Estimating Semiparametric Panel Data Models by Marginal Integration

We propose a new methodology for estimating semiparametric panel data models, with a primary focus on the nonparametric component. We eliminate individual effects using first differencing transformation and estimate the unknown function by marginal integration. We extend our methodology to treat panel data models with both individual and time effects. And we characterize the asymptotic behavior of our estimators. Monte Carlo simulations show that our estimator behaves well in finite samples in both random effects and fixed effects settings.Semiparametric Panel Data Model, Partially Linear, First Differencing, Marginal Integration

Panel Data Models with Interactive Fixed Effects and Multiple Structural Breaks

In this paper we consider estimation of common structural breaks in panel data models with interactive fixed effects which are unobservable. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. The Monte Carlo simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data

PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition

As two fundamental representation modalities of 3D objects, 3D point clouds and multi-view 2D images record shape information from different domains of geometric structures and visual appearances. In the current deep learning era, remarkable progress in processing such two data modalities has been achieved through respectively customizing compatible 3D and 2D network architectures. However, unlike multi-view image-based 2D visual modeling paradigms, which have shown leading performance in several common 3D shape recognition benchmarks, point cloud-based 3D geometric modeling paradigms are still highly limited by insufficient learning capacity, due to the difficulty of extracting discriminative features from irregular geometric signals. In this paper, we explore the possibility of boosting deep 3D point cloud encoders by transferring visual knowledge extracted from deep 2D image encoders under a standard teacher-student distillation workflow. Generally, we propose PointMCD, a unified multi-view cross-modal distillation architecture, including a pretrained deep image encoder as the teacher and a deep point encoder as the student. To perform heterogeneous feature alignment between 2D visual and 3D geometric domains, we further investigate visibility-aware feature projection (VAFP), by which point-wise embeddings are reasonably aggregated into view-specific geometric descriptors. By pair-wisely aligning multi-view visual and geometric descriptors, we can obtain more powerful deep point encoders without exhausting and complicated network modification. Experiments on 3D shape classification, part segmentation, and unsupervised learning strongly validate the effectiveness of our method. The code and data will be publicly available at https://github.com/keeganhk/PointMCD

Can international experience of returnee senior managers contribute to the improvement of CSR performance- Evidence from China

As the competition among countries becomes more and more intense, returnee talents with international experience play a significant role in promoting economic performance as the scarce resource, not only from the organisational level but also from the individual level, especially in developing countries. Meanwhile, the public attention has been attracted by corporate social responsibility (CSR) since the 1990s with the development of the economy. As a part of firm performance, CSR is influenced by the international experience of returnee senior managers as well, which is shown in prior research. Using the sample comprised of Chinese listed companies which are rated by RKS system during the period of 2015 to 2017, this paper investigates the relationship between international experience possessed by returnee senior managers and corporate social performance. The regression analysis reveals that both the existence and the percentage of returnee senior managers with international experience have a positive relationship with corporate social performance (i.e. CSR performance). And this effect is greater when the percentage of returnee senior managers in the top management team is higher. Furthermore, the detailed analysis shows that international work experience has a more profound impact on corporate social performance than international study experience. This paper expands the growing body of research on the determinants of corporate social performance and the economic consequences of returnee senior managers. The empirical evidence also has implications for the government to implement national policies and for companies to recruit returnee senior managers with international experience

Estimating Semiparametric Panel Data Models by Marginal Integration

We propose a new methodology for estimating semiparametric panel data models, with a primary focus on the nonparametric component. We eliminate individual effects using first differencing transformation and estimate the unknown function by marginal integration. We extend our methodology to treat panel data models with both individual and time effects. And we characterize the asymptotic behavior of our estimators. Monte Carlo simulations show that our estimator behaves well in finite samples in both random effects and fixed effects settings

Shrinkage Estimation of Regression Models with Multiple Structural Changes

Published in Econometric Theory https://doi.org/10.1017/S0266466615000237</p

Structural change estimation in time series regressions with endogenous variables

Ministry of Education, Singapore under its Academic Research Funding Tier

Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso

Ministry of Education, Singapore under its Academic Research Funding Tier 2Working Paper No. 07-2015</p

Estimating Semiparametric Panel Data Models by Marginal Integration

We propose a new methodology for estimating semiparametric panel data models, with a primary focus on the nonparametric component. We eliminate individual effects using first differencing transformation and estimate the unknown function by marginal integration. We extend our methodology to treat panel data models with both individual and time effects. And we characterize the asymptotic behavior of our estimators. Monte Carlo simulations show that our estimator behaves well in finite samples in both random effects and fixed effects settings