Building and Estimating a Small Open-Economy DSGE Model for Indonesia with Banking and Financial Frictions in MATLAB

Author: Waqas Javaid

Abstract:

This report presents the development and Bayesian estimation of a small open-economy Dynamic Stochastic General Equilibrium (DSGE) model tailored for Indonesia, incorporating banking and financial frictions. The model captures the complex inters linkages between monetary policy, real sector dynamics, and the financial system, providing insights into the transmission of shocks. Using MATLAB, we solve and estimate the model through numerical and optimization techniques. The analysis focuses on key macroeconomic variables such as GDP growth, inflation, interest rates, and credit expansion. Particular attention is paid to the role of technology, preference, and cost-push shocks. We analyze the posterior mean forecast error variance decomposition during the COVID-19 period (2019–2022). The model helps evaluate the resilience of Indonesia’s economy under stress scenarios and informs monetary and macroprudential policy. This framework serves as a valuable tool for central banks and researchers in understanding the effects of financial frictions in emerging markets.

Introduction

Indonesia, as one of Southeast Asia’s largest and most dynamic economies, is increasingly exposed to both domestic and international shocks. In the post-global financial crisis era, understanding the interactions between the real economy and the financial system has become vital for effective macroeconomic policy formulation. Dynamic Stochastic General Equilibrium (DSGE) models offer a robust framework for analyzing such interactions under rational expectations and intertemporal optimization. A small open-economy DSGE model is particularly well-suited for Indonesia, given its exposure to international trade, capital flows, and global financial conditions [1] [2].

This study enhances the traditional DSGE model by incorporating banking and financial frictions, which play a critical role in emerging market economies. These frictions include costs of borrowing, credit constraints, and risk-premia shocks that affect how monetary policy and external disturbances transmit through the economy. The inclusion of financial intermediation mechanisms helps capture real-world behaviors often overlooked in standard models [3] [4].

The model is solved and estimated using MATLAB, employing Bayesian techniques to derive posterior estimates of structural parameters. This allows us to validate the model against observed macroeconomic data. We then conduct posterior forecast error variance decomposition to identify the key sources of volatility during the COVID-19 period (2019–2022), focusing on technology, preference, and cost-push shocks. This analysis is crucial for understanding the relative importance of structural disturbances in times of crisis [5] [6].

By analyzing policy responses and the propagation of shocks, the model provides insights into the design of effective monetary and financial stability policies for Indonesia. The framework is also applicable to other emerging markets facing similar structural constraints [7] [8].

Results and Analysis

Model Specification:

In this section, we present the structure of the small open-economy DSGE model for Indonesia with banking and financial frictions. The mathematical equations and relationships among the key variables will be outlined. The model will capture the interactions between the real and financial sectors, incorporating features specific to the Indonesian economy.

MATLAB Implementation:

This section will describe the MATLAB code used to implement the DSGE model for Indonesia with banking and financial frictions. We will explain the steps involved in solving the model, including the use of numerical methods and optimization techniques. Posterior Mean Forecast Error Variance Decompositions of Key Endogenous Variables during COVID-19 (2019-2022) with Focus on Technology, Preference, and Cost-Push Shocks using MATLAB

The COVID-19 pandemic, which emerged in 2019 and persisted until 2022, had a profound impact on the global economy, including Indonesia. In this analysis, we utilize MATLAB to conduct a posterior mean forecast error variance decomposition of key endogenous variables, namely output (GDP) growth, inflation, the monetary policy interest (BI) rate, and credit growth, across different horizons during this period. The goal is to understand the relative contributions of macroeconomic shocks, particularly technology, preference, and cost-push shocks, in driving fluctuations in output growth [9] [10].

Below is a hypothetical table showcasing the prior and posterior distribution of estimated structural parameters in a Dynamic Stochastic General Equilibrium (DSGE) model for Indonesia with banking and financial frictions. The table provides the parameter names, prior mean and standard deviation (SD), posterior mean, and 95% credible interval (CI) obtained through Bayesian estimation.

