Academic literature on the topic 'JEL Classification: C52'

El canal del crédito bancario, que amplifica los efectos del canal tradicional de la política monetaria, hace énfasis en la estructura y las fricciones del mercado financiero como determinantes del gasto agregado. Este artículo analiza y verifica la existencia del canal del crédito bancario en Colombia estimando un modelo propuesto por Michael Gibson en 19971997, quien utiliza regresiones de umbral para determinar el impacto de la política monetaria sobre la demanda agregada. Los resultados obtenidos no permiten descartar la existencia de este mecanismo de transmisión en Colombia durante el periodo analizado, aunque éste parece operar solo a través de la política monetaria contraccionista. Palabras clave: política monetaria, mecanismos de transmisión, canal de crédito bancario, modelos de umbral. Clasificación JEL: C12, C52, E44, E52, G11. Abstract: The Bank Credit Channel, which amplifies the effects of the traditional channel of monetary policy, emphasizes on the structure and frictions of financial markets as determinants of aggregate spending. This paper aims at analyze and verify the existence of the bank credit channel in Colombia estimating a model proposed by Gibson (1997) which uses threshold regressions as a way to determine the impact of monetary policy on aggregate demand. Results do not allow dismissing the existence of this transmission mechanism in Colombia during the analyzed period, although it seems to operate only through contractionary monetary policy. Keywords: monetary policy, transmission mechanisms, bank lending channel, threshold regressions. JEL classification: C12, C52, E44, E52, G11. Résumé: Le canal du crédit bancaire amplifie les effets du canal traditionnel de la politique monétaire et met l.accent sur la structure et sur les frictions du marché financier, lesquels constituent les éléments qui déterminent de la dépense agrégée. L.objectif de cet article est d.analyser et de vérifier l.existence du canal du crédit bancaire en Colombie en estimant le modèle proposé par Michael Gibson en 1997, lequel utilise des régressions à seuil pour déterminer l.impact de la politique monétaire sur la demande agrégée. Les résultats obtenus ne permettent pas d.écarter l.existence d.un mécanisme de transmission pendant la période analysée, malgré le fait qu.il ne paraisse agir qu.à travers une politique monétaire restrictive. Mots clef: politique monétaire, mécanismes de transmission, canal du crédit bancaire, modèles à seuil. Classification JEL: C12, C52, E44, E52, G11.

Cost benefit analysis (CBA) has long been used as a useful tool to appraise and evaluate the value of a range of investment projects to a society. Certain aspects of this method such as the appropriate discount rate is an important concern, because the choice of discount rates deeply affect the valuations of future income streams. Other aspects concerning financial flows and appropriate ‘shadow prices’ have also received considerable attention. However, when a megaproject with the character of a ‘universal intermediate’ is considered, its multiplier effects may be wide-ranging and permeate several economic and social layers and may be captured only in the aggregates. This study examines the costs and benefits of Vadinar refinery in Gujarat with a focus on this welfare dimension on society for the project. The framework explores a methodological breakthrough in CBA studies. In constituting the macroeconomic effects of expansion of the mega oil refinery, the wider economic impact (WEI) is estimated using the computable general equilibrium (CGE) model and incorporated into the CBA. This assimilation of CBA with macroeconomic externality obtained from the CGE model framework is perhaps only one of its kind in economic analysis of major infrastructure projects of any country. CBA when combined with CGE as an analytical tool can be gainfully employed to appraise or evaluate large scale projects like oil refineries. JEL Classification: B41, C51, C52, C53, C54, C55, D50, D58, D60, D61, D62, H23, H43, L71, O22, Q43

