Inflation targeting and financial stability: Does the quality of institutions matter?

Fazio, Dimas M.; Silva, Thiago Christiano; Tabak, Benjamin Miranda; Cajueiro, Daniel O.

doi:10.1016/j.econmod.2017.09.011

Cited by 54 publications

(22 citation statements)

References 40 publications

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“…Following Horváth and Vaško (2016), we include the following country-specific controls in the model: gross domestic product per capita (GDPPC), the growth rate of GDP (GDPG), inflation measured in percentage change of the consumer price index (INFL), the real interest rate change in percentage (REALINT), domestic credit to GDP in percentage (CREDIT), the change of the nominal exchange rate against the U.S. Dollar in percentage (EXCH), stock market capitalization to GDP in percentage (MARKCAP) and financial openness (the sum of foreign assets and liabilities divided by GDP; FINOPEN). Furthermore, B it is a set of bankspecific controls as proposed by Fazio et al (2018), namely the ratio of non-interest income to total income in percentage (NONINT) as a proxy for non-traditional activities of banks, banks' overhead costs to assets ratio (COST) and banking concentration (measured as the total assets of the three largest banks in percentage; CONCEN). Finally, ε it represents the error term.…”

Section: Methods and Datamentioning

confidence: 99%

Does institutional quality condition the impact of financial stability transparency on financial stability?

van Duuren,

de Haan,

van Kerkhoff

2019

Applied Economics Letters

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Section: Methods and Datamentioning

confidence: 99%

Does institutional quality condition the impact of financial stability transparency on financial stability?

van Duuren,

de Haan,

van Kerkhoff

2019

Applied Economics Letters

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“…Our models include a series of controls. The literature shows the conditioning effect of different measures of democracy or rule of law on CBI (Acemoglu et al, 2008;Bodea and Hicks, 2015a;Fazio et al, 2018). Thus, we include democracy both as a control and interacting with CBI.…”

Section: Datamentioning

confidence: 99%

More effective than we thought: Central bank independence and inflation in developing countries

Garriga

Rodriguez

2020

Economic Modelling

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This study examines the effect of legal central bank independence on inflation in developing countries. In spite of the policy consensus suggesting that central bank independence is an effective tool to control inflation, the evidence is still limited, particularly for developing countries. Using a novel dataset, we analyze the effect of central bank independence on inflation for a sample of 118 developing countries between 1980 and 2013. We find that higher central bank independence is associated with lower inflation rates. This effect on inflation is stronger the more democratic a country is, but it is also present in non-democratic countries. Our results are robust to different specifications and methodologies. Furthermore, we find that all dimensions included in the measurement of central bank independence (objectives, personnel, policy, and financial independence) contribute to curb inflation. Our results shed light on which types of reforms may be more effective at fighting inflation in developing countries.

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“…Machine learning and data mining [1][2][3][4][5][6][7][8][9], which is the process of learning in order to look for patterns in observations or data and make better decisions in the future based on the training samples, is widely used in various fields such as cybernetics [10][11][12][13][14], engineering [15][16][17][18], bioinformatics [19], medical informatics [20], economics [21][22][23][24][25][26][27], etc. Especially in economics, there are many issues for optimizing profits in the business such as customer lifetime value modeling (CLVM), churn customer modeling (CCM), dynamic pricing, customer segmentation, recommendation systems, etc.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction

et al. 2019

Complexity

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The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. Due to the class imbalance problem occurring in the bankruptcy datasets, several special techniques would be used to improve the prediction performance. Oversampling technique and cost-sensitive learning framework are two common methods for dealing with class imbalance problem. Using oversampling techniques and cost-sensitive learning framework independently also improves predictability. However, for datasets with very small balancing ratios, combining two above techniques will produce the better results. Therefore, this study develops a hybrid approach using oversampling technique and cost-sensitive learning, namely, HAOC for bankruptcy prediction on the Korean Bankruptcy dataset. The first module of HAOC is oversampling module with an optimal balancing ratio found in the first experiment that will give the best overall performance for the validation set. Then, the second module uses the cost-sensitive learning model, namely, CBoost algorithm to bankruptcy prediction. The experimental results show that HAOC will give the best performance value for bankruptcy prediction compared with the existing approaches.

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Inflation targeting and financial stability: Does the quality of institutions matter?

Cited by 54 publications

References 40 publications

Does institutional quality condition the impact of financial stability transparency on financial stability?

Does institutional quality condition the impact of financial stability transparency on financial stability?

More effective than we thought: Central bank independence and inflation in developing countries

A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction

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