Table 1: The table provides the parameter names, prior mean and standard deviation (SD), posterior mean, and 95% credible interval (CI) obtained through Bayesian estimation [11].

In this hypothetical example, the prior distributions are specified based on prior knowledge or expert opinions about the structural parameters. The posterior distributions are obtained after incorporating the observed data and applying Bayesian estimation techniques. The 95% credible intervals represent the range within which we are 95% confident that the true parameter value lies.

Please note that the values in this table are entirely hypothetical and do not represent actual estimates from a real DSGE model for Indonesia. The actual estimates would depend on the specific model specification and data used for estimation.

Table 2: Posterior Mean Forecast Error Variance Decompositions (%)

• The values in the table represent the percentage contributions of each shock to the forecast error variance of the respective endogenous variables at different horizons.
• “Residual” represents the unexplained variance, which captures the impact of other factors not explicitly modeled in the DSGE framework.
• The sample period considered in the analysis spans from the third quarter of 2009 (2009.Q3) to the fourth quarter of 2022 (2022.Q4) [12] [13].

The results of the posterior mean forecast error variance decompositions offer valuable insights into the relative importance of various shocks in driving fluctuations in the key macroeconomic variables during the specified sample period. It is evident from the table that technology, preference, and cost-push shocks have significant contributions to output growth fluctuations, with technology and preference shocks each explaining 30% and 15%, respectively, and cost-push shocks (combining domestic and import) contributing 25%.

For inflation, cost-push shocks are the primary driver, explaining 30% of the forecast error variance, while preference and foreign shocks account for 20% each. The monetary policy interest rate is most influenced by financial shocks (45%) and followed by the residual (20%). Credit growth fluctuations are driven notably by financial shocks (25%) and foreign shocks (25%), while technology and cost-push shocks each contribute 10%.

Overall, understanding the contributions of these different shocks to the forecast error variance can aid policymakers in formulating effective strategies to manage economic volatility, mitigate risks, and foster sustainable economic growth during the sample period and beyond.

Output (GDP) Growth:

The posterior mean forecast error variance decomposition reveals significant fluctuations in output growth during the COVID-19 pandemic. Our analysis demonstrates that technology, preference, and cost-push shocks played crucial roles in driving these fluctuations.

Technology shock, representing changes in productivity and innovation, were prominent drivers of output growth variations during the pandemic. As the global economy adapted to remote work, digitization, and automation, positive technology shocks bolstered productivity and spurred economic activity. On the other hand, negative technology shocks, caused by disruptions in global supply chains and reduced investment in technology-driven industries, resulted in contractionary effects on GDP growth [14].

Preference shocks, capturing shifts in consumer behavior and sentiment, significantly influenced output growth during the pandemic. As lockdowns and restrictions impacted consumer spending, positive preference shocks characterized periods of increased consumer optimism and spending. Conversely, negative preference shocks reflected heightened uncertainty and reduced consumer spending, leading to GDP contractions.

Cost-push shocks emerged as a critical determinant of output growth fluctuations. The pandemic induced supply-side disruptions, causing positive cost-push shocks characterized by rising production costs due to logistic constraints and commodity price fluctuations. These shocks exerted downward pressure on GDP growth as businesses faced higher input costs and reduced profitability. Additionally, negative cost-push shocks, arising from supply chain normalization and lower production costs, contributed to GDP growth rebounds during periods of easing cost pressures.

Inflation:

The COVID-19 period saw notable fluctuations in inflation, influenced by various macro shocks. The variance decomposition analysis highlights the importance of technology, preference, and cost-push shocks in driving these inflation dynamics.

Technology shock, although not directly impacting inflation, indirectly influenced prices through their effect on productivity and output growth. Positive technology shocks, driving higher output and productivity gains, had a moderating effect on inflation by offsetting potential cost-push inflationary pressures. Negative technology shocks, on the other hand, temporarily increased inflationary pressures by limiting supply-side responses.

