Journal of Applied Microeconometrics最新文献

Determinants of household savings rates: Logistic quantile regression approach 家庭储蓄率的决定因素：逻辑量值回归法

Journal of Applied Microeconometrics Pub Date : 2023-07-05 DOI: 10.53753/jame.1944

Şaban Kızılarslan, Serdar Göcen

{"title":"Determinants of household savings rates: Logistic quantile regression approach","authors":"Şaban Kızılarslan, Serdar Göcen","doi":"10.53753/jame.1944","DOIUrl":"https://doi.org/10.53753/jame.1944","url":null,"abstract":"Saving rates have a fundamental economic importance that affects the economic performance of countries and the welfare level of individuals. Savings have been addressed in various ways with alternative economic approaches. The determinants of household savings rates were examined at the level of quantiles in this study. For this purpose, the logistic quantile regression approach proposed for bounded dependent variables was used. Since savings rates have a bounded and continuous structure, it is appropriate to analyze them with this method. Income level has been considered the principal determinant of savings rates and the change in the effect of income on savings rate at the level of quantiles was examined in details. As a result of the analysis performed separately for homeowners and tenants, it was determined that there were differences between the two groups. The change in the income effect was non-linear at the quantile level in both groups. While income was more effective at high savings rates for homeowners, it was more effective at low savings rates for tenants. On the other hand, the effects of other characteristics of the households also differed between the homeowners and the tenants at the level of the quantiles.","PeriodicalId":105896,"journal":{"name":"Journal of Applied Microeconometrics","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139362543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of GARCH and SVRGARCH models: Example of gold return GARCH 模型与 SVRGARCH 模型的比较：黄金回报率实例

Journal of Applied Microeconometrics Pub Date : 2023-07-05 DOI: 10.53753/jame.2068

Zeynep Şengül

{"title":"Comparison of GARCH and SVRGARCH models: Example of gold return","authors":"Zeynep Şengül","doi":"10.53753/jame.2068","DOIUrl":"https://doi.org/10.53753/jame.2068","url":null,"abstract":"Gold has been a precious resource for people on earth from the past to the present. It is used as both a value gain and jewelry, and is the focus of interest for people in terms of receiving attention and protecting its value. Especially recently, it has been the most favorite for investors due to its excess value increase and decrease which is constantly monitored. The study aimed to compare the predictive performance of the gold price return using the Support Vector Regression-GARCH hybrid models combined with the traditional volatility models. It has been examined whether the Support Vector Regression GARCH models would increase foresight performance. The study used data on the daily frequency between 01/01/2010–01/04/2023. Generalized Autoregressive Conditional Variable Variance, Glosten-Jaganthan-Runkle GARCH, Exponential GARCH and hybrid model Support Vector Regression -GARCH are utilized as prediction methods. For all methods, the gold series is divided into two groups as training and test data. The Root Mean Square Error values are compared as a model performance criterion. The RMSE values and graphics outputs have been concluded that the Support Vector Regression-GARCH model based on predicted linear, radial-based and polynomial kernel predicts more effectively than the GARCH models.","PeriodicalId":105896,"journal":{"name":"Journal of Applied Microeconometrics","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139362623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A semi-nonparametric extended ordered probit model with selection for financial barrier perception 金融障碍认知的半非参数扩展有序概率模型（带选择功能

Journal of Applied Microeconometrics Pub Date : 2023-07-05 DOI: 10.53753/jame.2097

H. Ünlü

引用次数: 0

Income inequality-labor productivity relationship: CS-ARDL approach 收入不平等-劳动生产率关系:CS-ARDL方法

Journal of Applied Microeconometrics Pub Date : 2021-12-29 DOI: 10.53753/jame.1.2.02

H. Bağlıtaş

{"title":"Income inequality-labor productivity relationship: CS-ARDL approach","authors":"H. Bağlıtaş","doi":"10.53753/jame.1.2.02","DOIUrl":"https://doi.org/10.53753/jame.1.2.02","url":null,"abstract":"With the acceleration of globalization, “Reducing Inequalities”, which is the 10th of the sustainable development goals, has started to attract more attention in the world. Many factors lead to inequality. Therefore, inequality requires consensus and strength at the interdisciplinary, local, national, and international levels. The leading indicator of inequality is income inequality. Its measurability and widespread impact are sources of its importance and priority. Unfair income distribution might have unfavorable effects on employees such as being more reluctant to work and the well-being of workers. In addition, if workers believe they earn less than they deserve, this might negatively affect the labor productivity. Ultimately, this process may cause countries to reduce their production output.\u0000\u0000This study aims to explore the link between income inequality and labor productivity among 31 countries in Europe with the period of 2005-2019. To do this, a cross-sectional auto-regressive distributed lag model (CS-ARDL) is employed. According to the results, wage inequality damages the productivity of labor. A 1% increase in the wage inequality reduces labor productivity by 0.16%. Moreover, the unequal income distribution has an explanatory power of approximately 33% on the decrease in productivity. This helps to determine the possible effects of the unequal income distribution leading towards two targets. These targets are to create an efficient wage structure and eliminate the destructive effects of inequality, respectively. In terms of the policy effectiveness, simultaneous application of tools may be more beneficial.","PeriodicalId":105896,"journal":{"name":"Journal of Applied Microeconometrics","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128457016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Customer churn analysis in banking sector: Evidence from explainable machine learning models 银行业客户流失分析:来自可解释机器学习模型的证据

Journal of Applied Microeconometrics Pub Date : 2021-12-29 DOI: 10.53753/jame.1.2.03

H. Guliyev, Ferda Yerdelen Tatoğlu

{"title":"Customer churn analysis in banking sector: Evidence from explainable machine learning models","authors":"H. Guliyev, Ferda Yerdelen Tatoğlu","doi":"10.53753/jame.1.2.03","DOIUrl":"https://doi.org/10.53753/jame.1.2.03","url":null,"abstract":"Although large companies try to gain new customers, they also want to retain their old customers. Therefore, customer churn analysis is important for identifying old customers without loss and developing new products and making new strategic decisions for retaining customers. This study focuses on the customer churn analysis, that is a significant topic in banks customer relationship management. Identifying customer churn in banks will helps the management to classification who are likely to churn early and target customers using promotions, as well as provide insight into which factors should be considered when retaining customers. Although different models are used for customer churn analysis in the literature, this study focuses on especially explainable Machine Learning models and uses SHapely Additive exPlanations (SHAP) values to support the machine learning model evaluation and interpretability for customer churn analysis. The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customers.","PeriodicalId":105896,"journal":{"name":"Journal of Applied Microeconometrics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128796698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Returns to education: Empirical evidence from Kyrgyzstan 回归教育:来自吉尔吉斯斯坦的经验证据

Journal of Applied Microeconometrics Pub Date : 2021-12-29 DOI: 10.53753/jame.1.2.01

B. Sulaimanova

引用次数: 0

Volume 1, Issue 1 第1卷第1期

Journal of Applied Microeconometrics Pub Date : 2019-10-30 DOI: 10.37435/nbr-01-01

Nayan Krishna Joshi

引用次数: 43