Empirical Economics最新文献

Building on the GJR-GARCH model, this paper uses the mixed-data sampling (MIDAS) approach to link monthly realized volatility of EU carbon future prices and macroeconomic variables to the volatility of EU carbon futures market and proposes the GJR-GARCH-MIDAS model incorporating macroeconomic variables including the economic sentiment indicator of the EU, the harmonized index of consumer prices of the EU, the European economic policy uncertainty index and ECB’s marginal lending facility rate (GJR-GARCH-MIDAS-X models). An empirical analysis based on the monthly macroeconomic variables and daily EUA futures data shows that the above four low-frequency macroeconomic variables have significant positive or negative impacts on the long-term volatility of EUA future prices, respectively. The GJR-GARCH-MIDAS-X models significantly outperform other competing models, including the GJR-GARCH model, GARCH-MIDAS model and standard GJR-GARCH-MIDAS model, in terms of out-of-sample volatility forecasting, which suggests that macroeconomic variables contain important information for EUA future price volatility forecasts. In particular, the GJR-GARCH-MIDAS model with harmonized index of consumer prices (HICP) (GJR-GARCH-MIDAS-HICP model) performs best in out-of-sample volatility forecasting, and our findings are robust to different forecasting windows.

Abstract

The article presents a robust quantitative approach for determining significant economic factors for sex trafficking in the United States. The aim is to study monthly counts of sex trafficking-related convictions, and use a wide range of economic variables as covariates to investigate their effect on conviction counts. A count time series model is considered along with a regression setup to include economic time series as covariates (economic factors) to explain the counts on sex trafficking-related convictions. The statistical significance of these economic factors is investigated and the significant factors are ranked based on appropriate model selection methods. The inclusion of time-lagged versions of the economic factor time series in the regression model is also explored. Our findings indicate that economic factors relating to immigration policy, consumer price index and labor market regulations are the most significant in explaining sex trafficking convictions.

Using the Consumer Expectations Survey of the ECB, I estimate how individual expectations on core economic outcomes changed in France, Germany, Italy, and Spain right after the beginning of the Ukraine–Russia war. I find that individuals expected lower economic growth and higher inflation. The effect of the war was larger in the countries with a higher energy-imports dependency. Hence, the expectation formation process might have changed.

This study employs Chilean administrative data to investigate the impact of school starting age on the characteristics of students’ initial enrolled schools. Employing minimum age requirements and an RD design to mitigate endogeneity concerns, we identify benefits linked to commencing school at a later age. Our findings demonstrate that children starting school at an older age enroll in institutions with higher average scores in standardized tests and interact with older peers whose parents have higher education levels. Furthermore, they display a heightened likelihood of entering schools employing academic selection methods, a greater proportion of full-time teachers, and a larger percentage of instructors with a 4-year college degree. The analysis by level of education of the parents and gender reveals that most of our results are driven by parents with lower levels of education and girls. Subgroup analyses further reveal that many of our results are driven by parents with lower levels of education and parents of girls.

This paper studies whether forced increases in the residual working life, determined by a restrictive pension reform, induce additional training activities. By exploiting a sizable Italian pension reform, in a difference-in-differences setting, I find that a lengthening of the working horizon increases, through training, workers’ human capital. Additionally, I show that the response to the reform appears very heterogeneous and depends on gender, age, education, marital status, sector of employment and firm size. My estimates suggest, furthermore, that these individual positive effects are not attributable to employers’ sponsorship.

With mean instrumental variables regression, k-class estimators have the potential to reduce bias, which is larger with weak instruments. With instrumental variables quantile regression, weak instrument-robust estimation is even more important because there is less guidance for assessing instrument strength. Motivated by this, we introduce an analogous k-class of estimators for instrumental variables quantile regression. We show the first-order asymptotic distribution under strong instruments is equivalent for all conventional choices of k. We evaluate finite-sample median bias in simulations for a variety of k, including the k for the conventional k-class estimator corresponding to limited information maximum likelihood (LIML). Computation is fast for all k, and compared to the (k=1) benchmark estimator (analogous to 2SLS), using the LIML k reliably reduces median bias in a variety of data-generating processes, especially when the degree of overidentification is larger. We also revisit some empirical estimates of consumption Euler equations derived from quantile utility maximization. All code is provided online (https://kaplandm.github.io).