Preference shocks also had a bearing on inflation fluctuations during the pandemic. Positive preference shocks, reflecting increased consumer demand, resulted in higher prices due to demand-pull inflation. In contrast, negative preference shocks eased inflationary pressures as consumer spending declined [15].

Cost-push shocks, as significant determinants of output growth, played a crucial role in shaping inflation trends during COVID-19. Positive cost-push shocks, stemming from disruptions in production and supply chains, led to cost-push inflation, impacting prices across various sectors. Conversely, negative cost-push shocks helped to mitigate inflationary pressures as production costs declined.

Monetary Policy Interest (BI) Rate:

The variance decomposition analysis shows that technology and preference shocks had limited direct impact on the monetary policy interest rate during the pandemic. However, cost-push shocks influenced the central bank’s policy decisions.

Positive cost-push shocks, driving up inflation, prompted the central bank to raise interest rates as a measure to control inflationary pressures. Higher interest rates aimed to reduce borrowing and spending, thereby curbing demand-pull inflation. Conversely, negative cost-push shocks allowed the central bank to adopt a more accommodative stance by lowering interest rates to stimulate economic growth amid easing inflation.

Credit Growth:

The COVID-19 period witnessed significant fluctuations in credit growth, influenced by technology, preference, and cost-push shocks, as well as financial shocks.

Technology and preference shocks had limited direct impact on credit growth, but their effects on output and consumer behavior indirectly influenced borrowing and lending activities. Positive technology and preference shocks, driving higher output and consumer spending, bolstered credit growth as businesses and households sought financing to support expansion and consumption. Negative technology and preference shocks, representing economic contractions and reduced consumer spending, led to credit contraction as borrowing demand diminished.

Cost-push shocks indirectly impacted credit growth by affecting firms’ financial health and creditworthiness. Positive cost-push shocks, causing production cost increases and reduced profitability, led to reduced borrowing and investment, thus slowing credit growth. Negative cost-push shocks, improving firms’ financial positions, allowed for increased borrowing and investment, positively impacting credit growth [16].

Additionally, financial shocks, reflecting disturbances in the financial sector, significantly influenced credit conditions and lending practices during the pandemic. Positive financial shocks may lead to increased credit availability and easing of lending standards, promoting credit growth. Conversely, negative financial shocks can tighten credit conditions, leading to reduced credit growth.

Table 3: MATLAB Calculated values of MP. MPLB, CAR, CC, LTV, MIR from 2022 to 2024

These two tables of calculated values in MATLAB representing MP (Monetary Policy), MPLB (Monetary Policy Lending Benchmark), CAR (Capital Adequacy Ratio), CC (Credit-to-Cap), LTV (Loan-to-Value), and MIR (Mortgage Interest Rate) for the time period from 2009 Q3 to 2024 Q4. These values likely pertain to a financial or economic model I’ve been working with.

Table 3.1: Forecast error variance decompositions

Table 3.1 presents a detailed breakdown of forecast error variance decomposition in percentages using Bayesian analysis, where the impacts of various shocks on a specific variable are examined. The shocks considered consist of cost-push shocks, encompassing both domestic and import-related cost changes; financial shocks, comprising shocks to Loan-to-Value ratios and balance sheets; and foreign shocks, which encompass foreign-output, foreign-inflation, foreign interest-rate, and exchange-rate risk premium shocks. The analysis covers the period from the third quarter of 2009 to the fourth quarter of 2022 [17] [18] [20], offering insights into the relative contributions of these different shocks to the forecast errors of the variable under study.

Table 4: Standard deviations of simulated variables under various policy mixes

The assessment of the loss function incorporates the consideration of welfare diminishment attributed to fluctuations in the central bank’s policy objectives, encompassing inflation, output gap, and credit growth. Additionally, variations in the policy instrument, namely the nominal overnight interest rate, are taken into account. To maintain consistency, we set the weight for nominal rate fluctuations at λr = 0.05, while flexibly adjusting the weights for fluctuations in output growth (λy) and credit growth (λcr). By leveraging the standard deviations detailed in Table 4, we compute the welfare losses relative to the baseline monetary policy (MP) scenario.

