Identifying multiple regimes in the model of credit to households

Serwa, Dobromił

doi:10.1016/j.iref.2012.10.011

Cited by 4 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Multiple and multinomial logistic regressions are applied to estimate the relationships between these variables. Serwa (2013) considers the specifi c features of the market in terms of lending to households. The researcher employs the multi-regime regression model related to different economic states of the credit market (i.e.…”

Section: Theoretical Reviewmentioning

confidence: 99%

The analysis of export trade between Ukraine and Visegrad countries

Nagyová¹,

Horáková²,

Moroz³

et al. 2018

E+M

View full text Add to dashboard Cite

The development of export from Ukraine to V4 countries is investigated using the software GRETL and log-log linear model. It is discovered that Ukraine's export operations are impacted by export to other trade partners and GDP per capita of the country. The strongest positive link to Ukraine export from V4 group is represented by Poland. It is identifi ed that, when the GDP per capita of the concerned country goes up, the export to that country declines. It is also revealed that there is a substantial difference with respect to export of agricultural commodities from Ukraine to the above-mentioned countries. To a signifi cant extent, Ukraine's export is oriented towards plant products.Ukraine should elaborate a well-defi ned trade strategy and extend its current export activities with V4 countries. It should be more deeply integrated into the EU's market for using more effi ciently possibilities, which exist in the frame of the signed Ukraine-European Union Association Agreement.

show abstract

Section: Theoretical Reviewmentioning

confidence: 99%

The analysis of export trade between Ukraine and Visegrad countries

Nagyová¹,

Horáková²,

Moroz³

et al. 2018

E+M

View full text Add to dashboard Cite

show abstract

“…(2013), Camarero vd. (2013 ve Serwa (2013) 2) ***, %1'de anlamlılığı, **, %5' te anlamlılığı göstermektedir.…”

Section: Anali̇z Yöntemi̇ Ve Ekonometri̇k Modelunclassified

Uluslararası Turizm Talebini Etkileyen Ekonomik Faktörler: Türkiye Üzerine Bir Uygulama

Aydın¹,

Darici²,

Tasci³

2015

ERÜ İİBF DERGİSİ

View full text Add to dashboard Cite

ÖZBu çalışmanın amacı, 1996(1)-2013(4) döneminde Türkiye'ye yönelik uluslararası turizm talebini etkileyen ekonomik faktörleri araştırmaktır. Bu kapsamda, Türkiye'ye gelen turist sayısı itibariyle en yüksek paya sahip ilk beş ülke için bir panel veri analizi yapılmıştır. Literatürde turizm talebinin belirleyicileri içinde en çok yer verilen makroekonomik değişkenler milli gelir, fiyatlar, taşıma maliyetleri ve döviz kurlarıdır. Bu çalışmada da literatürde yer alan bu temel değişkenler esas alınmıştır. Değişkenlere ait ön-testler (yatay-kesit bağımlılığı, durağanlık ve uzun dönem ilişkinin olup olmadığı) yapılmış, kurulan ekonometrik model tahmin edilmiş ve Türkiye'ye yönelik turizm talebinin belirleyicileri tespit edilmeye çalışılmıştır. Turizm fiyatları ve seyahat maliyetleri ile turizm talebi arasında beklendiği gibi negatif ve güçlü, döviz kurları ile turizm talebi arasında ise pozitif ve güçlü ilişki bulunmuştur. Bu bulgular, literature ile uyumludur. Türkiye'ye turist gönderen ülkelerin milli geliri ile Türkiye'ye yönelik turizm talebi arasında ise negatif ve güçlü ilişki bulunmuştur. Literatür ile farklı olan bu durum, Türkiye turizm sektörü için önemli bir soruna işaret etmektedir.Anahtar Kelimeler: Uluslararası Turizm Talebi, Panel Veri, Türkiye Ekonomisi. ECONOMIC DETERMINANTS OF INTERNATIONAL TOURISM DEMAND: AN EMPIRICAL APPLICATION ON TURKEY ABSTRACTThe main purpose of the study is to investigate the main factors that determine the tourism demand to Turkey. For this aim, we employed panel data methods on turizm demand data of firstfive countries which have the highest share with respect to the number of tourists coming to Turkey. The main determinants of tourism demand cited in the literature cover macroeconomic variables, such as gdp, price level, transportation and other costs, exchange rate and etc. In this study, after having carried out the pre-estimation tests (cross-sectional dependence, stationarity and co-integration tests) on the panel data, the estimation results of the econometric model to determine the tourism demand to Turkey is discussed. We find, as expected from the literature, that transportation costs and inflation have negative effects on tourism demand, whereas exchange rate has a positive effect on tourism demand. However, per capita GDP of tourist sending country has a negative and strong effect on tourism demand. This is in contrast to findings in the literature and can be considered as an indicator of a significant problem in the tourism sector in Turkey.

show abstract

“…Traditional banking supervision models of regulators relied on using confidential entity-level data and supervisory inspections to gauge the health of the banks and banking system, viz., the CAMEL ratios and other risk-based supervision measures (RBI 2023). Alternatively, using publicly available data, other models were also developed to track the performance of the banking system (Gersl and Seidler 2010;Serwa 2013). These models were developed to measure or indicate the fragility in the banking system (Kibritcioglu 2003;Allen and Gale 2004) using broad measures, viz., the bank deposit, credit, foreign currency assets, bank reserves to indicate the presence or absence of fragility in the banking system.…”

Section: Introductionmentioning

confidence: 99%

Assessing Machine Learning Techniques for Predicting Banking Crises in India

Puli,

Thota,

Subrahmanyam

2024

JRFM

View full text Add to dashboard Cite

The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies.

show abstract

Identifying multiple regimes in the model of credit to households

Cited by 4 publications

References 34 publications

The analysis of export trade between Ukraine and Visegrad countries

The analysis of export trade between Ukraine and Visegrad countries

Uluslararası Turizm Talebini Etkileyen Ekonomik Faktörler: Türkiye Üzerine Bir Uygulama

Assessing Machine Learning Techniques for Predicting Banking Crises in India

Contact Info

Product

Resources

About