Consistent Model Selection Procedure for Random Coefficient INAR Models.

In the realm of time series data analysis, information criteria constructed on the basis of likelihood functions serve as crucial instruments for determining the appropriate lag order. However, the intricate structure of random coefficient integer-valued time series models, which are founded on thinning operators, complicates the establishment of likelihood functions. Consequently, employing information criteria such as AIC and BIC for model selection becomes problematic. This study introduces an innovative methodology that formulates a penalized criterion by utilizing the estimation equation within conditional least squares estimation, effectively addressing the aforementioned challenge. Initially, the asymptotic properties of the penalized criterion are derived, followed by a numerical simulation study and a comparative analysis. The findings from both theoretical examinations and simulation investigations reveal that this novel approach consistently selects variables under relatively relaxed conditions. Lastly, the applications of this method to infectious disease data and seismic frequency data produce satisfactory outcomes.

An Observation-Driven Random Parameter INAR(1) Model Based on the Poisson Thinning Operator.

This paper presents a first-order integer-valued autoregressive time series model featuring observation-driven parameters that may adhere to a particular random distribution. We derive the ergodicity of the model as well as the theoretical properties of point estimation, interval estimation, and parameter testing. The properties are verified through numerical simulations. Lastly, we demonstrate the application of this model using real-world datasets.

A New Overdispersed Integer-Valued Moving Average Model with Dependent Counting Series.

A new integer-valued moving average model is introduced. The assumption of independent counting series in the model is relaxed to allow dependence between them, leading to the overdispersion in the model. Statistical properties were established for this new integer-valued moving average model with dependent counting series. The Yule-Walker method was applied to estimate the model parameters. The estimator's performance was evaluated using simulations, and the overdispersion test of the INMA(1) process was applied to examine the dependence between counting series.

Effect of technological progress on carbon emissions: New evidence from a decomposition and spatiotemporal perspective in China.

The continuously increasing carbon emissions have become a significant hurdle for global sustainable development. Technological progress is considered essential for controlling carbon emissions. However, previous literature has analyzed technological progress as a whole, largely ignoring its spatial spillovers. Therefore, our understanding of how technological progress influences carbon emissions is still limited. To fill this gap, we conduct an in-depth analysis of the effect of technological progress regarding carbon emissions by introducing a new framework that combines the slacks-based measure of the Malmquist-Luenberger index and the spatial dynamic model. Employing a Chinese provincial panel dataset for 2000-2016 as a case study, the conventional analysis indicates that both technological progress and its components have not played a significant role in decreasing carbon emissions. A further analysis using the spatial dynamic model suggests that the technological progress of neighbouring regions plays a significant role in reducing carbon emissions. Moreover, the effect of efficiency change is stronger than that of technical change, which provides new evidence on how technological progress influences carbon emissions.

Absolute Income, Income Inequality and the Subjective Well-Being of Migrant Workers in China: Toward an Understanding of the Relationship and Its Psychological Mechanisms.

No study has been conducted linking Chinese migrants' subjective well-being (SWB) with urban inequality. This paper presents the effects of income and inequality on their SWB using a total of 128,000 answers to a survey question about "happiness". We find evidence for a satiation point above which higher income is no longer associated with greater well-being. Income inequality is detrimental to well-being. Migrants report lower SWB levels where income inequality is higher, even after controlling for personal income, a large set of individual characteristics, and province dummies. We also find striking differences across socio-economic and geographic groups. The positive effect of income is more pronounced for rural and western migrants, and is shown to be significantly correlated with the poor's SWB but not for the well-being of more affluent respondents. Interestingly, high-income earners are more hurt by income inequality than low-income respondents. Moreover, compared with migrants in other regions, those in less developed Western China are found to be more averse to income inequality. Our results are quite robust to different specifications. We provide novel explanations for these findings by delving into psychological channels, including egalitarian preferences, social comparison concerns, expectations, perceived fairness concerns and perceived social mobility.