This paper proposes a new AR-sieve bootstrap approach on high-dimensional time series. The major challenge of classical bootstrap methods on high-dimensional time series is two-fold: the curse dimensionality and temporal dependence. To tackle such difficulty, we utilise factor modelling to...

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to carry an index, and to be unknown but parametrically or...

Machine learning and artificial intelligence methods are often referred to as \black boxes" when compared to traditional regression-based approaches. However both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous...

Government regulation plays a significant role in the field of heritage conservation. Namely, regulation is aimed at controlling the stock of heritage, restricting or modifying the activities of public as well as private actors. Surprisingly, the literature has neither extensively investigated...

This study proposes a nonparametric bootstrap-based test to compare performances between two portfolios in terms of their information ratio. This serves as an extension to the literature that tests performance between two portfolio investment strategies that uses Sharpe ratio. Monte Carlo...

The study examines the efficiency differences across the ownership structure of Indian microfinance institutions (MFIs) operating during the year 2005/06 to 2017/18 in response to regulatory reforms initiated by the Reserve Bank of India (RBI) in the year 2011. We remove the outliers from the...

The confirmed approach to choosing the number of principal components for prediction models includes exploring the contribution of each principal component to the total variance of the target variable. A combination of possible important principal components can be chosen to explain a big part...