JOURNAL OF APPLIED MICROECONOMETRICS <p><strong>The Journal of Applied Microeconometrics (ISSN: 2791-7401)</strong> is a peer-reviewed open access journal covering any issues in theoretical and applied microeconometrics. The journal also covers quantitative research in microeconomics. Journal of Applied Microeconometrics aims to serve as a platform for high quality research in applied microeconometrics. The scope of the Journal includes any papers dealing with identification, modelling, estimation, testing and prediction issues encountered in the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for microeconometric data i.e. cross sectional data, repeated cross- sectional data, pool data, cohort and panel data etc. The journal also accepts case study articles written for both developing and developed countries. The language publication of the journal is <strong>English. </strong>It is published<strong> 2 times</strong> a year as SUMMER (June) and WINTER (December) periods.</p> en-US (Aysu Şahintepe) (Dr. Cumali YAŞAR) Wed, 05 Jul 2023 12:15:05 +0300 OJS 60 A semi-nonparametric extended ordered probit model with selection for financial barrier perception <p>In order to contribute to the literature concerning the difficulties faced by innovative firms in terms of financing, this paper aims to investigate the perception levels of financial barriers according to their innovation intensity and analyzes determinants of financial barriers for a developing country for the most recent years. A semi-nonparametric extended ordered probit model with selection is used to establish the determinants of perception of financial barriers by employing the Business Enterprise and Environment Survey, BEEPS 2013 and BEEPS 2019. According to the findings, when there is an engagement in innovation activities, then firms are more likely to assess financial barriers as important. It is believed that these results have important implications for developing countries.</p> Hülya Ünlü Copyright (c) 2023 Holistence Publications Wed, 05 Jul 2023 00:00:00 +0300 Determinants of household savings rates: Logistic quantile regression approach <p>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.</p> Şaban Kızılarslan, Serdar Göcen Copyright (c) 2023 Holistence Publications Wed, 05 Jul 2023 00:00:00 +0300 Comparison of GARCH and SVRGARCH models: Example of gold return <p>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.</p> Zeynep Şengül Copyright (c) 2023 Holistence Publications Wed, 05 Jul 2023 00:00:00 +0300