INTRODUCTION
RELEVANCE AND BACKGROUND OF STUDY
Since the financial reforms in Vietnam during the early 1990s, the restructuring of state-owned commercial banks (SOCBs) and the establishment of joint-stock banks (JSBs) have significantly transformed the country's financial system By 2004, the monetization of the economy had increased dramatically, with the M2 to GDP ratio rising to over 70%, compared to just 25% in the mid-1990s In that same year, SOCBs accounted for 73% of total credit, highlighting their dominant role in the credit market The financial landscape became increasingly segmented, with JSBs and smaller banks primarily serving the private sector, while SOCBs provided loans to both private and public sectors equally (Camen, 2006).
Vietnam's entry into the World Trade Organization (WTO) has led to a significant increase in foreign direct investment and portfolio inflows, marking a notable shift towards globalization However, this rapid economic growth has also brought challenges, particularly an unfavorable balance of payments Since 2000, Vietnam's financial sector has experienced explosive growth, especially during the 2007-2008 period, with the credit market expanding by approximately 50% in January 2008 This surge contributed to rising inflation, which reached 14% at that time (Ishii, 2008) By March 2011, inflation accelerated to 13.89%, the highest rate in 25 months, while the trade gap widened to $1.15 billion, up from $1.11 billion in February (S&P Reporting, 2011).
PROBLEM STATEMENT
Economic models often simplify the complexities of the economy by assuming that changes influenced by financial conditions are primarily determined by a limited set of financial variables, such as short-term risk-free interest rates and long-term government bond rates (Hall, 2001).
As financial systems have evolved significantly in recent years, their impact on the economy has become increasingly complex and profound This complexity makes it challenging to identify the root causes of economic issues, as certain variables may remain obscured A prime example is the 2008 global financial crisis, which originated in the credit sector, particularly through the mortgage asset crisis in the U.S., as well as the capital flight from the Vietnamese securities market, driven by prior years' lenient monetary policies.
Historically, economists like Pintinkin, Gurley, and Shaw highlighted the crucial role of financial intermediaries and credit markets in the economy Modigliani and Papademos (1977) acknowledged that traditional monetary theory overlooked the impact of these intermediaries and bank credit Research by Gurley and Shaw (1956) indicated that financial intermediaries exert a stronger influence on credit supply compared to money supply Consequently, the credit channel plays a vital role in shaping the decisions of policymakers.
Understanding the role of the credit channel in the financial market is vital for policymakers Recognizing how monetary policy is transmitted through this channel is essential for improving current policies This knowledge ultimately supports the achievement of national economic objectives.
This study aims to identify the role of credit channel in Vietnam’s monetary transmission mechanism, specify 1996-2010 period Following the main objective, the thesis:
- To analyze whether past value of credit helps predict the money supply;
- To examine the impact of credit shocks on money supply, also other macro economies;
- To test whether credit shock plays important role in forecasting the error of money supply.
RESEARCH QUESTION
To obtain the above objectives, this thesis will attempt to answer the following questions:
What is the role of credit channel in the monetary policy transmission in Vietnam case over the period 1996-2010?
- Does the past value of credit help predict money supply?
- How does money supply reaction to credit shock?
- Whether credit shocks plays important role in forecasting money supply’s error? 1.5 METHODOLOGY
To carry out above objectives, this study uses quarterly data from 1996:Q1 to 2010:Q3 Econometric techniques and descriptive statistic will be employed as primary quantitative in this research.
Descriptive statistical analysis provides an overview of all variables used in this thesis, including the distribution, variation, and central tendency of both original and altered data, allowing for an initial assessment of data quality For the econometric analysis of time series data, vector autoregression (VAR) will be utilized to address key research questions To ensure the validity of t-tests and F-tests, a unit root test will be conducted to examine the stationarity of all variables Subsequently, optimal lag lengths for the VAR model will be determined using various criteria to achieve the best model fit The Granger causality test will assess whether past values of credit can effectively forecast money supply, while impulse response and variance decomposition techniques will address the remaining research questions The empirical results will ultimately clarify the role of the credit channel in monetary transmission within the context of Vietnam.
STRUCTURE OF THESIS
The study is organized as following:
Chapter 1 introduces the importance of thesis, relevance and back ground of study, the objectives and research questions And the methodology is presented as briefly in this part.
Chapter 2 demonstrates the literature review Firstly, credit channel theory is mentioned as a core of study Secondly, empirical studies about the role of credit channel in monetary policy transmission are presented In addition, the chapter gives overview the Vietnam’s monetary policy framework, in which focuses on the credit market.
Chapter 3 presents analytical framework, then develop the model which helps us answer key question Finally, data description as well as steps of economic techniques will be mentioned in this chapter.
Chapter 4 shows the empirical results and discussion Finally, results comparison is also presented in this part.
Chapter 5 give conclusion, suggests some practical policy implications, and discusses the limitations and direction for further studies.
LITERATURE REVIEW
CREDIT CHANNEL THEORY
Credit channel theory revolves around the concept of an external finance premium, often highlighted by the principal-agent problem between lenders and borrowers Bernanke and Gertler (1995) elaborated on this theory through two primary linkages: the bank lending channel and the balance-sheet channel This thesis will explore these two linkages that are pertinent to credit channel theory.
The bank lending channel: concentrates the variability of loan supply through deposit institutions caused by the effect of monetary policy actions.
Banks play a crucial role as primary information sources in the economy, effectively addressing asymmetric information issues and other challenges in credit markets This is particularly important for small and medium-sized enterprises, which heavily rely on bank credit As long as this function persists, the bank lending channel will continue to be a significant factor in the transmission of monetary policy.
When the government implements expansionary monetary policy, it boosts bank deposits and reserves, resulting in an increase in the availability of bank loans This surge in lending stimulates investment, ultimately leading to an increase in overall economic output.
Tightening monetary policy results in a reduction of bank reserves and customer deposits, which subsequently decreases the availability of bank loans This reduction in loan availability leads to a decrease in investment spending and a decline in overall output.
Another side of credit view, when we mention to the impact of monetary policy to enterprises, small firms suffer bad effects on expenditure than large firms (Mishkin,
Small companies often rely heavily on bank loans for funding, while larger companies have the advantage of accessing significant capital through stock and bond markets, reducing their dependence on traditional banking channels.
Balance sheet channels: focuses influence of monetary policy changing on borrower’s balance sheets and income statement.
Changes in a company's worth due to fluctuating monetary policy can exacerbate adverse selection and moral hazard issues when banks issue loans Borrowers may have insufficient collateral, increasing the likelihood of moral hazard as firms with lower equity are more inclined to pursue risky projects This tendency raises the risk of loan defaults, potentially leading to bank failures and a subsequent decline in lending and investment In response to decreasing net worth, banks often demand more collateral from borrowers, further intensifying adverse selection problems and constraining available funds for investment.
Here is several ways which monetary policy acts upon on firm’s balance sheet:
Expansionary monetary policy drives an increase in equity stakes, while simultaneously reducing issues related to adverse selection and moral hazard This leads to a rise in firm valuations and enhances access to capital for investments, ultimately boosting aggregate demand.