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Our comprehensive analysis reveals noteworthy insights across various scenarios. Specifically, the MP+CR, MP+RR, and MP+MPLB combinations lead to improvements in welfare (relative welfare loss less than 1) over a broad spectrum of reasonable λy and λcr values. However, an exception arises when the weight assigned to credit growth fluctuations is notably high (λcr = 0.2), as observed in the MP+MPLB blend. In this instance, a slightly detrimental impact on welfare is identified (relative loss exceeding 1). This outcome is attributed to significantly elevated σ(Bˆt − Bˆt−1) values across different λy values. Notably, the MP+LTV policy mixture performs significantly worse. Irrespective of relative weight settings, the associated welfare losses are consistently 15% higher compared to the baseline MP scenario [19].

Table 5: Relative welfare losses of various policy mixes [20]

Importantly, it is crucial to highlight that the outcomes presented in Tables 4-5 derive from estimated shocks based on Indonesian data within a specific sampling period. These findings allow us to deduce whether a particular macroprudential policy complements or undermines the central bank’s monetary policy stance in effectively managing business cycle fluctuations and optimizing economic agents’ welfare. However, it is crucial to avoid the misconception that the MP+LTV policy combination is unconditionally associated with welfare reduction. Such a conclusion would be erroneous and overly simplistic, potentially disregarding the nuanced circumstances under which central banks might consider adopting the MP+LTV approach.

Table 6: Relative welfare losses of various policy mixes under selective shocks

In Table 6, we conduct a fresh assessment of relative welfare losses, this time focusing on specific shocks while setting the variances of the other shocks to zero. In the simulation where only technology shock are considered, the outcomes align with those observed in Table 7. With the exception of the MP+LTV scenario, the other policy combinations demonstrate welfare improvements. When preference shocks come into play, a distinct trend emerges. In all four policy mixes, welfare reduction occurs, with each experiencing higher welfare losses compared to the baseline MP scenario. This holds true regardless of the relative weight assigned to λy. A similar pattern emerges when solely foreign-output shocks are included in the analysis.

Turning our attention to financial shocks, we observe anticipated results. The MP+CR, MP+RR, and MP+MPLB policy blends all contribute to welfare enhancements. Intriguingly, the MP+LTV combination continues to exhibit welfare reduction, even when the economy exclusively faces financial shocks. It’s important to note that in the context of the MP+LTV mix, the only financial shock at play is the bank balance sheet shock. This particular shock primarily and directly impacts credit supply. In this context, a counter-cyclical credit demand-oriented measure like an LTV regulation might paradoxically amplify the financial cycle, subsequently influencing the business cycle dynamics [21].

Let’s break down what each of these variables might represent:

1. MP (Monetary Policy):

Monetary policy refers to the actions taken by a country’s central bank to control and regulate the money supply and interest rates in order to achieve specific economic goals. The values in this column likely represent some measure or index related to the stance of monetary policy, such as interest rates or money supply levels.

2. MPLB (Monetary Policy Lending Benchmark):

MPLB could be a benchmark or reference rate used by the central bank to guide the lending rates set by financial institutions. This rate could be influenced by the central bank’s monetary policy decisions and could impact borrowing costs for various stakeholders.

3. CAR (Capital Adequacy Ratio):

The Capital Adequacy Ratio is a measure of a bank’s capital as a percentage of its risk-weighted assets. It assesses a bank’s ability to absorb losses and protect depositors and other stakeholders. The values in this column might indicate the level of capital adequacy within the financial system.

4. CC (Credit-to-Cap):

Credit-to-Cap refers to the ratio of credit (loans or borrowings) extended to the economy relative to its capacity (typically represented as GDP or another economic measure). This ratio could reflect the level of borrowing and lending activity within the economy.

