Modern Model of Interconnection of Inflation and Unemployment in Latvia

Kochetkov, Yuri

doi:10.5755/j01.ee.23.2.1543

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…A number of experts consider inflation to be a complicated multisided process, which depends not only on economical but also on social and political reasons. Theory of inflation considers unanimity of three components thereof: excessiveness of currency circulation; depreciation of money; redistribution of income, property and downfall of net remuneration (Kochetkov, 2012).…”

Section: Macroeconomic Factors Influencing the Loan Portfolio Credit ...mentioning

confidence: 99%

Macroeconomic Determinants Of Loan Portfolio Credit Risk In Banks

Mileris

2013

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The credit risk is one of the main risks in commercial banks and the ability to manage it meaningly affects banks' stability. This risk arises due to the particular reasons related to the possibility to lose loans if the debtors are not able to meet their financial obligations. When making the decisions of financing the loan applicants, banks use the credit risk assessment models that allow estimating the probability of the potential borrowers to default on their loan commitments. The main goal of managing the credit risk in banks is to compound the loan portfolio of the acceptable risk level. According to Derelioglu and Gurgen (2011) the credit risk analysis aims to decrease future losses by estimating the potential risk and eliminating the new credit proposal if the risk is higher than a defined tolerance value. In this respect, it is essential to identify the main factors causing this risk in order to manage it. When assessing the credit risk of every company, banks usually analyze the financial data and some qualitative factors as the independent variables in the statistical credit risk assessment models. But in changing the credit policy in banks and pricing the credits, it is very important to predict the quality of loan portfolio in future. The problem can be summarized as finding the statistical methods that relates the proportion of doubtful and non-performing credits in the loan portfolio (dependent variable) with the set of explanatory variables (macroeconomic information of a country). The aim of this research is to find the macroeconomic determinants that significantly influence the changes of loan portfolio credit risk in banks and to develop the statistical model for prediction of the proportion of doubtful and non-performing loans. The scientific literature analysis results confirmed the influence of macroeconomic conditions on credit risk of debtors in banks and presented that the changes in quality of loan portfolio in banks depend on GDP, inflation, interest rates, money supply, industrial production index, current account balance and other. In empirical research 22 EU countries were grouped into 3 clusters according to their similarity in changes of the doubtful and non-performing loans percentage in banks. The set of 20 independent variables as factors determining the changes in amount of doubtful and non-performing loans was created. These variables were calculated from 9 macroeconomic indicators of 3 years. The model was developed to classify the countries into clusters applying the logistic regression, factor analysis and probit methods. The classification accuracy is 100 %. The predictions of doubtful and non-performing loans indexes are based on the analysis of the scores of extracted 5 factors as new independent variables. The multiple regression and polynomial regression methods were applied for the index predictions in clusters. The developed model in this research enables to predict the percentage of doubtful and non-performing loans in banks with the average 98,06% accuracy. The res...

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Section: Macroeconomic Factors Influencing the Loan Portfolio Credit ...mentioning

confidence: 99%

Macroeconomic Determinants Of Loan Portfolio Credit Risk In Banks

Mileris

2013

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“…ARDL models are among the popular and frequently used approaches in econometric studies, see, e.g., (Marques et al 2019;Menegaki 2019;Kochetkov 2012;Khan et al 2019;Obradovic et al 2017;Ridzuan et al 2017;Kurecic and Kokotovic 2017).…”

Section: Models and Methodsmentioning

confidence: 99%

“…Figure 7 indicates similarly low levels after 2010 directing the analyzed countries towards fulfilling the convergence criteria. Economic analysis of inflation in Latvia is presented in (Kochetkov 2012).…”

Section: % Risk Of Cross-border Conflictmentioning

confidence: 99%

Development of Military Spending Determinants in Baltic Countries—Empirical Analysis

Odehnal

Neubauer

Dyčka³

et al. 2020

Economies

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The article presents the use of the ARDL model to identify military expenditure determinants of the Baltic States (Lithuania, Latvia, and Estonia). Factors influencing military expenditure include the variables characterizing the economic environment of the analyzed countries (GDP per Capita, Government Deficit/Surplus, General Government Gross Debt, Inflation), and the security environment measured by Risk of Foreign Pressures, Risk of Cross-border Conflict, and Democratic Accountability. General conclusions about the analysis of relationships between the military expenditure level and selected economics and security determinants were confirmed in the cases of Government Deficit/Surplus, GDP per Capita and Inflation. The results, therefore, indicate that the military expenditure of Estonia and Lithuania depended on the state budget deficit where military expenditure tended to go down in relation to an increasing deficit within the assessed period. As far as Estonia is concerned, the findings about relationship between the economic position and military expenditure was validated as an increasing economic performance tended to increase military expenditure.

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“…Focusing on Latvia, Kochetkov (2012) examined relationship between inflation rate and unemployment rate by estimating the natural rate of unemployment in the country. He concluded that there was a negative association between unemployment and inflation between 1999 and 2008.…”

Section: Literature Reviewmentioning

confidence: 99%

A Scientific Inquiry on the Estimation of the Phillips Curve in the Baltic Region

Furuoka

2016

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Countries in the European Union (EU) have experienced a number of economic problems in the aftermath of the Eurozone crisis, including high unemployment and inflation. However, there is still a lack of systematic analyses of these issues, especially in the context of the new EU member countries. This study aims to address this research gap. It examines an important topic in applied macroeconomics research-the Phillips curve-and chooses three Baltic countries, namely, Estonia, Latvia and Lithuania, as case studies. In other words, the main objective of this paper is to revisit the debate on the inflation-unemployment trade-off by estimating the new Keynesian Phillips curve (NKPC) in the Baltic region over the period of 1995-2013. The analysis was based on the generalized method of moments (GMM) procedure suggested by Gali and Gertler (1999). The novelty of the present study is that it applied the Gali-Gertler method to examine the trade-off relationship between inflation and unemployment in the Baltic countries. This study yielded some important findings. Firstly, it was found that inflation dynamics in the Baltic countries was largely determined by forward-looking behaviour, which means that firms located in the region had forward-looking price setting tendency and they paid due attention to the expected level of inflation. Secondly, the inflation dynamics did not seem to be determined by backward tendency and the firms in the region did not have backward price setting tendency. Thirdly, no significant trade-off relationship was detected between inflation rate and marginal cost. In other words, there was no significant negative association between the inflation rate and marginal cost in the Baltic countries.

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Modern Model of Interconnection of Inflation and Unemployment in Latvia

Cited by 7 publications

References 14 publications

Macroeconomic Determinants Of Loan Portfolio Credit Risk In Banks

Macroeconomic Determinants Of Loan Portfolio Credit Risk In Banks

Development of Military Spending Determinants in Baltic Countries—Empirical Analysis

A Scientific Inquiry on the Estimation of the Phillips Curve in the Baltic Region

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