Lower interest rates resulting from expansionary monetary policy lead to improvements in a firm's balance sheet due to enhanced cash flow This, in turn, reduces the issues of adverse selection and moral hazard Consequently, the availability of capital for loans increases, stimulating investment spending and ultimately boosting aggregate output.
Monetary expansion leads to an unexpected rise in price levels, boosting company net worth while reducing issues related to adverse selection and moral hazard This process stimulates increased investment spending and enhances overall aggregate output.
Contractionary monetary policy leads to a decrease in equity prices and cash flow, resulting in lower net worth for businesses This decline exacerbates issues related to adverse selection and moral hazard, ultimately reducing investment and consumption financing.
MONETARY POLICY FRAMEWORK OF VIETNAM
The State Bank of Vietnam (SBV) serves as the central bank and government body of the Socialist Republic of Vietnam, as outlined in the "Law on the State Bank of Vietnam." The National Assembly and the government are tasked with making decisions and supervising monetary policy, while the government prepares plans that include annual inflation projections and determines liquidity levels for the economy Additionally, the government must report on the implementation of monetary policy to the National Assembly The SBV's role involves executing the monetary policy established by the government, managing state monetary and banking activities, and functioning as the currency issuing authority, the bank for credit institutions, and the bank for the government These activities aim to stabilize currency value, preserve banking operations, and align with the state budget and economic growth within the framework of the country's socialist orientation.
According to SBV Law, the State Bank of Vietnam (SBV) functions as a government entity, with the National Assembly significantly influencing monetary policy decisions This close involvement of the government and the National Assembly in monetary policy implementation highlights the limited independence of the SBV in its operational tools (Camen, 2006).
2.2.2MONETARY POLICY STRATEGY AND INSTRUMENTS
Vietnam's monetary policy strategy is derived from the five-year Social and Economic Development Plan, with the government responsible for creating an actionable implementation plan This plan not only establishes liquidity injection targets but also defines key metrics such as M2, credit, and deposit targets, which are essential components of the government's strategy (Camen, 2006).
The State Bank of Vietnam (SBV) plays a crucial role in the government by setting annual targets for total liquidity and credit within the economy, aligned with macroeconomic and monetary objectives Each year, the SBV prepares a report detailing the implementation of monetary policy and forecasts for the upcoming year, which is then submitted to the government for review and approval Following this, the government presents the report to the National Assembly for final approval, after consulting with the National Monetary Policy Advisory Board.
Regarding to monetary instruments, a number of indirect tools have been introduced include reserve requirement, refinancing, discount financing facilities, open market operation and foreign exchange interventions.
SBV has applied reserve requirement in various forms since 1990s This instrument proves its important role on money market regulating in past Currently, required
The National Monetary Policy Advisory Board (NMPAD) comprises key figures such as the Governor of the State Bank, the Minister of Finance, and various experts Reserves are categorized based on deposit maturity, the bank's sectoral focus, and the type of currency deposits—whether domestic or foreign Short-term deposits, typically under one year, attract higher rates compared to long-term deposits, and banks receive interest subsidies when providing credit to the agricultural sector or the People Credit’s Fund (Camen, 2006).
In 2008, the State Bank of Vietnam (SBV) utilized reserve requirements as a key strategy to combat inflation amid significant domestic and international economic fluctuations In February of that year, the SBV increased the reserve requirement ratio by 1 percentage point for both local and foreign currency deposits across most credit institutions However, in response to the need to avert an economic downturn, the SBV subsequently lowered the required reserve for VND deposits under 12 months twice in 2009, reducing it from 6% to 5% and then to 3% Notably, the Vietnam Bank for Agriculture and Development was given special consideration, with its rate decreasing from 3% to 2% and ultimately to 1%.
In 2010, the State Bank of Vietnam (SBV) maintained a low reserve requirement ratio, setting it at 3% for VND deposits under 12 months and 1% for those over 12 months Additionally, the SBV reduced the reserve ratio for foreign currency to enable credit institutions to enhance their foreign currency funding.
Since their introduction by the State Bank in July 2000, Open Market Operations (OMOs) have become a crucial monetary tool for managing liquidity in the market These operations have been effectively aligned with other monetary policy measures to enhance market stability In the first seven months of 2008, the State Bank of Vietnam (SBV) issued compulsory bills and raised base interest rates, notably increasing the annual rates for 182-day and 364-day bills to 7.5% and 7.75% respectively, to control inflation Additionally, in the first half of 2009, the SBV offered to purchase 14-day maturity securities to provide short-term capital, thereby supporting credit institutions in meeting their funding needs for economic stimulus initiatives.
In 2009, the State Bank of Vietnam (SBV) attempted to stimulate the economy, but this initiative failed due to low demand for funds and an excess of financial resources at the time To address this, the SBV conducted open market operations (OMOs) during the first nine months of 2010 by purchasing valuable papers However, by the end of the third quarter of 2010, rising inflation pressures led to increased interest rates for these financial instruments.
In addition to adjusting reserve requirements, the State Bank of Vietnam (SBV) employs refinancing and rediscount facilities as part of its discount policy While both rates are similar, the rediscount rate is determined by the SBV based on collateralized securities such as drafts, promissory notes, and bonds, which commercial banks use to secure funding from the SBV In contrast, the refinancing rate is linked to loans provided to commercial banks, which then use these loans as collateral for further borrowing from the SBV Consequently, the refinancing rate is typically higher than the rediscount rate Since 2010, the SBV has utilized the refinancing rate to enhance short-term lending and liquidity for credit institutions, primarily offering refinancing terms of 1 to 2 months to support economic liquidity Additionally, at the end of 2010, the SBV implemented refinancing measures to address the increased demand for deposit withdrawals by economic entities and individuals during the Lunar New Year.
Historically, Vietnam's financial system was primarily served by the State Bank of Vietnam (SBV) and two state-owned commercial banks (SOCBs) Following the Doi Moi reforms in 1988, the SBV was designated as the central bank, focusing on monetary policy and financial supervision The financial landscape evolved further with the introduction of joint-stock commercial banks and foreign banks in 1991 and 1992, respectively In 2000, the Development Assistance Fund was established to support financial policy objectives Notably, Vietnam's financial market has traditionally been linked to non-commercial lending, with a significant emphasis on the agriculture sector, as highlighted in a World Bank report from 2006.
According to the World Bank's 2006 report on Vietnam's capital market, the banking sector experienced rapid growth primarily through lending to the private sector, with State-Owned Commercial Banks (SOCBs) continuing to play a dominant role in credit provision Despite ongoing reforms, the banking sector remains financially vulnerable, necessitating improvements to strengthen its stability and enhance lending capacity.
Table 2.1: One decade and Vietnam’s credit
Source: Calculated from IMF-IFS and GSO data
In the banking sector credit, credit to the economy rose from VND 155 trillion in 2000
(35 percent GDP) to VND 2,690 trillion (136 percent GDP) in 2010 (as shown table
2.1) Only one decade, the supplied credit increased seventeen times Noticeable,
Since late 2007, Vietnam has experienced rapid credit growth, reaching 50 percent, primarily driven by significant capital inflows and the emergence of real estate price bubbles used as collateral for loans.