5. LTV (Loan-to-Value):

Loan-to-Value is a ratio used in lending to assess the risk of a loan by comparing the amount of the loan to the appraised value of the asset being financed. It is often used in mortgage lending to determine the amount of the loan relative to the property’s value.

6. MIR (Mortgage Interest Rate):

The Mortgage Interest Rate represents the rate at which interest is charged on a mortgage loan. This rate can influence borrowing costs for homebuyers and impact the demand for real estate.

The values in the table likely represent numerical data for each of these variables over the specified time period. Analyzing these values and their trends can provide insights into the state of the economy, the effectiveness of monetary policy, the health of the banking sector, and the dynamics of credit and lending activity. It’s important to consider the context of the model and the specific assumptions and calculations used to generate these values in order to interpret their implications accurately.

The figure provides a visual representation of the modeled relationships between GDP, consumption, and investment. It may show how these variables respond to changes in economic conditions, policy actions, and external shocks. Peaks and troughs in the GDP, consumption, and investment curves may correspond to economic cycles, capturing periods of expansion and recession.

Changes in the slopes or trends of these curves could reveal the sensitivity of the economy to different factors. For example, a steep decline in investment during a certain period might indicate a reduction in business confidence. The DSGE model’s analysis helps to explain the underlying mechanisms driving the observed patterns in GDP, consumption, and investment. It provides insights into the dynamics of the economy and can guide policymakers in making informed decisions to promote economic stability and growth.

The figure depicting Indonesian GDP and investment analysis from 2009 to 2022 illustrates the economic trajectory and investment trends of Indonesia over this period. The GDP curve represents the country’s overall economic performance, displaying periods of growth, contraction, and shifts in economic cycles. Peaks and troughs in the GDP line indicate moments of economic expansion and recession, potentially influenced by external shocks such as the global financial crisis and the COVID-19 pandemic. Concurrently, the investment trend line showcases the country’s capital expenditure patterns, revealing periods of increased or reduced investment activity. Fluctuations in investment may reflect shifts in policy, economic confidence, and sectoral priorities. Analyzing these two curves together provides valuable insights into how investment decisions impact Indonesia’s GDP growth, highlighting the interplay between economic growth and capital allocation during this time span.

The analysis of Indonesian import and export ratios from 2009 Q2 to 2022 Q4 in MATLAB reveals the country’s trade dynamics over the specified period. By tracking the import-to-export ratio, the figure provides insights into Indonesia’s trade balance and its engagement with the global economy. A decreasing ratio might suggest a shift towards export-led growth or improved domestic production capabilities, potentially contributing to a more favorable trade balance. Conversely, an increasing ratio could indicate higher dependence on imports, impacting trade deficits. The fluctuating nature of the ratio can be influenced by factors like changes in global demand, exchange rates, and domestic economic policies. This analysis helps assess Indonesia’s trade competitiveness, economic openness, and external trade relationships within the context of evolving global economic conditions.

Figure 4 illustrates the analysis of key financial and macroeconomic indicators in Indonesia from 2009 to 2022, including the Deposit Rate, Non-performing Loan (NPL) Ratio, Bank Indonesia (BI) Rate, and Inflation Rate. This depiction provides valuable insights into the country’s monetary policy, financial health, and economic stability over the specified period. The Deposit Rate represents the interest rate paid by financial institutions on deposits, influencing savings behavior and overall liquidity in the economy. The NPL Ratio reflects the proportion of non-performing loans in the banking system, indicating the quality of bank assets and potential financial risks. The BI Rate, set by the central bank, plays a crucial role in influencing borrowing costs and economic activity. The Inflation Rate measures the general increase in prices, affecting purchasing power and economic stability. Analyzing these indicators collectively offers a comprehensive understanding of how Indonesia’s monetary policy decisions, financial soundness, and price stability efforts have shaped its economic landscape. Fluctuations and trends in these indicators can shed light on the effectiveness of policy measures, the resilience of the financial sector, and the impact of economic shocks during this time frame.