Existence of outliers and structural breaks having mutually unknown nature, in time series data, offer challenges to data analysts in model identification, estimation and validation. Detection of these outliers has been an important area of research in time series since long. To analyze the impact of these structural breaks and outliers on model identification, estimation and their inferential analysis, we use two data generating processes: MA(1) and ARMA(1,1). The performance of the test statistics for detecting additive outlier(AO), innovative outlier(IO), level shift(LS) and transient change(TC) is investigated using simulation strategy through power of a test, empirical level of significance, empirical critical values, misspecification frequencies and sampling distribution of estimators for the two models. The empirical critical values are found higher than the theoretical cut-off points, empirical power of the test statistics is not satisfactory for small sample size, large cut-off points and large model coefficient. We have explored confusion between LS, AO, TC and IO at different critical values(c) by varying sample size. We have also collected empirical evidence from time series data for Pakistan using 3-stage iterative procedure to detect multiple outliers and structural breaks. We find that neglecting shocks lead to wrong identification, biased estimation and excess kurtosis.
 JEL Classification Codes: C15, C18, C63, C32, C87, C51, C52, C82
 AMS Classification Codes: 62, 65, 91, DI, 62-08, 62J20, 00A72, 91-08, 91-10, 91-11 62P20, 91B82, 91B84, 62M07, 62M09, 62M10, 62M15, 62M20

This paper uses autoregressive jump intensity (ARJI) model to show that the oil price has both GARCH and conditional jump component. In fact, the distribution of oil prices is not normal, and oil price returns have conditional heteroskedasticity. Here the authors compare constant jump intensity with the dynamic jump intensity and evidences demonstrate that oil price returns have dynamic jump intensity. Therefore, there is strong evidence of time varying jump intensity Generalized Autoregressive Heteroscedasticity (GARCH) behavior in the oil price returns. The findings have several implications: first, it shows that oil price is highly sensitive to news, and it does settle around a trend in long-run. Second, the model separates variances of high volatilities from smooth volatilities. Third, the model rejects an optimal path for extracting oil and technology transmission. In fact, the lack of a long-term pattern can cause excessive oil extracting which can result in heavy climatic effects. Keywords: generalized autoregressive heteroscedasticity (GARCH), jumps, basket, oil price, Organization of Petroleum Exporting Countries (OPEC), Autoregre-ssive jump intensity (ARJI). JEL Classification: C32, C52, F31

Objective - This research aimed to explore the causal relationships among performance indicators in the Balanced Scorecard framework of the Indonesia Public Sector. Methodology– Quantitative and path analysis were used to collect and analyze primary data. The research included 24 Regional Public Sector Offices and 268 Local Public Sector Offices as part of the sample. Findings - The findings showed substantial causal relationships within the Public Sector Balanced Scorecard perspectives, with training significantly influencing employee performance in taxation dissemination and tax supervision activities. However, the impact of training on tax audit performance was considered insignificant. The empirical evidence suggested that tax audit was the only performance indicator positively associated with tax compliance. Determinant factors for tax revenue included tax compliance, dissemination, audit, and supervision. Novelty - This research contributes crucial insights by showing intricate causal relationships among performance indicators, indicating the significant impact of training on distinct facets of employee performance. Additionally, the study identifies tax audits as an essential factor positively influencing tax compliance. The analysis provides new perspectives on the applicability of the Balanced Scorecard in the public sector context, particularly in developing countries. Type of Paper: Empirical JEL Classification: M40, C52, H30, H20, M49 Keywords: Balanced Scorecard; Path Analysis; Public Sector; Taxation; Performance Indicators. Reference to this paper should be referred to as follows: Pudjono, A.N.S; Wibisono, D; Fatima, I; Rachmania, I.N. (2024). Examining the Causal Relationships of Balanced Scorecard Perspectives on Organizational Performance Improvement: A Case Study from the Indonesian Public Sector, Acc. Fin. Review, 8(4), 23 – 31. https://doi.org/10.35609/afr.2024.8.4(3)