Vietnam’s credit growth 2011 and orientation in 2012
In 2022, Vietnam's banking system experienced a 10.9 percent increase in total outstanding loans, marking the lowest credit growth rate in a decade, significantly below the targeted range of 15-17 percent Additionally, deposit growth stood at 8.89 percent, while the money supply expanded by an estimated 9.27 percent, according to a monthly report from the State Bank of Vietnam (SBV).
Vietnam aims for a credit growth of 15-17% and a 14-16% increase in money supply this year, utilizing monetary policy to achieve these targets According to SBV Governor Nguyen Van Binh, lending growth may remain below 15% The State Bank of Vietnam (SBV) is prioritizing the development of agriculture, rural sectors, export production, auxiliary industries, and support for small and medium-sized enterprises Agribank, the country's leading lender, allocates 75-89% of its loans to agriculture and rural development Additionally, the SBV has set various credit growth targets for domestic banks, categorizing them into four groups with maximum loan growth rates of 17%, 15%, 8%, and 0%, respectively.
EMPIRICAL LITERATURE
Recent empirical studies have explored the impact of credit on monetary policy transmission, beginning with Bernanke and Blinder's influential 1988 paper, "Credit, Money and Aggregate Demand." They utilized the IS/LM model to analyze the effects of money demand shocks and introduced the CC (commodities and credit) curve, mirroring the IS/LM curve, to examine the credit channel's role via bank lending during economic shocks Their analysis of two sub-samples (1974:1-1979:3 and 1979:4-1985:4) revealed that while the variance of money-demand shocks was significantly smaller than that of credit-demand shocks in the first period, the importance of credit demand increased notably in the 1980s.
The role of the credit channel in monetary transmission has been a topic of interest since Ramey (1993) analyzed it in the context of Bernanke's earlier work Utilizing a monthly sample of American data from 1954 to 1991, Ramey employed a dynamic stochastic general equilibrium model with eight key variables, including industrial production and various monetary aggregates, to assess the relative importance of the money and credit channels His findings indicated that the money channel significantly outweighs the credit channel in the direct transmission of policy shocks during that period In contrast, Bernanke and Gertler (1995) highlighted the credit channel as a crucial component of the monetary transmission mechanism, detailing its bank lending and balance sheet linkages Subsequent studies adopted their framework, using vector autoregression methods to analyze responses to policy shocks and confirming that the credit channel can reveal substantial costs associated with capital effects in a neoclassical context.
In his 1999 investigation into Korea's financial crisis, Kim examined the significance of the credit channel in the transmission of monetary policy Utilizing monthly data from January 1993 to May 1998, he employed a combination of three methodologies, including a narrative approach and disaggregated bank analysis.
Bernanke and Blinder characterized the monetary transmission mechanism as a "black box," utilizing a disequilibrium model to examine the bank lending channel They employed standard vector autoregression for their econometric specification to assess the significance of loan supply Their findings offered compelling evidence of the critical role played by the credit channel in the aftermath of the financial crisis.
Numerous researchers have explored the same topic over the years, yielding diverse results due to the varied characteristics of different countries Warner and Georges (2001) introduced a novel approach to examining the credit view of monetary transmission through stock market returns, specifically analyzing abnormal returns of U.S manufacturing firms during two distinct periods: the recessionary phase of 1990-1991 and the expansionary phase of 1993-1994 Their findings indicated no consistent relationship between abnormal stock returns and credit constraints Similarly, Suzuki (2004) investigated the lending view in Australia from 1985:Q1 to 2000:Q2 and concluded that the lending channel was less influential due to specific behaviors of Australian banks Additionally, Lown and Morgan (2002) and Disyatat and Vongsinsirikul (2003) examined credit effects in the U.S and Thailand, respectively, with Lown and Morgan using bank commercial credit standards as a proxy for credit availability in their vector autoregression (VAR) model analysis.
This study classifies the loan market into two categories: classical and augmented markets Through market discrimination, the authors reveal the significant role of credit standards in the U.S economy Their empirical analysis demonstrates that the dynamics of commercial credit standards significantly influence both loans and economic output Utilizing a typical econometric methodology, specifically the VAR model, Disyatat and Vongsinsirikul present their findings.
3 The economists who supported the view: bank loans play important role in monetary policy transmission mechanism.
Lown and Morgan (2002) explored the enigmatic nature of credit effects within the Thai economy, focusing on three key variables: real output, the Consumer Price Index (CPI), and the 14-day repurchase rate from Q1 1993 to Q4 2001 Their research revealed that investment significantly responds to monetary shocks, highlighting the crucial role of banks as vital facilitators for effective monetary policy implementation.
Charoenseang and Manakit (2006) examined the financial environment in Thailand following the adoption of inflation targeting and found that the transmission of monetary policy was primarily driven by the credit channel rather than the interest rate channel during June.
Between 2000 and July 2006, the Thai financial market heavily relied on bank lending as a primary source of capital for economic activities This period underscored the significant role that commercial bank lending plays in supporting the Thai economy.
In the same year, Podpiera (2007) had employed commercial banks data to study the impact of monetary policy shocks on loan market in Czech case; meanwhile Kubo.A.
In 2007, research on Thailand's monetary transmission mechanism emphasized the significance of the credit channel Empirical studies, including Podpiera's adaptation of the Kashyap and Stein model using Czech banks' balance sheet data from 1996 to 2001, revealed that changes in monetary policy influenced loan growth rates, particularly during the 1999-2001 period Kubo's study employed a structural vector autoregression (SVAR) approach to analyze the impact of exogenous monetary policy shocks on domestic macroeconomic variables from May 2000 to December 2006, focusing on five key indicators: consumer price index, industrial production, producer price index, inter-bank overnight lending rate, and private credit aggregates The findings underscored the Bank of Thailand's (BOT) reliance on the credit channel as a crucial factor during this period and highlighted the negative effects of monetary policy shocks on import demand within the SVAR framework.
Balazs Egert (2009) explored the effectiveness of research on the monetary transmission mechanism in Central and Eastern Europe, revealing that a decrease in the inflation rate significantly influenced the exchange rate transmission over time The study identified the credit channel as a crucial component of monetary policy transmission, while the asset price channel lacked strength in the context of stagnant stock and bond markets Similarly, Fiorentini and Tamborini (2001) highlighted the critical role of credit supply in shaping Italy's policy decisions over the past decade.
This article examines the monetary policy transmission in India, highlighting the significance of the bank lending channel in influencing economic activities Abdul (2009) utilized a VAR model that incorporated key macroeconomic variables such as bank rate, repo rate, and reserve repo rate, emphasizing the crucial role of these factors in monetary policy transmission Additionally, the study suggested the inclusion of the Federal Reserve's rate when analyzing monetary shocks due to its substantial impact on emerging economies like India In a related study, Catão and Pagan (2010) employed an expectation-augmented SVAR model to investigate monetary transmission in Brazil and Chile, using data from the IMF, Brazilian Planning Ministry Research Institute, and the Central Bank of Chile Their findings underscored the importance of the bank-credit channel and demonstrated that typical credit shocks significantly affect output and inflation, particularly in Chile, where the banking system's penetration is notably higher.