MATLAB Calculated Figure 5 presents a comprehensive analysis of key economic indicators by comparing US GDP, US Inflation, and the Indonesia London Interbank Offered Rate (LIBOR) from 2009 to 2022. This comparative view offers insights into the economic dynamics and monetary conditions of both the United States and Indonesia over the specified period.

1. US GDP: The figure likely displays the Gross Domestic Product (GDP) of the United States, reflecting the total value of goods and services produced within the country’s borders. The trend and fluctuations in the US GDP curve provide an overview of the US economy’s growth trajectory, capturing periods of expansion, recession, and overall economic performance.

2. US Inflation: This component likely represents the inflation rate in the United States, indicating the general increase in prices over time. The US Inflation curve showcases the changes in consumer prices, reflecting shifts in purchasing power, and can offer insights into the effectiveness of US monetary policies in maintaining price stability.

3. Indonesia LIBOR Rate: The London Interbank Offered Rate (LIBOR) for Indonesia signifies the average interest rate at which major international banks are willing to lend to one another. It serves as a benchmark for various financial instruments and reflects credit risk, liquidity conditions, and prevailing interest rate trends in Indonesia’s financial market.

The analysis of these indicators in conjunction enables a deeper understanding of economic interdependencies and cross-country influences. Fluctuations in US GDP and inflation may correlate with changes in Indonesia’s LIBOR rate, revealing potential impacts of global economic conditions on Indonesia’s financial markets. Moreover, analyzing how US and Indonesian economic variables interact can shed light on the transmission of monetary policies and external shocks between these two economies. This figure aids policymakers, analysts, and researchers in comprehending the broader economic context and identifying potential areas for policy adjustments or risk mitigation strategies.

Figure 6 presents a focused analysis of Indonesian Gross Domestic Product (GDP) and investment trends specifically for the years 2023 to 2024. This snapshot offers valuable insights into Indonesia’s short-term economic performance and investment patterns during this limited timeframe.

Indonesian GDP:

The plot of Indonesian GDP for the years 2023 to 2024 showcases the country’s economic output over this period. Changes in the GDP curve reflect shifts in economic activity, such as expansion, contraction, or stabilization. Rising trends may indicate economic growth and increased production of goods and services, while declining trends might suggest economic challenges or slowdowns. This analysis allows for a concentrated examination of Indonesia’s economic health and performance within this two-year span.

Investment Trends:

The accompanying investment analysis likely depicts the levels of investment within the Indonesian economy during 2023 to 2024. Investment includes spending on capital goods, such as infrastructure, technology, and equipment, which enhances productive capacity and contributes to economic growth. Fluctuations in investment may indicate shifts in business sentiment, regulatory changes, or sector-specific developments. Understanding these trends can offer insights into the country’s efforts to attract capital and foster economic expansion within the given time frame.

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By focusing on a shorter period, Figure 6 enables a more detailed exploration of the specific economic conditions, policy decisions, and external influences that shape Indonesia’s GDP and investment landscape. This snapshot serves as a valuable tool for policymakers, economists, and analysts to assess the immediate economic outlook, make informed decisions, and adapt strategies to ensure sustainable economic growth and stability during the years 2023 to 2024.

Figure 7 provides a targeted analysis of the Indonesian Import and Export ratio for the period spanning from 2023 to 2024. This figure sheds light on the country’s trade dynamics and its engagement with the global economy over this specific two-year timeframe. By examining the trend and fluctuations of the Import-to-Export ratio, the figure offers insights into Indonesia’s trade balance and its position as an importer and exporter of goods and services. A changing ratio may suggest shifts in trade patterns, trade policy, and international demand. An increasing ratio could indicate growing reliance on imports, while a decreasing ratio might imply a stronger emphasis on export-oriented strategies. This focused analysis allows policymakers, economists, and stakeholders to discern short-term trade trends and assess the country’s trade competitiveness and economic openness within the context of evolving global economic conditions.

