Structural change plays an important role in developing any economy, so understanding it is critical to make policies that increase total factor productivity. The structural change that leads to an efficient resource allocation after trade reforms is desirable; the key factor that can affect the relation of "structural change and trade liberalization" with productivity is the quality of institutions. In this study, we first use the principal component method to propose a multidimensional index for structural change and then apply the ARDL econometrics model to evaluate the effect of trade liberalization and structural changes on total factor productivity in Iran during 19912018. The results show that structural changes increase the total factor productivity, and trade liberalization has a positive and significant effect on total factor productivity in the short term. Our results also indicate that there is no long-run relationship in this period.
{"title":"Investigating the Effect of Structural Changes and Trade Liberalization on Total Factor Productivity in Iran (1991-2018)","authors":"Yasaman Hokmollahi, Ali Taiebnia, A. Souri","doi":"10.52547/jme.16.1.71","DOIUrl":"https://doi.org/10.52547/jme.16.1.71","url":null,"abstract":"Structural change plays an important role in developing any economy, so understanding it is critical to make policies that increase total factor productivity. The structural change that leads to an efficient resource allocation after trade reforms is desirable; the key factor that can affect the relation of \"structural change and trade liberalization\" with productivity is the quality of institutions. In this study, we first use the principal component method to propose a multidimensional index for structural change and then apply the ARDL econometrics model to evaluate the effect of trade liberalization and structural changes on total factor productivity in Iran during 19912018. The results show that structural changes increase the total factor productivity, and trade liberalization has a positive and significant effect on total factor productivity in the short term. Our results also indicate that there is no long-run relationship in this period.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126642315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowing and managing the concept of customer experience is the main factor in creating competitiveness for any organization. Moreover, without customer experience management, a business cannot specify appropriate strategies to maintain the current market and business sustainability. However, most of the existing studies have looked at this subject abstractly and have not provided a comprehensive model based on steps taken in the customer journey. This research aims to fill the gap by providing the body of knowledge with a comprehensive model for customer experience management, where the stepwise nature of the concept is maintained. Using a grounded theory (GT) strategy, 20 experts in the Iranian IT sector took part in this study. Data gathered using an interview protocol that was made based on reviewing the existing literature. Both reliability (Inter-coder rating) and validity (face and content validity) measures for the data gathering tool were obtained. Three coding approaches of grounded theory (open, axial, and selective coding) were applied to analyze the data. This study introduced a stepwise model of customer management experience through the customer journey steps. The model also contains the prerequisites conditions to realize the customer experience in the IT sector and reveals the contextual factors affecting the process and finally the consequences of applying the model.
{"title":"A Stepwise Model of Customer Experience Management for Iranian ICT Sector","authors":"Fatemeh Saeedi, A. Danaei, S. M. Zargar","doi":"10.52547/jme.16.1.115","DOIUrl":"https://doi.org/10.52547/jme.16.1.115","url":null,"abstract":"Knowing and managing the concept of customer experience is the main factor in creating competitiveness for any organization. Moreover, without customer experience management, a business cannot specify appropriate strategies to maintain the current market and business sustainability. However, most of the existing studies have looked at this subject abstractly and have not provided a comprehensive model based on steps taken in the customer journey. This research aims to fill the gap by providing the body of knowledge with a comprehensive model for customer experience management, where the stepwise nature of the concept is maintained. Using a grounded theory (GT) strategy, 20 experts in the Iranian IT sector took part in this study. Data gathered using an interview protocol that was made based on reviewing the existing literature. Both reliability (Inter-coder rating) and validity (face and content validity) measures for the data gathering tool were obtained. Three coding approaches of grounded theory (open, axial, and selective coding) were applied to analyze the data. This study introduced a stepwise model of customer management experience through the customer journey steps. The model also contains the prerequisites conditions to realize the customer experience in the IT sector and reveals the contextual factors affecting the process and finally the consequences of applying the model.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133308822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and copulaGARCH with different copula functions to estimate volatilities and conditional correlations between Iran gold futures contract return and Tehran stock exchange main index return. The empirical results reveal that the dynamic conditional correlations switch between positive and near-zero values over the period under study. These correlations are high and positive during the major national currency devaluation and are low near to zero during other times. Out-of-sample one-step-ahead forecasts based on rolling window analysis show that DCC and ADCC multivariate GARCH models outperform other models for variance reduction, while a more interesting finding is that the copula-GARCH model outperforms other models for downside risks reduction.