Research on the role of credit in monetary policy transmission in Vietnam is limited In 2008, Hung and Pfau conducted a study that, while not directly addressing credit, analyzed Vietnam's monetary transmission mechanism using a vector autoregression (VAR) approach Their findings highlighted a weak connection between monetary policy and its various channels, revealing that the credit and exchange rate channels are more significant than the interest rate channel in the Vietnamese context.
This chapter delves into the credit channel's significance within the thesis, highlighting that credit theory is driven by the external finance premium It emphasizes how monetary policy shocks can impact the loan market through both the bank lending and balance-sheet channels In the context of Vietnam, the National Assembly oversees sovereign monetary policy, while the State Bank of Vietnam (SBV) implements these policies, utilizing key instruments such as reserve requirements, open market operations (OMOs), and discount rates The empirical literature largely supports the critical role of the credit channel across various countries and econometric techniques, although some studies present findings that contradict this notion.
CHAPTER 3: MODEL SPECIFICATION AND DATA
This section outlines the analytical framework and methodology utilized in my thesis It introduces the VAR model as the primary method for examining the impact of the credit channel on monetary policy transmission in Vietnam Additionally, a concise overview of the estimation steps employed in this research is provided.
ANALYTICAL FRAMEWORK
Rely on theory and empirical studies, an analytical framework is conducted below.
Vietnam's monetary policy is transmitted through four channels: interest rate, exchange rate, asset rate, and credit channels, which impact macroeconomic variables such as output and inflation However, due to the inexperience of Vietnam's stock market, the asset channel will not be analyzed in this study Additionally, strict capital controls in Vietnam mean that capital mobility is imperfect, leading to the exclusion of the exchange rate channel from this paper This thesis focuses on the interest rate and credit channels, emphasizing the critical role of the credit channel in the transmission of monetary policy in Vietnam To explore this, two market types are introduced: the classical market (without credit) and the augmented market (with credit) The VAR model will be applied to both markets using Granger causality, impulse response, and variance decomposition tests The results will highlight the differences between the two markets and ultimately clarify the role of the credit channel in the monetary policy transmission mechanism.
MODEL SPECIFICATION
To achieve the thesis objective, a Vector Autoregression (VAR) model will be constructed to investigate the significance of the credit channel in the transmission of monetary policy in Vietnam.
According to Stock and Watson (2001), there are three types of Vector Autoregressions (VARs): reduced form, recursive, and structural This thesis adopts the VAR approach, emphasizing the reduced form as the most suitable method to achieve its objectives.
A reduced form represents each variable as a linear function of its own previous values and the historical values of all other variables This approach allows for the derivation of a reduced form of a Vector Autoregression (VAR) model.
Yt is an m-dimensional vector of endogenous variables
(L) denotes vector polynomial of lag operator with optimal lag order;
t is assumed to be vector white noise residual.
In regression analysis, the error term (εt) represents the unexpected variations in the variables after accounting for their past values Each equation is individually estimated using ordinary least squares, and the optimal number of lagged values is determined through various methods.
This thesis utilizes quarterly data from 1996:Q1 to 2010:Q3, focusing on key macroeconomic variables such as M2, consumer price index, domestic credit, real industrial output, refinancing rate, and lending rate Due to specific data access limitations in Vietnam, this timeframe is necessary Table 3.1 provides detailed definitions and sources for these variables, which collectively represent a comprehensive overview of Vietnam's macroeconomy The selection of these variables is motivated by their relevance to monetary policy dynamics; for instance, Romer and Romer (1990) highlight that a decrease in reserves leads to reduced loan availability, often resulting from contractionary monetary policy M2 is included as it is a primary measure of money supply and policy shocks in the Vietnamese context, while the refinancing rate serves as a critical tool for the State Bank of Vietnam in managing monetary policy (Hung and Pfau, 2008).
The customer price index plays a crucial role in measuring inflation, which serves as a key predictor of economic output To accurately assess the supply of loans within the domestic economy, it is essential to incorporate domestic credit variables into the model Additionally, the lending rate is a vital element in the policy transmission mechanism While GDP is commonly used to gauge a country's economic growth, Vietnam only has available data since 2000; therefore, real industrial output is utilized as a proxy for GDP This approach was similarly employed in the VAR model by Hung and Pfau (2008) to analyze monetary transmission in Vietnam It is important to control for spurious regression among all variables, as Asteriou and Hall (2007) noted that most macroeconomic time series exhibit trends, indicating they are non-stationary.
According to the empirical study by Lown and Morgan (2002), which examined the role of the credit channel in the U.S., the loan market can be categorized into two scenarios: a classical market characterized by quantity and price, and an augmented market that incorporates credit This framework will be applied to the Vietnamese context, distinguishing between a classical market that includes key indicators such as the customer price index, money-quasi, industrial output, refinancing rate, and lending rate, and an augmented market that uses credit as a proxy for loan supply or domestic credit to the economy.
DATA SOURCES
M2denoted broad of money stock and are defined by formula:
In 2009, the International Financial Statistics (IFS) defined M2 as encompassing money supply (M1) along with savings and time deposits in the national currency, as well as demand deposits in foreign currency, excluding those held by the central government and other depository corporations.
The Consumer Price Index (CPI) is calculated based on data from the 37 largest provinces across eight economic regions in Vietnam The weighting for these indices is derived from the 2004 Vietnam Household Living Standard Survey, as noted by the IMF in 2009.
CREDIT denoted domestic credit It is the sum of credit to the nonfinancial public sector, credit to private sector and other account.
OUTPUT denoted real industrial output As already represented, Vietnam’s industrial output is used as proxy for GDP, due to data limited.
M2 Broad money stock IFS-IMF
CPI Customer price index IFS-IMF
CREDIT Domestic credit to economy IFS-IMF
OUTPUT Real industrial output Vietnam GSO
REFIN Refinancing rate IFS-IMF
LR Lending Rate IFS-IMF
REFIN symbolized refinancing rate That is the rate charged by the State Bank ofVietnam on its lending to facilities to all credit institution (IMF world and country noted, 2009).
The LR, or lending rate, represents the average interest rates at the end of a period for short-term working capital loans provided by four major state-owned commercial banks, as noted by the IMF in 2009.
Excepting for output that is extracted from Vietnam General Statistic Office, these variables are taken from the International Monetary Fund’s (IMF) InternationalFinancial Statistic (IFS).
STEPS OF ESTIMATION
Stock and Watson (2001) stated that due to the complicated dynamics in the VAR, below statistics are more informative than estimated VAR regression coefficients or R 2 statistics.
Stationarity is crucial in time series analysis, as non-stationary data can lead to misleading results in regression, known as "spurious" relationships According to Gujarati (2003), it is essential to test for stationarity in all variables prior to applying a VAR model to ensure meaningful interpretations of the data.
Several formal statistical tests address the unit-root problem, with the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests being the most favored These tests incorporate additional lagged terms of the dependent variable to effectively eliminate autocorrelation.
The Granger causality test serves as the second step in the regression of the VAR model and is widely utilized in economic policy analysis This test is an effective method for assessing the significance of coefficients, allowing for accurate predictions of Xt based on its past values.
Yt variable rather than not using such past value, or causality represents the ability of one variable to predict another variable.