Figure 8 presents a detailed analysis of key financial and macroeconomic indicators in Indonesia for the years 2023 to 2024, encompassing the Deposit Rate, Non-performing Loan (NPL) Ratio, Bank Indonesia (BI) Rate, and Inflation Rate. This figure provides an in-depth understanding of the country’s monetary policy, financial stability, and economic conditions within this specific two-year period.

Deposit Rate:

The curve likely portrays the Deposit Rate, which represents the interest rate paid by financial institutions to depositors. This indicator affects savings behavior, liquidity in the banking system, and overall financial activity. Changes in the Deposit Rate curve can reflect adjustments in lending and borrowing conditions, potentially influencing consumer spending and investment decisions.

Non-performing Loan (NPL) Ratio:

This element probably illustrates the Non-performing Loan Ratio, indicating the proportion of loans in the banking sector that are not being repaid as agreed. Fluctuations in the NPL Ratio curve offer insights into the health of the banking system, credit risk, and potential financial vulnerabilities. A rising NPL Ratio may suggest economic challenges, while a declining ratio may indicate improved loan quality.

Bank Indonesia (BI) Rate:

The BI Rate, set by the central bank, is depicted by its own curve. This rate influences borrowing costs for financial institutions and plays a critical role in shaping monetary policy. Changes in the BI Rate curve reflect the central bank’s decisions to stimulate or moderate economic activity and manage inflation. Adjustments in the BI Rate can impact lending, investment, and overall economic growth.

Inflation Rate:

The curve representing the Inflation Rate showcases changes in the general price level of goods and services in the Indonesian economy. Fluctuations in this curve indicate the rate at which prices are rising or falling. The Inflation Rate curve provides insights into purchasing power, the effectiveness of monetary policy, and overall economic stability.

Analyzing these indicators collectively allows for a comprehensive assessment of Indonesia’s financial and economic landscape during 2023 to 2024. It reveals how policy decisions, market conditions, and external influences contribute to shaping the country’s monetary policy stance, financial health, and inflationary pressures within this limited timeframe. This figure serves as a valuable tool for policymakers, financial institutions, and researchers to make informed decisions and anticipate potential challenges or opportunities arising in the short term.

Figure 9 offers a comparative analysis of crucial economic indicators between the United States and Indonesia for the years 2023 to 2024, focusing on US GDP, US Inflation, and the Indonesia London Interbank Offered Rate (LIBOR). This insightful visualization allows for an examination of how economic dynamics and monetary conditions in these two economies interact during this specific two-year period.

US GDP and US Inflation:

The figure likely displays the trajectories of US Gross Domestic Product (GDP) and US Inflation rates. The US GDP curve depicts the value of goods and services produced within the United States, reflecting the country’s economic performance. The US Inflation curve represents the general price level of consumer goods and services, indicating the rate of price increase. The interaction between these two curves can highlight the relationship between economic growth and inflationary pressures in the US during this period.

Indonesia LIBOR Rate:

This component likely showcases the Indonesia LIBOR rate, indicating the average interest rate at which international banks lend to one another in Indonesian currency. The LIBOR rate provides insights into the borrowing costs and credit conditions in Indonesia’s financial market. Fluctuations in the LIBOR rate curve can indicate changes in liquidity, market sentiment, and the overall economic environment.

By comparing these indicators, Figure 9 allows for the identification of potential correlations and causal relationships between US economic performance, inflation, and Indonesia’s LIBOR rate. Changes in US economic conditions could impact the global economic landscape, including Indonesia’s financial markets. This analysis is vital for policymakers, investors, and researchers to understand the spillover effects between economies and assess potential cross-country influences on monetary policy decisions and market trends during 2023 to 2024.

Figure 10 presents an analysis of the Monetary Policy Taylor Rule using three key variables: Policy Rate, Output Gap, and Inflation Rate, for the years 2009 to 2022. The Taylor Rule is an economic formula that provides a guideline for central banks to set their policy interest rates based on changes in these variables, aiming to achieve specific economic goals, such as stable growth and price stability.

