Estimating and testing interactions in linear regression models when explanatory variables are subject to classical measurement error

The main aim of the paper was the analysis of determinants of life satisfaction of people aged 50 years and more in Poland. The data used in the analysis came from the second wave of the Polish Generations and Gender Survey (GGS-PL) that had been carried out in 2014 and 2015. The linear regression models with the dependent variable describing life satisfaction were estimated. Explanatory variables, incorporated into the model, included basic socio-demographic variables as well as a variable describing loneliness. The obtained results are in line with those described in the literature. As expected, better educated individuals, with higher income are more satisfied with life. Similarly, married persons had higher life satisfaction than those living without a spouse. Moreover, persons in employment were more satisfied with life than those economically inactive, while unemployment diminishes life satisfaction of people aged 50+ in Poland, especially for men. The same was found for those having children: they were more satisfied with life than the childless. The most important finding is that the lonely individuals are less satisfied with their lives. Interestingly, the positive relationship between satisfaction with life and having children among people aged 50+ may be explained by loneliness. It means that childlessness is strongly related to loneliness and loneliness mediates the effect of childlessness on satisfaction with life.

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Asymptotic Distribution of the Bias Corrected Least Squares Estimators in Measurement Error Linear Regression Models Under Long Memory

Journal of Time Series Analysis ◽

10.1111/jtsa.12451 ◽

2019 ◽

Vol 40 (4) ◽

pp. 493-518

Author(s):

Hira L. Koul ◽

Donatas Surgailis

Keyword(s):

Measurement Error ◽

Linear Regression ◽

Least Squares ◽

Asymptotic Distribution ◽

Long Memory ◽

Regression Models ◽

Linear Regression Models ◽

Least Squares Estimators

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FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

Journal of Business Economics ◽

10.35760/eb.2018.v23i1.1812 ◽

2018 ◽

Vol 23 (1) ◽

pp. 60-71

Author(s):

Wigiyanti Masodah

Keyword(s):

Interest Rate ◽

Linear Regression ◽

Interest Rates ◽

Regression Models ◽

Linear Regression Models ◽

Negative Impacts ◽

Main Activity ◽

The Impact ◽

The Given ◽

Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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