Impulse response functions illustrate how current and future values of variables react to a one-unit increase in a specific VAR error, while keeping other errors constant This analysis includes ± 1 standard error bands, providing an approximate 66 percent confidence interval for each impulse response Additionally, the forecast error decomposition tool reveals the percentage of variance in forecasting errors attributed to specific shocks over time (Stock and Watson, 2001).
FINDING AND DISCUSSION
DESCRIPTIVE STATISTIC
This part reports the descriptive statistic of all variables of original data, also changed.
It summarizes the mean, median, max, min, standard deviation and count of each variable.
Table 4.1: Description statistic of variables
Source: Calculated from IMF-IFS and GSO data
The standard deviations of CPI, CREDIT, M2, and OUTPUT are significantly high, alongside a large spread between their maximum and minimum values, indicating considerable volatility during this period (see Table 4.1) Consequently, the estimation results derived from their original values may be unreliable To address this issue, transforming these variables into logarithmic form is recommended.
Variables Mean Median Maximum Minimum Std Dev Obs.
CL_REFIN -0.5 0.0 50.0 -42.8 13.7 58 multiplying by 100, or estimating those variables in percentage changes otherwise makes the standard deviation of them drop significant.
Figure 1a and 1b in the appendix provide an overview of the original data presented in this thesis Both domestic credit supply and quasi-money exhibit a similar upward trend, particularly accelerating after 2007, while incremental output shows minimal change As discussed in Chapter 2, the significant increase in credit can be attributed to substantial capital inflows and real estate price bubbles.
UNIT ROOT TESTS
Asteriou and Hall (2007) assert that macroeconomic variables typically exhibit trends, and both the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests confirm that all analyzed variables are non-stationary, as indicated in Tables 4.2 and 4.3.
Table 4.2: Augment Dickey-Fuller test
Source: Calculated from IMF-IFS and GSO data
The analysis indicates that while the changed data are stationary, the exclusion of the CL_CPI variable did not meet the criteria for stationarity in the ADF test, although it did pass the PP test Consequently, these data can be utilized to identify the key answers for the thesis.
Variables Exogenous t-statistic p-value Data in Level
Source: Calculated from IMF-IFS and GSO data
Concern to the optimal lag problem for VAR model, different criteria are used to determine.
Table 4.4: Optimal lap-Classical market
Lag LogL LR FPE AIC SC HQ
Source: Self calculation by using Eviews
Table 4.5: Optimal lap-Augmented market
Lag LogL LR FPE AIC SC HQ
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
HQ: Hannan-Quinn information criterion
Table 4.4 and 4.5 illustrate that several statistical methods are available for selecting the optimal lag length in our model, with the Akaike Information Criterion (AIC) and Schwarz Criterion (SC) being the most significant To minimize the information criteria, the optimal number of lags is determined to be five for both markets based on the minimum AIC Therefore, I will use five lags in both cases when applying the VAR model.
VAR REGRESSION STATISTICS FOR CLASSICAL AND AUGMENTED
Table 4.6 presents regression statistics for VAR in a classical market, showcasing p-values from a VAR Granger Causality test with five lags The null hypothesis indicates that the independent variable does not cause the dependent variable Significant relationships emerged, with lagged values of most variables predicting output at a 5% level, except for the lending rate, which only predicts output at a 10% level While the lending rate does not forecast money-quasi, it does help predict the consumer price index, output, and refinancing rate at a 10% significance level The refinancing rate provides limited predictive power for other macro variables, except for output Money supply does not Granger cause the lending rate but is useful for predicting output, price level, and refinancing rate at a 10% level Historically, from the late 1980s to 2000, Vietnam maintained a positive real interest rate to control inflation, achieving this goal despite instances where deposit rates exceeded lending rates Since August 2000, the State Bank of Vietnam (SBV) has shifted to a base interest rate mechanism, allowing control over lending rates Currently, lending rates in Vietnam remain non-liberalized, failing to reflect true demand and supply in the money market Notably, in a credit-less market, no independent variable in the classical market Granger causes money supply, indicating potential gaps in the classical market framework.
Table 4.6: VAR Regression Statistic- Classical market
Source: Calculated from IMF-IFS and GSO data
Independent vars CL_CPI CL_LR CL_M2 CL_OUTPUT CL_REFIN
Table 4.7: VAR Regression Statistic- Augmented market VAR Granger- CausalityTest
Note: The reported are p-value Source: Calculated from IMF-IFS and GSO data
The augmented credit market reveals significant findings regarding the relationship between credit and money supply, with lagged credit values showing strong predictive power (p-value of zero) However, credit does not Granger-cause output or price levels, indicated by p-values of 0.67 and 0.19, respectively This suggests that the State Bank of Vietnam primarily uses credit to inject liquidity into the money market Additionally, price levels are useful for forecasting money supply at a 5 percent significance level While the lending rate does not predict output, it shows a weaker Granger-causal relationship with money policy shocks and credit (p-value of 0.1) Notably, the combination of price level and refinancing rates Granger-causes output, and both output and lending rates can predict credit at a 10 percent significance level, whereas M2 fails to forecast either credit or output.
CL_CPI CL_CREDIT CL_LR CL_M2 CL_OUTPUT CL_REFIN
Regression statistics reveal significant insights into monetary transmission and lending channels Firstly, while classical market VAR results may inadequately explain these mechanisms, an augmented model that includes credit proves highly effective in forecasting money supply Secondly, in the absence of credit, the lending rate appears to have a diminished impact on money market regulation, but its influence increases when credit is included Lastly, the price level and lending rate emerge as the second and third most important variables for predicting money supply, even beyond the effects of credit.
IMPULSE RESPONES AND VARIANCE DECOMPOSITIONS
This article examines the impact of monetary policy and credit variable shocks on the macroeconomy, particularly in Vietnam By analyzing regression estimates, we identify changes in monetary policy through fluctuations in the money supply, specifically M2.
Figure 4.1: The impulse response functions for classical market
Response of CL_CPI to
Response of CL_CPI to CL_LR
Response of CL_CPI to CL_M2
Response of CL_CPI to CL_OUTPUT 2
Response of CL_CPI to CL_REFIN 2
Response of CL_LR to
Response of CL_LR to CL_LR 10
Response of CL_LR to CL_M2 10
Response of CL_LR to CL_OUTPUT 10
Response of CL_LR to CL_REFIN 10
Response of CL_M2 to CL_LR 6
Response of CL_M2 to CL_M2 6
Response of CL_M2 to CL_OUTPUT 6
Response of CL_M2 to CL_REFIN 6
Response of CL_OUTPUT to
Response of CL_OUTPUT to CL_LR
Response of CL_OUTPUT to CL_M2
Response of CL_OUTPUT to CL_OUTPUT 4
Response of CL_OUTPUT to CL_REFIN 4
Response of CL_REFIN to
Response of CL_REFIN to CL_LR
Response of CL_REFIN to CL_M2
Response of CL_REFIN to CL_OUTPUT 15
Response of CL_REFIN to CL_REFIN 15
Source: Calculated from IMF-IFS and GSO data
The monetary policy shock, highlighted by a significant decline from 4.46 in the first quarter to 0.006 in the third quarter, indicates a substantial tightening of policy Output adjusts with a lag, peaking in the second quarter, and exhibits greater fluctuations compared to M2 This suggests that output is quite sensitive to monetary shocks, albeit not in a one-to-one ratio, with reactions persisting for two quarters before recovering growth in the fourth quarter The output reaches a trough of 1.32 percent below its pre-shock level The lending rate shows a positive response to the monetary shock after one quarter, peaking at 2.2 percent in the sixth quarter, but subsequently declines over the next three quarters, despite M2 maintaining a tight policy Additionally, the price level reacts slowly to M2 shocks, with noticeable effects emerging after the fifth quarter, while the refinancing rate also responds to the monetary shock after one quarter, peaking at 3.8 percent.
