Norimah Rambeli

A notable feature of empirical studies on the unknowns about future economic events that lead to precautionary demand for money among economic agents is that very few studies put emphasis on the role of institutional development in mediating the relationship between them. To overcome this shortcoming, this study examines the threshold effect of institutional development on the relationship between precautionary demand for money and economic uncertainty, without neglecting the roles of income, the interest rate and the exchange rate in the money demand function in 28 economies. The empirical results from the panel threshold methods demonstrate significant threshold effect for these determinants, especially for economic uncertainty, which indicate that a better understanding that the relationship between the precautionary demand for money and economic uncertainty may be contingent on institutional development could help policy makers fine-tune the money demand function in a more precise manner to achieve the optimal liquidity provision under economic uncertainty over time.

This study explores the asymmetric impact of monetary policy on financial deepening in Saudi Arabia from 1992 to 2022. It uses the nonlinear autoregressive distributed lag (NARDL) bounds-testing method to show that there is strong evidence of a long-term link between financial deepening and the sums of monetary policy and control variables. The findings indicate that financial deepening responds nonlinearly to monetary policy shocks. Specifically, both positive and negative shifts in monetary policy encourage financial deepening, but positive shifts have a more significant effect. Additionally, monetary uncertainty and economic activity levels notably influence financial deepening. The study suggests that policymakers should use tools like changing interest rates and managing inflation to help the economy become more diverse, deepen the financial system, and lower short-term monetary risks. This will ensure that the financial sector continues to grow.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

Insurance fraud claims have become a major problem in the insurance industry. Several investigations have been carried out to eliminate negative impacts on the insurance industry as this immoral act has caused the loss of billions of dollars. In this paper, a comparative study was carried out to assess the performance of various classification models, namely logistic regression, neural network (NN), support vector machine (SVM), tree augmented naïve Bayes (NB), decision tree (DT), random forest (RF) and AdaBoost with different model settings for predicting automobile insurance fraud claims. Results reveal that the tree augmented NB outperformed other models based on several performance metrics with accuracy (79.35%), sensitivity (44.70%), misclassification rate (20.65%), area under curve (0.81) and Gini (0.62). In addition, the result shows that the AdaBoost algorithm can improve the classification performance of the decision tree. These findings are useful for insurance professionals to identify potential insurance fraud claim cases.

The impact of spatial heterogeneity on industrial outputs is a new important topic in economic geography. A considerable amount of research literature has accumulated, but the academic community lacks a systematic and comprehensive review and consensus on this topic. This study carried out research by mining the relevant classical literature. This investigation first combed the connotation of spatial heterogeneity, which is both corresponding to and related to spatial dependence. Theorists generally acknowledge that there is spatial heterogeneity in the process of industrial outputs. Then this study summarizes the logical basis, relationship coordination, measurement and other aspects of the effect of spatial heterogeneity on industrial outputs. In analyzing the impact of spatial heterogeneity on industrial outputs, we should not ignore the spatial dimension, but must also pay attention to the heterogeneity of individual enterprises. Industrial output analysis needs to be based on the relationship between spatial heterogeneity and spatial dependence. The influence of spatial heterogeneity on industrial outputs and the degree of differences among observation objects can be measured by econometric methods. The common indicators for measuring and quantitatively describing the impact of spatial heterogeneity on industrial outputs mainly include semivariogram, the spatial expansion model and the geographical weighted regression model. Finally, some directions of future research are pointed out in order to provide useful ideas for future theoretical research and industrial practice.