{"title":"Dynamic Cross Hedging Effectiveness between Gold and Stock Market Based on Downside Risk Measures: Evidence from Iran Emerging Capital Market","authors":"R. Tehrani, Vahid Veisizadeh","doi":"10.52547/jme.16.1.43","DOIUrl":"https://doi.org/10.52547/jme.16.1.43","url":null,"abstract":"This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and copulaGARCH with different copula functions to estimate volatilities and conditional correlations between Iran gold futures contract return and Tehran stock exchange main index return. The empirical results reveal that the dynamic conditional correlations switch between positive and near-zero values over the period under study. These correlations are high and positive during the major national currency devaluation and are low near to zero during other times. Out-of-sample one-step-ahead forecasts based on rolling window analysis show that DCC and ADCC multivariate GARCH models outperform other models for variance reduction, while a more interesting finding is that the copula-GARCH model outperforms other models for downside risks reduction.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123078482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Azam Parsaee Tabar, N. Abdolvand, S. Rajaee Harandi
Money laundering is among the most common financial crimes that negatively affect countries' economies and hurt their social and political relations. With the increasing growth of e-banking and the increase in electronic financial transactions, the identification of money laundering methods and behaviors has become more complex; because money launderers, by accessing the Internet and using new technologies, find new ways to legalize their illegal income. Although many efforts have been made to identify suspected cases of money laundering and fight against this financial crime, little success has been achieved in this regard, especially in developing countries. Hence, this study tries to identify the risk factors involved in money laundering in banking transactions. To this end, multiple attribute decision-making methods, such as the Shannon entropy method, hierarchical analysis, and two-level fuzzy hierarchical analysis, have been used to assess and score the risk of various transactions in money laundering. The results indicated that the highest risk of money laundering was in the POS transactions.
{"title":"Identifying the Suspected Cases of Money Laundering in Banking Using Multiple Attribute Decision Making (MADM)","authors":"Azam Parsaee Tabar, N. Abdolvand, S. Rajaee Harandi","doi":"10.52547/jme.16.1.1","DOIUrl":"https://doi.org/10.52547/jme.16.1.1","url":null,"abstract":"Money laundering is among the most common financial crimes that negatively affect countries' economies and hurt their social and political relations. With the increasing growth of e-banking and the increase in electronic financial transactions, the identification of money laundering methods and behaviors has become more complex; because money launderers, by accessing the Internet and using new technologies, find new ways to legalize their illegal income. Although many efforts have been made to identify suspected cases of money laundering and fight against this financial crime, little success has been achieved in this regard, especially in developing countries. Hence, this study tries to identify the risk factors involved in money laundering in banking transactions. To this end, multiple attribute decision-making methods, such as the Shannon entropy method, hierarchical analysis, and two-level fuzzy hierarchical analysis, have been used to assess and score the risk of various transactions in money laundering. The results indicated that the highest risk of money laundering was in the POS transactions.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115250739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Stress Testing of Credit Risk in Iran’s Banking System","authors":"Mahboobeh Sanatkhani, F. Bazzazan","doi":"10.52547/jme.16.1.93","DOIUrl":"https://doi.org/10.52547/jme.16.1.93","url":null,"abstract":"","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alireza Ahadifar, Zahra Karimi Takanlo, Reza Ranjpour, J. Haghighat
This study investigates the effect of intra-organizational and macroeconomic factors on banking leverage in selected Iranian banks. For this purpose, after calculation of the Banking Leverage for each bank, by using Random-Coefficients Approach (Swamy model), the impact of explanatory variables during the period of 1999-2016 was examined separately by 10 selected Iranian public and private banks. Based on calculations, Melli, Saderat, Refah, and Tejarat Bank had the highest and Sanat-vaMadan, Eghtesad-Novin, and Sepah had the lowest level of banking leverage. Furthermore; the results of estimations show that "organizational" and "structuralvariables" of each bank have different effects on their banking leverage. For example, "credit risk" has a positive and significant effect on bank leverage in "Tejarat", "Saderat", "Refah" and "Sanat-va-Madan" banks. The effect of "liquidity risk" is the same as "credit risk". In general, due to banks' dissimilar structures, organizational and structural variables hold a varying impact on their banking leverage.