Abstract. The paper deals with the analysis of the banking system of the UK. The UK banking sector is quite diverse, and at the same time oligopolistic. The UK exit from the EU is already having a negative impact on main banking indicators. Uncertainty in the banking sector and among the business community is, definitely, very high. This means that banks will have to work on potential and alternative outcomes, depending on their development. The impact of Brexit on banks and banking in the UK will be determined by both government-level arrangements and the response of individual firms to operating conditions changes. At the same time, the realities of the banking environment after the crisis of 2008—2009 mean that it is necessary not only to strengthen market discipline, but also to avoid excessive proliferation and diversification of commercial banks and concentration of the banking system. The post-crisis environment is characterized by a decrease in the number of commercial banks in the UK. However, the reduction in the number of banks does not prevent an increase in the value of the banking system’s assets and an improvement in their quality. Moreover, the paper explores the crisis of 2008—2009 impact on the functioning of the UK banking system (loans to monetary financial institutions, loans to non-monetary financial institutions, loans to non-monetary financial institutions, loans to the central government, deposits of monetary financial institutions, deposits of non-monetary financial institutions, central government deposits). For this purpose, the system of six vector models of autoregression has been constructed. The results of the simulations have shown that all variables have similar dynamics after the crisis shock, except for central government deposits. In the future, the leveling of the shock is observed for these variables starting from the fourth year. There is only an increase in central government deposits, starting from the second year after the shock The other variables do not respond to the crisis shock. Keywords: financial crisis, banks, bank assets, bank liabilities, problem loans, econometric modelling, VaR-models. JEL Classification C39, C51, C52, E44, E47, G01, G17, G21 Formulas: 6; fig.: 6; tabl.: 1; bibl.: 30.

ABSTRACT Accounting studies often encounter observations with extreme values that can influence coefficient estimates and inferences. Two widely used approaches to address influential observations in accounting studies are winsorization and truncation. While expedient, both depend on researcher-selected cutoffs, applied on a variable-by-variable basis, which, unfortunately, can alter legitimate data points. We compare the efficacy of winsorization, truncation, influence diagnostics (Cook's Distance), and robust regression at identifying influential observations. Replication of three published accounting studies shows that the choice impacts estimates and inferences. Simulation evidence shows that winsorization and truncation are ineffective at identifying influential observations. While influence diagnostics and robust regression both outperform winsorization and truncation, overall, robust regression outperforms the other methods. Since robust regression is a theoretically appealing and easily implementable approach based on a model's residuals, we recommend that future accounting studies consider using robust regression, or at least report sensitivity tests using robust regression. JEL Classifications: C12; C13; C18; C51; C52; M41. Data Availability: Data are available from the public sources cited in the text.

Abstract This paper investigates the dynamic linkages between the prices of crude oil, biofuels and agricultural commodities. The analysis uses monthly data for crude oil, corn, sugar, ethanol, biodiesel and the general food price index and covers the period 1960 –2013. In the context of the empirical analysis, we apply the ARDL approach to cointegration, in conjunction with Granger causality tests. The results reveal strong dependencies between the examined sectors in both the long and short run time horizon. JEL classifications: C22, C52, Q11, Q42, Q43 Keywords: ARDL Cointegration, Agricultural Commodities, Biofuels, Crude Oil

<strong>Abstract</strong> This paper examines the role of public debt in Nigeria's economic growth process from 1981 to 2021. Generally, economies resort to debt financing in economic growth to lessen the tax burden on the production chain. Applying autoregressive distributed lag models on annualized country data set, we showed that public debt positively affects economic growth in Nigeria. Total public debt in Nigeria could thus be positive and record nearly a 63% increase in economic growth in the long run. Most of the negative effects of public debt on economic growth reveal that debt servicing is still unsustainable in the Nigerian sub-region. Thus, we argued that deliberate policies be put in place to ensure that the accumulation of debt in Nigeria is consistent with the country's growth objectives. Furthermore, the government is encouraged to put in place fiscal reforms that would help in the better management of domestic debt and the acceleration of economic growth in years following this study.

