fixed effects model. Since the fixed effects model has more parameters than those that can be estimated, model parameters are not estimable. So it is not possible to make inferences for nonestimable functions of parameters. When the assumed model of matrix notation is reparameterized by the estimable functions in a basis set, it also discusses how to use projections for the estimation of estimable functions.

Analyzing Variation in the Cross-Section and Over Time: A Reassessment of Fixed Effects

The influence of market segmentation on energy efficiency in electric power industry: Empirical evidence from China

ObjectivesPromoting equity in healthcare resource allocation (EHRA) has become a critical political agenda of governments at all levels since the ambitious Universal Health Coverage was launched in China in 2009, while the role of an important institutional variable—fiscal autonomy of subnational governments—is often overlooked. The present study was designed to determine the effect of FASG on EHRA and its potential mechanism of action and heterogeneity characteristics to provide empirical support for the research field expansion and relative policies making of EHRA.MethodsFrom the start, we utilized the Theil index and the entropy method to calculate the EHRA index of 22 provinces (2011–2020) based on the medical resource data of 287 prefecture-level cities. Furthermore, we used the two-way fixed effects model (FE) to identify and analyze the impact of FASG on EHRA and then used three robustness test strategies and two-stage least squares (2SLS) regression to verify the reliability of the conclusions and deal with potential endogeneity problems, respectively. At last, we extend the baseline regression model and obtain the two-way FE threshold model for conducting heterogeneity analysis, which makes us verify whether the baseline model has nonlinear characteristics.ResultsThe static value and the trend of interannual changes in the EHRA values in different provinces are both very different. The regression results of the two-way FE model show that FASG has a significant positive impact on EHRA, and the corresponding estimated coefficient is – 0.0849 (P < 0.01). Moreover, this promotion effect can be reflected through two channels: enhancing the intensity of government health expenditure (IGHE) and optimizing the allocation of human resources for health (AHRH). At last, under the different economic and demographic constraints, the impact of FASG on EHRA has nonlinear characteristics, i.e., after crossing a specific threshold of per capita DGP (PGDP) and population density (PD), the promotion effect is reduced until it is not statistically significant, while after crossing a particular threshold of dependency ratio (DR), the promotion effect is further strengthened and still statistically significant.ConclusionsFASG plays an essential role in promoting EHRA, which shows that subnational governments need to attach great importance to the construction of fiscal capability in the allocation of health care resources, effectively improve the equity of medical and health fiscal expenditures, and promote the sustainable improvement of the level of EHRA.

Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the UnitedStates.