Management is the process of dealing with or controlling things, system or people. Many use this term in business and organizational perspectives. However, management is also applicable in economic perspective. In the existing study, the focused issue relates the managemnet of an economic tool in influencing the other economic variable. In particular, the study is about managing the credit market through the macroprudential policy and other selected macroeconomic variables namely GDP, interest rates, and property prices. The study focuses on the Indonesian credit market. To achieve the general objective, the following specific objectives are set in the following order; to detect the existence of the cointegration relationship between the variables, and to examine the dynamic interaction between the focused variables both in long run and short run. This study used quarterly data from Q1 2011 to Q4 2019[1]. The data were analyzed by using Auto Regressive Distributed Lag (ARDL). The results of the ARDL’s test give indication, in the long run the credit market is significantly and negatively affected by changes in Macroprudential policies and Property Price, and significant and positive association with GDP and interest rates. The results obtained from the Error Correction Model; which captures the short-term interactions between the focused variables had detected significant results in all factors except for property price factor. The overall findings lead us to conclude; the implementation of Macroprudential policy significantly affect the Indonesian credit market. As the implication, Indonesian government may consider Macroprudential policy as an economic tool in achieving a stable credit market in the future.

This study aims to identify which teaching and learning (TnL) method is more helpful to university students in stress management. The TnL methods compared are; the physical Face-to-Face (F2F) method which is practiced at campus during the period before Covid-19 hit the country; and the Online method, which is practiced at home during the Covid-19 period. This study uses a questionnaire as the research instrument and the input is documented in the Likert scale 5-point Likert scale. The respondents are the Universiti Pendidikan Sultan Idris (UPSI) students, who experienced both TnL methods considered in this study. The data were analyzed by using the descriptive statistical method; and the paired sample t-test inferential analysis. The findings provide an indication, the majority of the respondents admitted that they had experienced stress during both periods, but a higher mean (M=3.08) is observed during the Covid-19 period compared to before the Covid-19 period (M=2.70). The results for the stress level analysis by TnL methods recorded a moderate stress level (M=2.77) for the Face-to-Face method and a High-stress level (M=3.08) for the Online method. The paired sample t-test recorded a significant result. This provides an indication there is a significant change in stress levels experienced by the students when the teaching and learning method changed from Face-to-Face, to Online. In conclusion, changes in teaching and learning methods significantly influenced the students’ stress levels and with regards to that, from the mental health perspective, the F2F method is the better method compared to the Online method because the level of stress experienced by the students is low.

The principal point of this study is to examine the relationship between the spending pattern and consumer behavior, especially as it relates to income and financial literacy, among public and private university students in Malaysia. This study adopts Keynes's Psychological Law of Consumption theory. The foremost objective of this theoretical paper was to determine whether there is any difference in spending patterns among public and private university students and the association between parental incomes and spending behavior among public and private university students, as well as to examine the relationship between financial literacy and spending behavior among public and private university students. In addition, this study seeks to determine how well students comprehend the idea of needs and desires. The association between students’ degree of financial literacy as well as their income and their spending habits will next be examined using the data. The link between endogenous and exogenous variables will be determined over both the long and short terms using the Statistical Package for the Social Sciences (SPSS) version 23. The study includes the findings from earlier investigations. The study contributed to a new understanding for the researcher of financial behavior under the categories of needs and wants which outline the importance of financial literacy, parents’ income, and demographic factors. The study also contributes to a new idea for policymakers: adapt financial literacy programs among parents and children.

In the context of the recent financial crisis, there has been growing questions about improving the efficiency of the stock market through liberalization. Although many researchers have studied the empirical effects of financial liberalization on stock market efficiency, consistent conclusions remain elusive. This paper aims to study the long and short-run relationship between financial liberalization and stock market efficiency before and after the global crisis strike in Malaysia. Specifically, Autoregressive Distributed Lag (ARDL) is the main econometric technique applied to explore this study. This study uses quarterly time series data over the period from 1998Q1 to 2020Q4. The findings revealed that financial openness and trade openness affect the stock market positively in the short and long term. In conclusion, these results indirectly support the efficient market hypothesis in Malaysia. Findings provide some policy implications in which the country should reinforce the prerequisites of openness to avoid the potential of initial crisis by equipping in an appropriate manner to minimize the short-term costs of liberalization to achieve long-term benefits.