A decrease in lending rates typically leads to an increase in the money supply after one quarter, albeit for a short duration According to economic theory, contractionary monetary policy raises lending rates, which in turn restricts investment, ultimately reducing aggregate demand and output However, this expected response does not always occur consistently.
Historically, the relationship between two variables has shown anomalies, particularly regarding M2 growth and lending rates Despite a continuous increase in M2, lending rates remained stagnant at 14.4 percent in 1998, contrary to established literature This trend reemerged from the third quarter of 2002 to the third quarter of 2008, influenced by poor credit conditions in real estate and financial investments (Dung, 2010) Additionally, lending rate shocks impact output responses beginning in the second quarter, but these effects are short-lived, lasting only one quarter In contrast, output shocks prompt a significant reaction in money supply after just one quarter.
These reactions are consistent with the regression statistic; output is highly sensitive to monetary shock; the impact of lending rate shock on money supply is somewhat weak.
5 Response of all variables represent the cumulative percentage change following the shock
Figure 4.2: The impulse response functions for augmented market
Response to Cholesky One S.D Innovations ± 2 S.E.
Response of CL_CPI to
Response of CL_CPI to CL_CREDIT 2
Response of CL_CPI to CL_LR 2
Response of CL_CPI to CL_M2 2
Response of CL_CPI to CL_OUTPUT 2
Response of CL_CPI to CL_REFIN 2
Response of CL_CREDIT to
Response of CL_CREDIT to CL_CREDIT 8
Response of CL_CREDIT to CL_LR
Response of CL_CREDIT to CL_M2
Response of CL_CREDIT to CL_OUTPUT 8
Response of CL_CREDIT to CL_REFIN 8
Response of CL_LR to
Response of CL_LR to CL_CREDIT 8
Response of CL_LR to CL_LR 8
Response of CL_LR to CL_M2 8
Response of CL_LR to CL_OUTPUT 8
Response of CL_LR to CL_REFIN 8
Response of CL_M2 to CL_CREDIT 4
Response of CL_M2 to CL_LR 4
Response of CL_M2 to CL_M2 4
Response of CL_M2 to CL_OUTPUT 4
Response of CL_M2 to CL_REFIN 4
Response of CL_OUTPUT to
Response of CL_OUTPUT to CL_CREDIT 4
Response of CL_OUTPUT to CL_LR
Response of CL_OUTPUT to CL_M2
Response of CL_OUTPUT to CL_OUTPUT 4
Response of CL_OUTPUT to CL_REFIN 4
Response of CL_REFIN to
Response of CL_REFIN to CL_CREDIT 12
Response of CL_REFIN to CL_LR 12
Response of CL_REFIN to CL_M2 12
Response of CL_REFIN to CL_OUTPUT 12
Response of CL_REFIN to CL_REFIN 12
Source: Calculated from IMF-IFS and GSO data
In the augmented market, credit shocks initially lead to a 4.9% expansion, but this is short-lived, plummeting to -1.39% by the second quarter The tightening of credit persists for twelve quarters, significantly impacting output, which declines from 1.1% to -1.9% by the fourth quarter following the credit shock Although output begins to recover, it remains within a narrow fluctuation range until the year's end Similarly, M2 responds immediately and sharply to credit shocks, dropping from 3% to 0.5% within just one quarter, with this decline lasting throughout the twelve-quarter period.
Lending rate is slow reactions to credit shocks, because of specific characteristic of Vietnam’s credit market as mentioned above.
In response to monetary shocks, credit exhibits a modest and enduring reaction as M2 shifts from easing to tightening during the initial two quarters Output begins to respond to M2 shocks starting in the second quarter, reaching a low point of 0.7 percent by the fourth quarter The refinancing rate reacts to monetary policy tightening over a two-quarter period, whereas the price level shows a relatively sluggish response, taking five quarters to adjust.
Credit responses to lending rate shocks are gradual, as indicated in the second row and third column of the analysis Specifically, credit shows a delayed reaction, responding after four quarters and then decreasing immediately following an increase in lending rates In contrast, output reacts more swiftly, taking just one quarter to respond to lending rate changes, but continues to fluctuate over the following eight quarters.
In summary, credit shocks significantly impact monetary policy, leading to a decline in output and prolonged effects on lending rates Additionally, monetary policy shocks result in reduced output, higher refinancing rates, and a slight decrease in price levels.
In our analysis of variance decomposition for M2 across two markets, significant differences emerge in the impact of various shocks In the classical market, M2 accounts for over 50% of the variance in its error, while this is not the case in the augmented market This trend persists across all time horizons, gradually diminishing with longer horizons Notably, at the 13-quarter mark, credit shocks contribute 21.5% to the forecast error variance of M2, compared to 15.7% from M2 itself In the initial quarter, credit shocks dominate, representing over 50% of the prediction error for money-quasi Additionally, price level shocks play a crucial role in forecasting M2 within the credit market Following credit shocks, refinancing rate shocks contribute 20% to the variance after nine quarters in the augmented market, whereas their impact is less pronounced in the classical model Although lending rate shocks have gained some significance in the augmented market, they remain weaker compared to other factors Furthermore, the output response is slightly lower in the credit market, at 8.4% compared to 9.2% in the classical market after nine quarters.
The variance decomposition of output reveals that M2 shock has a smaller impact in credit markets, accounting for 19.9 percent initially and decreasing to 5.1 percent after nine quarters, with the gap widening over longer horizons The augmented market, which incorporates credit variables, reduces the contribution of lending rates to variance decomposition from 20 percent to 14 percent after five quarters Notably, credit shocks represent 18.9 percent of the forecast error in output at thirteen quarters, highlighting their significant and ongoing influence in predicting output errors.
The findings indicate that output and price levels are the primary factors influencing credit forecast errors, albeit with a fragile contribution of 15.2% and 14.8% over a 13-quarter horizon, respectively Similar to the impulse response results, M2 shocks contribute minimally, accounting for only 3.5% of credit error predictions after thirteen quarters Notably, credit shocks represent nearly half of the forecast error throughout this period Additionally, the introduction of credit variables significantly alters the decomposition of lending rate shocks, with approximately 30% of the prediction error in lending rates attributed to credit shocks, though this influence diminishes over longer horizons.