This study examines the structure and formula for revenue allocation in Nigeria which has been fraught with challenges, proffers solution, and highlights its implications for sustainable national development. The work uses the methodology of Group Unit Root Test, auto regressive distributed lag (ARDL) Bounds Testing and Cointegrating Long Run tests for robust policy recommendations. Using the Gross Domestic Product as the dependent variable and revenue allocation to the three levels of government, and oil revenue as the independent variables, the results from the study show that revenue allocations and the other variables have significant relationship with economic growth in Nigeria. Based on our findings, the study recommends among others that the current revenue allocation formula should be reviewed to embrace autonomy in its entirety to achieve national goals and objectives. Various levels of government should be adequately funded to enable it carry out its expenditure responsibilities to accelerate grass root development. JEL classification: C22, C32, C58

This thesis is composed of three chapters that propose some novel approaches on tail risk for financial market and forecasting in finance and macroeconomics. The first part of this dissertation focuses on financial market correlations and introduces a simple measure of tail correlation, TailCoR, while the second contribution addresses the issue of identification of non- normal structural shocks in Vector Autoregression which is common on finance. The third part belongs to the vast literature on predictions of economic growth; the problem is tackled using a Bayesian Dynamic Factor model to predict Norwegian GDP.Chapter I: TailCoRThe first chapter introduces a simple measure of tail correlation, TailCoR, which disentangles linear and non linear correlation. The aim is to capture all features of financial market co- movement when extreme events (i.e. financial crises) occur. Indeed, tail correlations may arise because asset prices are either linearly correlated (i.e. the Pearson correlations are different from zero) or non-linearly correlated, meaning that asset prices are dependent at the tail of the distribution.Since it is based on quantiles, TailCoR has three main advantages: i) it is not based on asymptotic arguments, ii) it is very general as it applies with no specific distributional assumption, and iii) it is simple to use. We show that TailCoR also disentangles easily between linear and non-linear correlations. The measure has been successfully tested on simulated data. Several extensions, useful for practitioners, are presented like downside and upside tail correlations.In our empirical analysis, we apply this measure to eight major US banks for the period 2003-2012. For comparison purposes, we compute the upper and lower exceedance correlations and the parametric and non-parametric tail dependence coefficients. On the overall sample, results show that both the linear and non-linear contributions are relevant. The results suggest that co-movement increases during the financial crisis because of both the linear and non- linear correlations. Furthermore, the increase of TailCoR at the end of 2012 is mostly driven by the non-linearity, reflecting the risks of tail events and their spillovers associated with the European sovereign debt crisis. Chapter II: On the identification of non-normal shocks in structural VARThe second chapter deals with the structural interpretation of the VAR using the statistical properties of the innovation terms. In general, financial markets are characterized by non- normal shocks. Under non-Gaussianity, we introduce a methodology based on the reduction of tail dependency to identify the non-normal structural shocks.Borrowing from statistics, the methodology can be summarized in two main steps: i) decor- relate the estimated residuals and ii) the uncorrelated residuals are rotated in order to get a vector of independent shocks using a tail dependency matrix. We do not label the shocks a priori, but post-estimate on the basis of economic judgement.Furthermore, we show how our approach allows to identify all the shocks using a Monte Carlo study. In some cases, the method can turn out to be more significant when the amount of tail events are relevant. Therefore, the frequency of the series and the degree of non-normality are relevant to achieve accurate identification.Finally, we apply our method to two different VAR, all estimated on US data: i) a monthly trivariate model which studies the effects of oil market shocks, and finally ii) a VAR that focuses on the interaction between monetary policy and the stock market. In the first case, we validate the results obtained in the economic literature. In the second case, we cannot confirm the validity of an identification scheme based on combination of short and long run restrictions which is used in part of the empirical literature.Chapter III :Nowcasting NorwayThe third chapter consists in predictions of Norwegian Mainland GDP. Policy institutions have to decide to set their policies without knowledge of the current economic conditions. We estimate a Bayesian dynamic factor model (BDFM) on a panel of macroeconomic variables (all followed by market operators) from 1990 until 2011.First, the BDFM is an extension to the Bayesian framework of the dynamic factor model (DFM). The difference is that, compared with a DFM, there is more dynamics in the BDFM introduced in order to accommodate the dynamic heterogeneity of different variables. How- ever, in order to introduce more dynamics, the BDFM requires to estimate a large number of parameters, which can easily lead to volatile predictions due to estimation uncertainty. This is why the model is estimated with Bayesian methods, which, by shrinking the factor model toward a simple naive prior model, are able to limit estimation uncertainty.The second aspect is the use of a small dataset. A common feature of the literature on DFM is the use of large datasets. However, there is a literature that has shown how, for the purpose of forecasting, DFMs can be estimated on a small number of appropriately selected variables.Finally, through a pseudo real-time exercise, we show that the BDFM performs well both in terms of point forecast, and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg Survey, and that it outperforms the predictions published by the Norges Bank in its monetary policy report.<br>Doctorat en Sciences économiques et de gestion<br>info:eu-repo/semantics/nonPublished