{"title":"Investigation of Factors Affecting Banking Leverage in Selected Iranian Banks (Random-Coefficients Approach)","authors":"Alireza Ahadifar, Zahra Karimi Takanlo, Reza Ranjpour, J. Haghighat","doi":"10.52547/jme.16.1.21","DOIUrl":"https://doi.org/10.52547/jme.16.1.21","url":null,"abstract":"This study investigates the effect of intra-organizational and macroeconomic factors on banking leverage in selected Iranian banks. For this purpose, after calculation of the Banking Leverage for each bank, by using Random-Coefficients Approach (Swamy model), the impact of explanatory variables during the period of 1999-2016 was examined separately by 10 selected Iranian public and private banks. Based on calculations, Melli, Saderat, Refah, and Tejarat Bank had the highest and Sanat-vaMadan, Eghtesad-Novin, and Sepah had the lowest level of banking leverage. Furthermore; the results of estimations show that \"organizational\" and \"structuralvariables\" of each bank have different effects on their banking leverage. For example, \"credit risk\" has a positive and significant effect on bank leverage in \"Tejarat\", \"Saderat\", \"Refah\" and \"Sanat-va-Madan\" banks. The effect of \"liquidity risk\" is the same as \"credit risk\". In general, due to banks' dissimilar structures, organizational and structural variables hold a varying impact on their banking leverage.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121296441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Sierpinski triangle is a fractal that is commonly used due to some of its characteristics and features. The Forex financial market is among the places wherein this triangle's characteristics are effective in forecasting the prices and their direction changes for the selection of the proper trading strategy and risk reduction. This study presents a novel approach to the Sierpinski triangle and introduces an innovative model based on it to forecast the direction changes in currency pairs, particularly EUR/USD. The model proposed in this study is dependent on the number of data selected for forecasting. The number of data is, in fact, the area of the initial triangle and the forecasted value of the self-similar triangles formed in each stage. For the performance assessment of the proposed method within one year (03/01/2019 to 28/02/2020), daily EUR/USD closed price data was classified into three categories, namely the training (70%), testing (20%), and validation (10%). Three approaches were proposed that led to forecasting the mean direction accuracy and the best result of over 60 percent in the third approach and over 50 percent in the first and second approaches. Results reflect the satisfactory improvements in the third approach compared to the econometrics, time-series, and machine learning methods. Moreover, the optimal number of data for the model is selected such that the difference between the accuracy of the direction forecasting in the training category and testing category is above 0.6 and below 0.05.