Abstract

In this paper, we consider a fractionally integrated multi-level dynamic factor model (FI-ML-DFM) to represent commonalities in the hourly evolution of realized volatilities of several international exchange rates. The FI-ML-DFM assumes common global factors active during the 24 h of the day, accompanied by intermittent factors, which are active at mutually exclusive times. We propose determining the number of global factors using a distance among the intermittent loadings. We show that although the bulk of common dynamics of exchange rates realized volatilities can be attributed to global factors, there are non-negligible effects of intermittent factors. The effect of the COVID-19 on the realized volatility comovements is stronger on the first global-in-time factor, which shows a permanent increase in the level. The effects on the second global factor and on the intermittent factors active when the EU, UK and US markets are operating are transitory lasting for approximately a year after the pandemic starts. Finally, there seems to be no effect of the pandemic neither on the third global factor nor on the intermittent factor active when the markets in Asia are operating.

This paper proposes a nonlinear fractional unit root approach which is known as the autoregressive neural network–fractional integration (ARNN–FI) test. This new fractional integration test is based on a new multilayer perceptron of a neural network process, proposed in Yaya et al. (Oxf Bull Econ Stat 83(4):960–981, 2021). The asymptotic theory and the properties of the proposed test are given. By setting up a Monte Carlo simulation experiment, the simulation results reveal that as the number of observations increases, size and power distortions would disappear in the test. The empirical application based on this new test reveals that the unemployment rates of three European countries are neither stationary nor mean-reverting in line with the hysteresis hypothesis.

Study objectives: In coronary artery bypass grafting (CABG), abnormal cardiac repolarization is associated with adverse cardiovascular events that can be measured via the QTc interval. We investigated the impact of obstructive sleep apnea on the change in repolarization after CABG and the association of change in repolarization with the occurrence of major adverse cardiac and cerebrovascular events.

Methods: A total of 1,007 patients from 4 hospitals underwent an overnight sleep study prior to a nonemergent CABG. Electrocardiograms of 954 patients (median age: 62 years; male: 86%; mean follow-up: 2.1 years) were acquired prospectively within 48 hours before CABG (T1) and within 24 hours after CABG (T2). QTc intervals were measured using the BRAVO algorithm by Analyzing Medical Parameters for Solutions LLC. The change in T2 from T1 for QTc (ΔQTc) was derived, and Cox regression was performed.

Results: Compared with those without, patients who developed major adverse cardiac and cerebrovascular events (n = 115) were older and had (1) a higher prevalence of smoking, hypertension, diabetes mellitus, and chronic kidney disease; (2) a higher apnea-hypopnea index and oxygen desaturation index; and (3) a smaller ΔQTc. Cox regression analysis demonstrated a smaller ΔQTc to be an independent risk factor for major adverse cardiac and cerebrovascular events (hazard ratio: 0.997; P = .032). In the multivariable regression model, a higher oxygen desaturation index was independently associated with a smaller ΔQTc (correlation coefficient: -0.58; P < .001).

Conclusions: A higher preoperative oxygen desaturation index was an independent predictor of a smaller ΔQTc. ΔQTc within 24 hours after CABG could be a novel predictor of occurrence of major adverse cardiac and cerebrovascular events at medium-term follow-up.

Clinical trial registration: Registry: ClinicalTrials.gov; Name: Undiagnosed Sleep Apnea and Bypass OperaTion (SABOT); URL: https://classic.clinicaltrials.gov/ct2/show/NCT02701504; Identifier: NCT02701504.

Citation: Teo YH, Yong CL, Ou YH, et al. Obstructive sleep apnea and temporal changes in cardiac repolarization in patients undergoing coronary artery bypass grafting. J Clin Sleep Med. 2024;20(1):49-55.