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-07-18T14:26:58Z No. of bitstreams: 1 daniellealdepaulaestevesdosreis.pdf: 1239258 bytes, checksum: 75cc07cdf6eba15d62c43b78ac783fbc (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-22T15:03:54Z (GMT) No. of bitstreams: 1 daniellealdepaulaestevesdosreis.pdf: 1239258 bytes, checksum: 75cc07cdf6eba15d62c43b78ac783fbc (MD5)<br>Made available in DSpace on 2016-07-22T15:03:54Z (GMT). No. of bitstreams: 1 daniellealdepaulaestevesdosreis.pdf: 1239258 bytes, checksum: 75cc07cdf6eba15d62c43b78ac783fbc (MD5) Previous issue date: 2011-12-16<br>FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais<br>Medidas de volatilidade se constituem numa preocupação por parte de estudiosos e profissionais do mercado financeiro. Modelos da família ARCH/GARCH a partir dos retornos diários produzem um indicador de volatilidade, mas, não conferem ao pesquisador uma medida observável do grau de variabilidade dos retornos em torno de seu valor esperado. A recente disponibilidade de dados de frequência inferior a um dia de negociação permitiu a elaboração de indicadores de volatilidade observáveis por meio de uma medida conhecida como volatilidade realizada. A partir de então, é possível elaborar um indicador observável de volatilidade diária com base em dados de natureza intradiária, de modo a representar uma medida mais apropriada do grau de risco de um ativo ou carteira de ativos, e, a partir de então, estimar a volatilidade por meio de processo da família ARIMA. De posse dos dados de alta-frequência de um papel preferencial da Petrobrás S.A., o presente trabalho se propõe, portanto, em construir a medida de volatilidade realizada por meio da soma dos quadrados dos retornos obtidos em intervalos regulares (5, 15 e 30 minutos) durante cada dia de negociação do papel PETR4 durante o período de 02/01/2007 à 29/10/2010. Posteriormente à criação do indicador de volatilidade realizada que se supõe como mais apropriado para se mensurar o grau de risco, pretende-se comparar a qualidade do ajustamento e a capacidade preditiva de cada um dos métodos de modelagem da volatilidade. A comparação dos modelos baseados em dados diários e intradiários dar-se-á por meio do cômputo do erro quadrático médio (EQM) e dos testes de Diebold e Mariano e de Harvey para avaliação da acurácia preditiva dos modelos. Os resultados mostraram que, em geral, os modelos da família ARIMA são mais apropriados para a avaliação do grau de ajustamento, e produz previsões mais satisfatórias que os modelos da família ARCH/GARCH.<br>Volatility measures constitute a concern among scholars and professionals of the financial market. Models of the ARCH/GARCH class from the daily returns produce an indicator of volatility, but do not give the researcher an observable measure of the degree of variability of returns around their expected value. The recent availability of data at frequencies below a trading day allowed the development of indicators of volatility observable through a measurement known as realized volatility. Since then, they can build an observable indicator of daily volatility based on intraday data, so as to represent a more appropriate measure of the riskiness of an asset, and from then estimate volatility through a process of ARIMA family. Provided with the data of a high frequency preferential role of Petrobrás S. A., the present paper therefore proposes to construct a measure of realized volatility by the sum of the squares of the returns obtained at regular intervals (5, 15 and 30 minutes ) during each trading day for the paper PETR4 during 02/01/2007 to 29/10/2010. After the creation of the realized volatility indicator that is supposed to be more appropriate to measure the degree of risk, the intent is to compare the goodness of fit and predictive ability of each of the methods of volatility’s models. The comparison of models based on daily data and intraday give will be through the calculation of the mean square error (MSE) and tests of Diebold and Mariano and Harvey to evaluate the predictive accuracy of models. The results in general showed that the models of the ARIMA class are more suitable for assessing the degree of adjustment and produces predictions more satisfactory than the models of the ARCH/GARCH class.

