Business Credit, Household Credit and Economic Performance in Malaysia: A Quantile Regression Approach

This study examines the effects of business (enterprise) credit and household credit on economic performance in Malaysia. The World Bank’s Doing Business report ranked Malaysia at number one among developing countries in terms of ease of getting credit in the six consecutive years since 2008. The analysis is based on quantile regression estimations, using quarterly time series datasets from 1999: Q4 to 2019: Q4. The empirical findings reveal that business credit is positively associated with economic performance whereas household credit is an insignificant determinant of economic performance. We also consider the interaction between credit and institutional quality, an emerging key fundamental variable that determines economic performance. The results demonstrate that only the interaction term between business credit and institutions is statistically significant. In short, business credit outperforms household credit in promoting economic performance in Malaysia. The empirical findings are robust to alternative control variables and quantile regression estimation techniques.

Analysis of Potential Factors Influencing China’s Regional Sustainable Economic Growth

The purpose of this article is to screen out the most important factors affecting China’s economic growth. Based on a literature review and relevant financial theoretical knowledge, China’s economic growth factors are selected from international and domestic aspects. Four methods, including least squares estimation, stepwise regression, ridge regression estimation, and Lasso regression, are used to screen and optimize 12 variables and analyze the degrees of influence empirically. The study finds that consumption levels and the development of the tertiary industry play significant roles in promoting China’s economic growth. Additionally, financial development and industrialization promote China’s economic growth, although in a gradual manner.

The One Standard Error Rule for Model Selection: Does It Work?

Previous research provided a lot of discussion on the selection of regularization parameters when it comes to the application of regularization methods for high-dimensional regression. The popular “One Standard Error Rule” (1se rule) used with cross validation (CV) is to select the most parsimonious model whose prediction error is not much worse than the minimum CV error. This paper examines the validity of the 1se rule from a theoretical angle and also studies its estimation accuracy and performances in applications of regression estimation and variable selection, particularly for Lasso in a regression framework. Our theoretical result shows that when a regression procedure produces the regression estimator converging relatively fast to the true regression function, the standard error estimation formula in the 1se rule is justified asymptotically. The numerical results show the following: 1. the 1se rule in general does not necessarily provide a good estimation for the intended standard deviation of the cross validation error. The estimation bias can be 50–100% upwards or downwards in various situations; 2. the results tend to support that 1se rule usually outperforms the regular CV in sparse variable selection and alleviates the over-selection tendency of Lasso; 3. in regression estimation or prediction, the 1se rule often performs worse. In addition, comparisons are made over two real data sets: Boston Housing Prices (large sample size n, small/moderate number of variables p) and Bardet–Biedl data (large p, small n). Data guided simulations are done to provide insight on the relative performances of the 1se rule and the regular CV.

Selection of Optimal Smoothing Parameters in Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression

In this article, we propose a new method of selecting smoothing parameters in semiparametric regression. This method is used in semiparametric regression estimation where the nonparametric component is partially approximated by multivariable Fourier Series and partly approached by multivariable Kernel. Selection of smoothing parameters using the method with Generalized Cross-Validation (GCV). To see the performance of this method, it is then applied to the data drinking water quality sourced from Regional Drinking Water Company (PDAM) Surabaya by using Fourier Series with trend and Gaussian Kernel. The results showed that this method contributed a good performance in selecting the optimal smoothing parameters.

Thin nanocrystalline semiconductor films as selective chemical sensors for ammonia, acetone and other donors

Studied are the films of variously doped polycrystalline n-semiconductors (ZnO, SnO2) as selective sensitive elements (SE) of chemical sensors for various gases and vapours (ammonia, acetone, propane, ethanol, hexane, solvent, turpentine etc.) in artificial air. It has been revealed that their conductivity changes under temperature modulation makes possible data processing which identifies the impurities above. This processing is based on nonlinear regression estimation of so called principal parameters which set is unique for every concentration of every of the gases/vapours.