Sources: Calculated form IMF-IFS and GSO data
Table 4.8: Variance Decompositions for vector autoregression for Classical and Augmented Market
Empirical results reveal that in VAR regression analysis, neither the lagged lending rate nor the refinancing rate effectively predicts money supply, suggesting potential gaps in classical market models Conversely, credit emerges as a significant predictor of money supply in augmented markets The impulse response and variance decomposition analyses show that the relationship between money supply and lending rate shocks can be inconsistent in credit-absent markets, while M2 responds promptly and strongly to credit shocks Credit shocks are crucial for forecasting errors in money supply, with variations in magnitude and order among macroeconomic variables in augmented markets, highlighting the dominant influence of credit and price levels on output and M2.
RESULTS COMPARISON
This section will compare the thesis's findings with relevant research conducted in Vietnam and other countries, highlighting both similarities and differences to clearly identify the contributions of this thesis.
Our findings validate the presence of the credit channel in Vietnam, aligning with the research conducted by Bernanke and Gertler (1995) The empirical results of this study are consistent with the majority of previous studies that employed the VAR model as a primary analytical approach, including the work of Kim.
(1999), Lown and Morgan (2002), Disyatat and Vongsinsirikul (2003), Abdul (2009); and structure vector autoregression model such as Kubo.A (2007), Catão and Pagan
(2010) that credit channel plays important role in monetary transmission mechanism. Once again, this study is in line with Podpiera (2007), Charoenseang J and Manakit P.
A study conducted in 2006 reaffirmed the significance of the credit channel in Thailand and Chile, despite differences in countries and econometric methods Similarly, research on Vietnam aligns with the findings of Hung and Pfau (2008), concluding that the credit channel is more crucial than the interest rate channel, making it the most significant channel in the Vietnamese context.
However, the study’s finding conflicts with several researches, such as Ramey (1993), Suzuki (2004) when their empirical provides evidence the insignificant role of credit in transmission of monetary policy.
In general, despite of different market conditions and Vietnam’s specific characteristic,the empirical finding still has same results to majority relevant studies.
CONCLUSION AND POLICY IMPLICATION
CONCLUSIONS
This thesis investigates the role of credit in the monetary transmission mechanism in Vietnam from Q1 1996 to Q3 2010, utilizing data primarily sourced from IFS-IMF, with the exception of industrial output Employing a reduced-form VAR model, the study differentiates between a classical market (without credit) and an augmented market (with credit) to analyze the credit channel's impact The research begins with stationary and unit-root tests to determine the suitability of the data for modeling, followed by selecting optimal lags based on the minimum AIC criteria The VAR Granger-causality test assesses the causal relationships between money supply, credit, and other relevant variables Additionally, impulse response functions are utilized to observe the effects of shocks on each variable, while variance decomposition measures the contribution of each variable to the forecast error of monetary shocks over a specified horizon.
In classical markets, there are no independent variables that exhibit Granger causality with money supply, which primarily predicts most dependent variables except for the lending rate This suggests that VAR regression statistics may not adequately explain the monetary transmission mechanism in such markets Conversely, in augmented markets, domestic credit plays a significant role in predicting money supply, along with price levels and lending rates, which lose their predictive function in classical markets However, the lagged value of money supply fails to forecast credit, output, or lending rates This highlights the importance of the credit channel in monetary policy transmission within augmented markets.
In classical markets, output and refinancing rates react to monetary policy shocks, such as tightening, after a lag of one period The output is highly sensitive to monetary shocks, while the lending channel's response can be short-lived and inconsistent In credit markets, the output reacts robustly to credit shocks after one lag, while M2 responds immediately and strongly, aligning with the findings of Bernanke and Gertler Additionally, the correlation between the lending channel and the credit channel is relatively weak.
In the variance decomposition of M2, a distinction is made between two markets, revealing that over fifty percent of the forecast error in M2 is due to credit shocks in the first quarter of the augmented market Both credit and price level shocks significantly influence the variance decomposition of M2, although their impact diminishes over longer time horizons Additionally, there is a notable difference in both magnitude and the order of variance decomposition of M2 in markets characterized by credit.
Since those findings, the study agrees with Bernanke and Gertler view that credit channel played as important channel in monetary transmission mechanism in Vietnam case.
POLICY IMPLICATION
The study highlights significant implications for policymakers regarding the credit channel and the monetary transmission mechanism The findings demonstrate the crucial role that the credit channel plays in the transmission of monetary policy.
Vietnam; hence; in order to regulate the economy development through reasonable monetary policy at each period, some recommendations are given below:
The credit channel is vital in Vietnam's monetary transmission mechanism, necessitating careful control of the credit sector when implementing new monetary policies Changes in government policy, whether from loose to tight or the reverse, directly and significantly impact this channel The State Bank of Vietnam (SBV) is essential in regulating credit flow within the economy through various instruments As the central bank overseeing commercial banks, the SBV must provide sound recommendations and practical policies to the government To enhance the effectiveness of these tasks, it is crucial to grant the SBV greater autonomy in decision-making.
Monetary policy tightening leads to immediate and significant responses to credit shocks, causing a dramatic decline in output after just one lag This effect occurs through two primary channels: the bank lending channel and the balance sheet channel, ultimately resulting in reduced output due to a credit supply shortage for manufacturing enterprises To mitigate these impacts and support production, especially in an agriculture-driven economy like Vietnam, it is crucial to implement effective support programs for these enterprises While the Vietnamese government has initiated several subsidized programs in recent years, there remain significant gaps in their implementation Therefore, it is essential for the government to enhance control and supervision to ensure these policies are effective.
The relationship between lending channels and monetary policy in Vietnam often deviates from theoretical expectations due to various factors, including the underdevelopment of the country's financial market Despite having some degree of autonomy in credit operations and interest negotiations, commercial banks remain reliant on the State Bank of Vietnam (SBV) for lending decisions Therefore, it is crucial to implement alternative solutions for lending rates, moving towards a more market-oriented lending mechanism.
Credit growth and inflation are significant factors in the short-term dynamics of money quasi, as indicated by variance decomposition results Therefore, it is crucial to carefully manage credit growth to support financial development projects rather than speculative activities, in order to prevent bubbles and reduce the risk of bad debts.
Monetary policy must be applied with caution and flexibility to improve the effectiveness of money stock management To fulfill this responsibility, the State Bank of Vietnam (SBV) needs to monitor and anticipate shifts in both domestic and global financial markets, allowing for the timely implementation of suitable policies.
Understanding the critical role of credit channels in the monetary transmission mechanism is essential for mitigating negative impacts from newly implemented policies Successful implementation of these measures can lead to significant improvements in the health of Vietnam's financial market.
LIMITATION AND FURTHER STUDIES
Although, this study has answered all the key questions about the role of credit in monetary transmission mechanism in Vietnam case, it also contains some limitations.
Vietnam faces significant limitations in data resource availability, restricting the study's ability to analyze longer time periods The country's GDP data has only been available since 2000, necessitating the use of industrial output value as a proxy for GDP Additionally, domestic credit value is utilized as a substitute for the net percentage tightening variable employed in Lown and Morgan’s research.
VAR models are essential tools for understanding the role of credit in monetary transmission They have significantly enhanced the toolkit of macroeconometricians by providing a reliable framework for data analysis and generating accurate multivariable benchmark forecasts (Stock and Watson, 2001).
