The absolute health income hypothesis revisited: A Semiparametric Quantile Regression Approach.

This paper uses the 1998-99 Canadian National Population Health Survey (NPHS) data to examine the health-income relationship that underlies the absolute income hypothesis. To allow for nonlinearity and data heterogeneity, we use a partially linear semiparametric quantile regression model. The “absolute income hypothesis” is partially true; the negative aging effects appear more pronounced for the illhealthy population than for the healthy population and when annual income is below 40,000 Canadian dollars.Absolute income hypothesis · Partially linear quantile

The Absolute Health Income Hypothesis Revisited : A Semiparametric Quantile Regression Approach

This paper uses the 1998-99 Canadian National Population Health Survey (NPHS) data to examine the health-income relationship that underlies the absolute income hypothesis. To allow for nonlinearity and data heterogeneity, we use a partially linear semiparametric quantile regression model. Among more than dozen of socioeconomic variables, we find that family income, age and the food security status are the most important factors in explaining an individual's overall functional health. The "absolute income hypothesis" is partially true; the negative aging effects appear more pronounced for the ill-healthy population than for the healthy population and when annual income is below 40,000 Canadian dollars.

Vibration characteristics and environmental responses of different vehicle-track-ballast coupling systems in subway operation

The vibration characteristics of two ballast beds are analyzed in this study from five aspects including the amplitude-frequency characteristic curve of foundation reaction. This study also shows that the maximum ground Z vibration level caused by a normal monolithic ballast bed structure is 75 dB. The range of its vibration influence during daytime is approximately 30 m. The maximum ground Z vibration level caused by a rubber floating slab track structure is 52 dB, whereas that caused by a steel spring floating slab track structure is 57 dB. The maximum damping amount in horizontal speed of a rubber floating slab track structure is 74 %, whereas the reduction of vertical ground vibration speed and acceleration is 92 % and 93 %, respectively. The reduction in Z level is 37 %. The horizontal speed reduction in a steel spring floating slab track structure is 71 %, whereas the reduction of ground vertical vibration speed and acceleration is 83 % and 84 %, respectively. The reduction in Z level is 29 %

Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models

This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and parameter heterogeneity in the spatial relationship. Unlike other semiparametric spatial dependence models, ours permits the spatial autoregressive parameter to meaningfully vary across units and thus allows the identification of a neighborhood-specific spatial dependence measure conditional on the vector of contextual variables. We propose several (locally) nonparametric GMM estimators for our model. The developed two-stage estimators incorporate both the linear and quadratic orthogonality conditions and are capable of accommodating a variety of data generating processes, including the instance of a pure spatially autoregressive semiparametric model with no relevant regressors as well as multiple partially linear specifications. All proposed estimators are shown to be consistent and asymptotically normal. We also contribute to the literature by putting forward two test statistics to test for parameter constancy in our model. Both tests are consistent

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance

Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects

This paper develops an innovative way of estimating a functional-coefficient spatial autoregressive panel data model with unobserved individual effects which can accommodate (multiple) time-invariant regressors in the model with a large number of cross-sectional units and a fixed number of time periods. The methodology we propose removes unobserved fixed effects from the model by transforming the latter into a semiparametric additive model, the estimation of which however does not require the use of backfitting or marginal integration techniques. We derive the consistency and asymptotic normality results for the proposed kernel and sieve estimators. We also construct a consistent nonparametric test to test for spatial endogeneity in the data. A small Monte Carlo study shows that our proposed estimators and the test statistic exhibit good finite-sample performance

Varying Coefficient Panel Data Model in the Presence of Endogenous Selectivity and Fixed Effects

This paper considers a flexible panel data sample selection model in which (i) the outcome equation is permitted to take a semiparametric, varying coefficient form to capture potential parameter heterogeneity in the relationship of interest, (ii) both the outcome and (parametric) selection equations contain unobserved fixed effects and (iii) selection is generalized to a polychotomous case. We propose a two-stage estimator. Given consistent parameter estimates from the selection equation obtained in the first stage, we estimate the semiparametric outcome equation using data for the observed individuals whose likelihood of being selected into the sample stays approximately the same over time. The selection bias term is then "asymptotically" removed from the equation along with fixed effects using kernel-based weights. The proposed estimator is consistent and asymptotically normal. We first investigate the finite sample properties of the estimator in a small Monte Carlo study and then apply it to study production technologies of U.S. retail credit unions from 2002 to 2006

Varying Coefficient Panel Data Model in the Presence of Endogenous Selectivity and Fixed Effects

This paper considers a flexible panel data sample selection model in which (i) the outcome equation is permitted to take a semiparametric, varying coefficient form to capture potential parameter heterogeneity in the relationship of interest, (ii) both the outcome and (parametric) selection equations contain unobserved fixed effects and (iii) selection is generalized to a polychotomous case. We propose a two-stage estimator. Given consistent parameter estimates from the selection equation obtained in the first stage, we estimate the semiparametric outcome equation using data for the observed individuals whose likelihood of being selected into the sample stays approximately the same over time. The selection bias term is then "asymptotically" removed from the equation along with fixed effects using kernel-based weights. The proposed estimator is consistent and asymptotically normal. We first investigate the finite sample properties of the estimator in a small Monte Carlo study and then apply it to study production technologies of U.S. retail credit unions from 2002 to 2006

Nanoscale mapping and spectroscopy of non-radiative hyperbolic modes in hexagonal boron nitride nanostructures

The inherent crystal anisotropy of hexagonal boron nitride (hBN) sustains naturally hyperbolic phonon polaritons, i.e. polaritons that can propagate with very large wavevectors within the material volume, thereby enabling optical confinement to exceedingly small dimensions. Indeed, previous research has shown that nanometer-scale truncated nanocone hBN cavities, with deep subwavelength dimensions, support three-dimensionally confined optical modes in the mid-infrared. Due to optical selection rules, only a few of many such modes predicted theoretically have been observed experimentally via far-field reflection and scattering-type scanning near-field optical microscopy. The Photothermal induced resonance (PTIR) technique probes optical and vibrational resonances overcoming weak far-field emission by leveraging an atomic force microscope (AFM) probe to transduce local sample expansion due to light absorption. Here we show that PTIR enables the direct observation of previously unobserved, dark hyperbolic modes of hBN nanostructures. Leveraging these optical modes could yield a new degree of control over the electromagnetic near-field concentration, polarization and angular momentum in nanophotonic applications.Comment: 14 pages with references, 4 figure

Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison

Abstract This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.</jats:p