{"title":"Proposing an Innovative Model Based on the Sierpinski Triangle for Forecasting EUR/USD Direction Changes","authors":"Fatemeh Rahimi, Seyed Alireza Mousavian Anaraki","doi":"10.52547/jme.15.4.423","DOIUrl":"https://doi.org/10.52547/jme.15.4.423","url":null,"abstract":"The Sierpinski triangle is a fractal that is commonly used due to some of its characteristics and features. The Forex financial market is among the places wherein this triangle's characteristics are effective in forecasting the prices and their direction changes for the selection of the proper trading strategy and risk reduction. This study presents a novel approach to the Sierpinski triangle and introduces an innovative model based on it to forecast the direction changes in currency pairs, particularly EUR/USD. The model proposed in this study is dependent on the number of data selected for forecasting. The number of data is, in fact, the area of the initial triangle and the forecasted value of the self-similar triangles formed in each stage. For the performance assessment of the proposed method within one year (03/01/2019 to 28/02/2020), daily EUR/USD closed price data was classified into three categories, namely the training (70%), testing (20%), and validation (10%). Three approaches were proposed that led to forecasting the mean direction accuracy and the best result of over 60 percent in the third approach and over 50 percent in the first and second approaches. Results reflect the satisfactory improvements in the third approach compared to the econometrics, time-series, and machine learning methods. Moreover, the optimal number of data for the model is selected such that the difference between the accuracy of the direction forecasting in the training category and testing category is above 0.6 and below 0.05.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122127672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this research, the relationship between accruals quality and bankruptcy of companies has been studied. According to Dechow et al.'s (1995) model, the quality of accruals was measured, and according to the Shirata model (1998), bankruptcy was examined. Operations were considered as the control variables. The research hypothesis was tested using a multivariate regression model and a combined data method. The study's statistical sample consists of 197 companies listed on the Tehran Stock Exchange from 2011 to 2019. The results showed a significant and negative relationship between the quality of accruals and bankruptcy of the companies. It means that in bankruptcy, the use of earnings management through optional accruals reduces the quality of accruals. The results indicate that size, return on assets, and audit quality all significantly impact the quality of accruals. Besides, the leverage, life, and operating cash flow have a significant and negative effect on accruals' quality. However, the ratio of market value to book value does not significantly affect the quality of accruals.
本研究主要研究应计质量与公司破产之间的关系。根据Dechow et al.(1995)的模型，衡量应计项目的质量，根据Shirata模型(1998)，检查破产。操作被认为是控制变量。采用多元回归模型和组合数据法对研究假设进行检验。该研究的统计样本包括2011年至2019年在德黑兰证券交易所上市的197家公司。结果显示，应计项目质量与企业破产呈显著负相关。这意味着在破产中，通过可选应计项目进行盈余管理降低了应计项目的质量。结果表明，规模、资产收益率和审计质量都显著影响应计项目的质量。此外，杠杆、寿命和经营性现金流对应计项目质量有显著的负向影响。然而，市场价值与账面价值的比率并不显著影响应计项目的质量。
{"title":"Accruals Quality and Bankruptcy in Shirata Model (Case Study: Tehran Stock Exchange)","authors":"Oveis Bagheri, Mona Ranjbaran Jalili","doi":"10.52547/jme.15.4.381","DOIUrl":"https://doi.org/10.52547/jme.15.4.381","url":null,"abstract":"In this research, the relationship between accruals quality and bankruptcy of companies has been studied. According to Dechow et al.'s (1995) model, the quality of accruals was measured, and according to the Shirata model (1998), bankruptcy was examined. Operations were considered as the control variables. The research hypothesis was tested using a multivariate regression model and a combined data method. The study's statistical sample consists of 197 companies listed on the Tehran Stock Exchange from 2011 to 2019. The results showed a significant and negative relationship between the quality of accruals and bankruptcy of the companies. It means that in bankruptcy, the use of earnings management through optional accruals reduces the quality of accruals. The results indicate that size, return on assets, and audit quality all significantly impact the quality of accruals. Besides, the leverage, life, and operating cash flow have a significant and negative effect on accruals' quality. However, the ratio of market value to book value does not significantly affect the quality of accruals.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114056889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elaheh Esfandi, M. Mousavi, Rassam Moshrefi, Babak Farhang-Moghaddam
We seek to determine the optimal amount of the insurer’s investment in all types of assets for a small and closed economy. The goal is to detect the implications and contributions the risk seeker and risk aversion insurer commonly make and the effectiveness in the investment decision. Also, finding the optimum portfolio for each is the main goal of the present study. To this end, we adopted the optimal asset-liability management (ALM) method to control the firm's risk of financial stability and growth by balancing the assets and liabilities of the firm. In the process, stochastic interest rates and inflation risks were taken into account according to the expected utility maximization framework. All assets were established and calculated by the Kalman Filter with the stochastic interest rate following the Hull-White model; an additional stochastic process models the inflation risk. To consider the stochastic process, we employed the geometric Brownian motion in the liability process to ensure a definite liability value. We chose Iran’s Social Security Organization as our sample insurer company since it has a portfolio of five types of assets and four types of liabilities, and operates in a small and closed economy. By Applying the ALM method with the stochastic control theory approach, we acquire the optimal investment strategies for insurers to minimize their risk. Our findings demonstrate the effects of model parameters, such as the degree of risk-taking on the insurer decision.