This study utilized impulse response and variance decomposition within a VAR model to analyze the impact of credit and monetary transmission using quarterly data from 1996:Q1 to 2010:Q3 Future research could enhance the analysis by incorporating monthly data over an extended period to better understand the correlations and interactions between these variables Additionally, employing alternative models, such as the vector error correction (VECM) model, would be beneficial to determine if the previous conclusions remain valid in the context of Vietnam.
Besides that, the study could add several variables such as Federal Funds rate, exchange rate to exploit how the changes of estimated result with joined new variables.
6 The number of banks tightening less the number easing, divided by the number reporting (Lown and Morgan, 2002)
The credit plays important role in monetary transmission Hence, potential study may expand by consider the determinants of domestic credit in Vietnam case.
Asteriou, D and Hall, S.G (2007) Applied Econometrics: A modern approach using eview and microfit, Revised Edition Palgrave Macmillan.
Abdul Aleem (2009) Transmission mechanism of monetary policy in India Journal of
Asian Economics 21: 186-197(October) Contents list available at ScienceDirect.
Bernanke, S and Alan S Blinder (1988) Credit, Money, and Aggregate Demand The
American Economic Review, Vol 78, No 2, (May): 435-439 Papers and Proceedings of the One-Hundredth Annual Meeting of the American Economic Association.
Bernanke, S and Gertler (1995) Inside the Black Box: The Credit Channel of
Monetary Policy Transmission Journal of Economic Perspectives, American Economic Association: 27-48.
Balazs Egert (2009) Monetary transmission mechanismin central and eastern europe:
Surveying the surveyable OECD Economics Department Working Papers No 654.
BaoMoi News (2011) Vietnam targets 2012 credit growth at 15-17 pct: Central bank.
Available from: http://en.baomoi.com/Info/Vietnam-targets-2012-credit-growth- at-1517-pct-Central-bank/5/206102.epi
Charoenseang, J and Manakit, P.,(2006) Thai monetary policy transmission in an inflation targeting era Journal of Asian Economics 18: 144–157 Available online at www.sciencedirect.com
Catão, L A V and Pagan, A (2010) The Credit Channel and Monetary Transmission in Brazil and Chile: A Structured VAR Approach NCER Working Paper Series, No.53.
Camen, U (Dec, 2006) Monetary policy in Vietnam: The case of transition country.
Disyatat, P and Vongsinsirikul, P., (2003) Monetary policy and the transmission mechanism in Thailand Journal of Asian Economics 14: 389–418 (May)
In his 2010 article, T.T Dung examines the mechanisms of interest rate regulation in Vietnam, highlighting the complexities and challenges faced by the financial system The analysis provides insights into how these regulatory frameworks impact economic stability and growth Dung emphasizes the need for effective policies to enhance the efficiency of interest rate management in the country For further details, the full article is available online at the specified URL.
%E1%BA%A5t-%E1%BB%9F-vi%E1%BB%87t-nam/
Fiorentini,.R and Tamborini,.R, (2001) The Monetary Transmission Mechanism in
Italy: The Credit Channel and a Missing Ring The journal of Bocconi University.
Gujarati, D.N (2003) Basic Econometrics, 4 th Edition, McGraw-Hill.
Gurley, J.G and Shaw, E.S (1956) Financial Intermediaries and the Saving—
Investment Process, Journal of Finance 11: 257–276
Hung, L.V and Pfau, W.D (2008) VAR analysis of the monetary transmission mechanism in Vietnam Vietnam Development Forum, Working Paper 081.
Hall, S (2001) Credit channel effects in the monetary transmission mechanism Bank of England Quarterly Bulletin.
IMF world and country noted, (2009) International Monetary Fund_ International
IBP USA, 2005 Vietnam Financial and Trade Policy Handbook Chapter Three- The financial system in Vietnam, page 45.
Ishii, S (2008) Concerns about credit growth: Vietnam’s New Challenges Amid Signs of Overheating IMF survey Magazine Available from: http://www.imf.org/external/pubs/ft/survey/so/index.aspx?type
Kim, H.E., (1999) Was Credit Channel a Key Monetary Transmission Mechanism
Following the Recent Financial Crisis in the Republic of Korea ? The World Bank,
Policy Research Working Paper 3003 (April).
Kubo,.A, (2007) Macroeconomic impact of monetary policy shocks: Evidence from recent experience in Thailand Journal of Asian Economics 19 : 83–91
(December) Available online on www.sciencedirect.com.
Lown, S and Morgan, P (2002) Credit Effects in the Monetary Mechanism The journal of Economic Policy Review Federal Reserve Bank of New York, page 217-235
Mishkin, S (1996) The channels of monetary transmission: lessons for monetary policy Studies , Banque De France Bulletin Digest _No 27.
Modigliani, F and Papademos, L (1977) The Structure of Financial Markets and the
Monetary Mechanism Research papers and Publication, Federal Reserve Bank
Podpiera, A.M.P ,(2007) The role of banks in the Czech monetary policy transmission
Mechanism Economic of Transition Volume 15(2): 393-428
Ramey, V (1993) How important is the credit channel in the transmission of monetary policy? Carnegie-Rochester Conference Series on Public Policy 39, North-Holland.
Reuters News, 2012 UPDATE 1-Vietnam 2011 credit growth dips to 10.9 y/y -c.bank.
Available from: http://www.reuters.com/article/2012/01/18/vietnam-economy- credit-idUSL3E8CI4ZO20120118
Reuters News, 2012 UPDATE 1-Vietnam c.bank sets credit targets for each bank.
Available from: http://www.reuters.com/article/2012/02/14/vietnam-economy- credit-idUSL4E8DE0IC20120214
Romer, C and Romer, D.,(1990) New Evidence on the Monetary Transmission
Mechanism Brookings Papers on Economic Activity (January): 149-214.
S&P Reporting, 2011 Vietnam stability hinges on slower credit growth, S&P says.
Retrieved June 20, 2011 form http://en.baomoi.com/Home/economy/en.vietstock.vn/Vietnam-stability-hinges- on-slower-credit-growth-SP-says/132412.epi.
Stock, J.H and Watson, M.W, (2001) Vector Autoregressions Journal of Economic
State Bank of Vietnam (SBV), (2003) The Law on the State Bank of Vietnam In
Documents Hanoi: State Bank of Vietnam.
SBV, (2008) State Bank of Vietnam -Annual Report 2008
SBV, (2009) State Bank of Vietnam -Annual Report 2009
SBV, (2010) State Bank of Vietnam -Annual Reporting- 2010.
Suzuki, T (2004) Is the Lending Channel of Monetary Policy Dominant in Australia?
The economic record, vol 80, no 249: 145–156.(June)
VietnamPlus News (2009) WB predicts growth of 5.5 percent Available from http://www.ncseif.gov.vn/sites/en/Pages/wbpredictsgrowthof5.5percent-nd- 13529.html
Warner.E.J and Georges C (2001) The credit channel of monetary policy transmission: Evidence from stock returns Economic Inquiry Vol 39, Issue 1. Available at SSRN: http://ssrn.com/abstract%3088
World Bank report,(2006) Overview the capital markets in Vietnam and directions for development Banking Finance and Investment Book-651, (May).
APPENDIX Original data illustration Figure 1a