scholarly journals Models of multiple regression as an instrument of regional mortgage markets’ functioning effectiveness assessment

Author(s):  
Tatyana S. Korosteleva ◽  
Vladimir E. Tselin

The article is dedicated to issues related to the formation of the Russian Federation regions economically justified classifications by housing mortgage lending (HML) systems stage of development, for the implementation of mortgage markets selective government adjustment policy. The purpose of the study is to develop the methodology of regions mortgage potential assessment based on the search of additional application solutions in the area of rapid assessment and forecasting of regional mortgage markets state. To achieve the purpose, an additional version of common methodology of Russian Federation regions systematic ranking by HML systems stage of development is represented, and its main steps are structured. Within the third step of new methodology, a multiple regression model of Factor 1 (F1) is developed, which was interpreted in authors early studies as a performance indicator of regional mortgage markets functioning effectiveness. The models development was based on the statistical processings results of 510 observations in 85 Russian Federation regions in a period from 2014 to 2019. The high accuracy of models approximation has been proved, as well as its statistical reliability. The application capabilities of the model are represented, particularly the results of period 2020-2021 forecasting for Samara region and Saint-Petersburg city. The ways of managing the rating of region through targeted distribution of available regional and federal resources received by the models predictors are outlined. The novelty of this study is contained in the results of adaptation of the new assessment methodology, which is based on the obtainment of multiple regression model for Factor 1. The methodology base of the study consists of systematic analysis methods and multivariate statistics, particularly regressive and correlation analysis methods.

Paradigm ◽  
2021 ◽  
Vol 25 (2) ◽  
pp. 181-193
Author(s):  
Nitya Garg

Banking sector is the backbone of any economy, so it is necessary to focus on its performance which is largely affected by its non-performing assets (NPAs). In the year 2018–2019, NPA of scheduled banks was Rs 355,076 Crore which is 3.7% of net advances. The purpose of this study is to identify the determinants based on analysis from previous literatures, and majorly macroeconomic and bank specific factors which are affecting NPAs using the relative weight analysis and to frame a model to predict future NPAs using multiple regression model using SPSS. The study also attempts to focus on actions and remedies that banks should make to control future NPAs. Findings of the study will act as a scaffolding for financial analysts and policymakers to prevent the conversion of its performing assets into NPAs and also help in proper management of banks and also in the recovery of economy.


2020 ◽  
Vol 12 (07) ◽  
pp. 527-544
Author(s):  
Assoué Kouakou Sylvestre Kouadio ◽  
Ouedraogo Moussa ◽  
Ismaïla Ouattara ◽  
Issiaka Savane

2014 ◽  
Vol 644-650 ◽  
pp. 5319-5324
Author(s):  
Tian Jiu Leng

In this paper, the relevant factors of PM2.5 and the degree of correlation between them were analyzed.The multiple regression model was established using stepwise regression analysis method and the temporal spatial evolution of PM2.5 was obtained by setting the initial and boundary conditions.


2005 ◽  
Vol 82 (4) ◽  
pp. 414-419 ◽  
Author(s):  
Hiroshi Okadome ◽  
Hidechika Toyoshima ◽  
Naoto Shimizu ◽  
Keitaro Suzuki ◽  
Ken'ichi Ohtsubo

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