To overcome the curse of dimensionality, dimension reduction is important andnecessary for understanding the underlying phenomena in a variety of fields.Dimension reduction is the transformation of high-dimensional data into ameaningful representation in the low-dimensional space. It can be...

In this paper, we report returns to education in India using unit level data from the nationwide Periodic Labour Force Survey for 2018-19. OLS estimates from the classical Mincerian equation are presented. Various econometric techniques (e.g., conventional IV and heteroskedasticity-based IV...

This paper gives a computer-intensive approach to multi-step-ahead prediction of volatility in financial returns series under an ARCH/GARCH model and also under a model-free setting, namely employing the NoVaS transformation. Our model-based approach only assumes i..id innovations without...

In this paper, we report returns to education in India using unit level data from the nationwide Periodic Labour Force Survey for 2018-19. OLS estimates from the classical Mincerian equation are presented. Various econometric techniques (e.g., conventional IV and heteroskedasticity-based IV...

In this paper, we use the Chinese General Social Survey data (2010-2017) to analyse the returns to different education qualifications. We additionally compare the returns to vocational education with returns to academic education, at both the upper secondary level and the tertiary level....

This paper explores how the unequal right of enrollment for public schools between households with different tenure statuses in urban People's Republic of China (PRC) affects rental yields. In the PRC, the rental yield is as low as 2% in major cities, mostly due to the lack of property tax and...

In this paper, we apply Generalized Propensity Score matching (GPSM) method, which deals with a continuous treatment variable, to estimate the returns to education in China from 2010 to 2017. Results are compared with OLS estimates from the classical Mincerian equation, as well as estimates from...