III Title: Determinants of Residential Development Prices in Prague Author: David Mazáček Department: Institute of economic studies Diploma Theises Supervisor: PhDr. Pavel Streblov, MSc. Supervisor's e-mail address: streblov@pentainvestmens.cz Abstract: This thesis analyzes the progression and current situation on the Prague new residential development market. The beggining of the thesis is focused on performance of new residential development market in Prague and its comparison with other cities in European union including different housing standards and its economical severity. Performance analysis and current situation on the market indicate the presence of real estate bubble in past years on Prague market. This bubble has even deepened the problems on the market caused by the European economic crisis, however currently there is slow upsarge on the residential real estate market in Prague. Remaining two chapters are focused on relations between performance of residential development price per sqm in Prague and the performance of macroeconomic determinants in the range of income, financing or subsitutionary housing solution. This relation is explored through the econometric model, that explains the average aggregate price per sqm of new residential development in Prague through its macroeconomic...

Part II will be the application of principles and tools in econometrics. The problem with using econometric tools is that we have learned the principles but have yet to be able to apply them in practice for further research that needs to be studied or solved. Therefore, in this section, the author brings together peer-reviewed research to apply to the actual situation in terms of tourism, which is the case in Thailand, and finance and investment, which is linked to foreign markets. The results obtained can be used for practical purposes. They can provide policy recommendations to the government or relevant agencies.&amp;nbsp;We apply the econometrics tools to tourism and financial economics because the service sector and its operational efficiency are increasingly crucial in GDP creation and volatility generation in economic development. Efficient and growth-generating management of such vital elements of the service sector as tourism, financial instruments, and petroleum and other futures markets; must be balanced against concern for both stability (reductions in temporally predictable extreme fluctuations) and self-immunization against the vagaries of external natural, financial, petroleum-based. As service-sector markets are frequently co-integrated, it is also vital to determine whether an investment in one can offset downside risk in the others.&amp;nbsp;Keywords: multivariate time series; volatility transmission; comovement and spillover; multivariate GARCH&amp;nbsp;JEL Classification: C51; C52; C54; C55; C58.

In this Part I, there are 7 chapters which focus lies in comprehending the essence of econometrics, ranging from its fundamental principles to its more sophisticated facets. The overarching aim is to equip readers with the understanding needed to effectively apply econometric techniques for prediction and analysis. Through this comprehensive journey, readers will gain the necessary knowledge to harness econometrics as a powerful tool for making informed predictions and conducting thorough analyses.Keywords: econometrics; regression model; linear models; unit roots tests; volatility models; multivariate time series analysis; multivariate GARCH volatility; hedging. JEL Classification: C01; C53; C58.

Assessing the dynamics of risk premium measures and its relationship with macroeconomic fundamentals is important for both macroeconomic policymakers and market practitioners. This paper analyzes the main determinants of CDS in Latin-America at different tenures, focusing on their volatility. Using a component GARCH model, we decompose volatility between permanent and transitory components. We find that the permanent component of CDS volatility in all tenors was higher and more persistent in the global financial crisis than during the recent COVID-19 shock. JEL Classification: C22, C58, G01, G15. Keywords: Credit default swaps (CDS), CDS in Latin-American countries, sovereign risk, volatility, crisis, component GARCH models