{"title":"Insurer Optimal Asset Allocation in a Small and Closed Economy: The Case of Iran’s Social Security Organization","authors":"Elaheh Esfandi, M. Mousavi, Rassam Moshrefi, Babak Farhang-Moghaddam","doi":"10.52547/jme.15.4.445","DOIUrl":"https://doi.org/10.52547/jme.15.4.445","url":null,"abstract":"We seek to determine the optimal amount of the insurer’s investment in all types of assets for a small and closed economy. The goal is to detect the implications and contributions the risk seeker and risk aversion insurer commonly make and the effectiveness in the investment decision. Also, finding the optimum portfolio for each is the main goal of the present study. To this end, we adopted the optimal asset-liability management (ALM) method to control the firm's risk of financial stability and growth by balancing the assets and liabilities of the firm. In the process, stochastic interest rates and inflation risks were taken into account according to the expected utility maximization framework. All assets were established and calculated by the Kalman Filter with the stochastic interest rate following the Hull-White model; an additional stochastic process models the inflation risk. To consider the stochastic process, we employed the geometric Brownian motion in the liability process to ensure a definite liability value. We chose Iran’s Social Security Organization as our sample insurer company since it has a portfolio of five types of assets and four types of liabilities, and operates in a small and closed economy. By Applying the ALM method with the stochastic control theory approach, we acquire the optimal investment strategies for insurers to minimize their risk. Our findings demonstrate the effects of model parameters, such as the degree of risk-taking on the insurer decision.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114697066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the Iranian economy, part of the government's fiscal policies and liabilities is always financed by banks. As government debt to banks increases, the private sector's access to loans and facilities is limited. It can cause undesirable macroeconomic outcomes. This study investigates the macroeconomic effects of government debt on banks in Iran over 1972–2016 by using an SVAR model. Results show that government debt to banks does not significantly affect the aggregate demand ratio to aggregate supply and GDP per labor. Still, it significantly increases the real exchange rate and decreases the nontradable goods' ratio to tradable goods prices. In the long-run, the real exchange rate, the ratio of non-tradable goods to tradable goods price, and the general price level changed by 34.46, 20.95, and 46.4 percent, respectively, which can be explained by the government debt to banks. Results indicate that the government policy manages the Iranian economy.
{"title":"Macroeconomic Effects of Government Debt to Banks in Iran","authors":"Soheil Roudari, Y. Salmani","doi":"10.52547/jme.15.4.403","DOIUrl":"https://doi.org/10.52547/jme.15.4.403","url":null,"abstract":"In the Iranian economy, part of the government's fiscal policies and liabilities is always financed by banks. As government debt to banks increases, the private sector's access to loans and facilities is limited. It can cause undesirable macroeconomic outcomes. This study investigates the macroeconomic effects of government debt on banks in Iran over 1972–2016 by using an SVAR model. Results show that government debt to banks does not significantly affect the aggregate demand ratio to aggregate supply and GDP per labor. Still, it significantly increases the real exchange rate and decreases the nontradable goods' ratio to tradable goods prices. In the long-run, the real exchange rate, the ratio of non-tradable goods to tradable goods price, and the general price level changed by 34.46, 20.95, and 46.4 percent, respectively, which can be explained by the government debt to banks. Results indicate that the government policy manages the Iranian economy.","PeriodicalId":151574,"journal":{"name":"Journal of Money and Economy","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127824701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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