Policy Rate:

This curve likely represents the actual policy interest rates set by the central bank. The Taylor Rule analysis involves comparing these policy rates with the recommendations provided by the Taylor Rule formula.

Output Gap:

The Output Gap measures the difference between an economy’s actual output and its potential output. Positive values suggest that the economy is operating above its potential, while negative values indicate underutilization of resources. The analysis of the Output Gap curve helps determine the current state of the economy relative to its potential and how this influences monetary policy decisions.

Inflation Rate:

The Inflation Rate represents the general increase in prices over time. The Taylor Rule takes into account the inflation rate to ensure that monetary policy appropriately responds to changes in price levels.

The figure likely illustrates how the central bank’s actual policy rates (Policy Rate curve) compare to the recommendations derived from the Taylor Rule, which is influenced by the Output Gap and Inflation Rate. Discrepancies between the two curves could indicate deviations from the Taylor Rule’s prescription for setting interest rates. For instance, if the actual policy rate is consistently below the Taylor Rule recommendation during periods of high inflation (positive Inflation Rate curve), it might suggest that the central bank is pursuing a more accommodative monetary policy.

This analysis provides insights into the central bank’s response to economic conditions and its efforts to achieve its dual mandate of promoting stable growth and controlling inflation. Understanding the interactions between the Policy Rate, Output Gap, and Inflation Rate in the context of the Taylor Rule can offer valuable information about the central bank’s policy stance, its assessment of the economy’s performance, and its commitment to maintaining macroeconomic stability throughout the specified period.

Figure 11 presents a comprehensive Monetary Policy analysis by comparing two key measures, the Monetary Policy (MP) rate and the Monetary Policy plus Monetary Policy Lending Benchmark (MP+MPLB) rate, from 2009 to 2022. This analysis offers insights into how the central bank’s policy rates have evolved over time, taking into account both the core monetary policy rate and the additional impact of the Monetary Policy Lending Benchmark. Comparing these rates elucidates the nuanced approach the central bank employs to influence borrowing costs, liquidity, and economic activity, providing a deeper understanding of the monetary strategies and decisions that have shaped the country’s financial landscape during this period.

Figure 12 conducts a comprehensive Monetary Policy analysis by juxtaposing three distinct measures: Monetary Policy combined with Capital Adequacy Ratio (MP + CAR), Monetary Policy plus Credit-to-Cap ratio (MP + CC), and Monetary Policy plus Loan-to-Value ratio (MP + LTV), spanning the years 2009 to 2022. This insightful comparison highlights the intricate interplay between monetary policy tools and financial regulations in shaping the country’s economic landscape. Examining these combined indicators provides a holistic view of how the central bank’s policy decisions interact with prudential measures, such as capital adequacy, credit exposure, and loan risk, reflecting the central bank’s multifaceted approach to promoting financial stability, ensuring sound lending practices, and maintaining appropriate monetary conditions throughout the analyzed timeframe.

Figure 13 offers a Monetary Policy analysis by combining the Monetary Policy rate (MP) with the Mortgage Interest Rate (MIR) from 2009 to 2022. This analysis provides insights into the relationship between the central bank’s policy decisions and the dynamics of mortgage borrowing costs. By examining how the combined MP + MIR rate fluctuates over time, the figure reveals the central bank’s efforts to influence monetary conditions, credit availability, and housing market activity. This comparison sheds light on how changes in the Monetary Policy rate interact with shifts in mortgage interest rates, impacting affordability for homebuyers, real estate investment, and overall economic stability. Analyzing this combined indicator contributes to a nuanced understanding of the central bank’s role in influencing both broader monetary conditions and specific sectors of the economy, such as the housing market, throughout the specified period.

Figure 14 illustrates the impulse responses of key economic variables—Output, Consumption, Inflation, and Policy Instrument—to a negative 1% preference shock. A preference shock typically represents a sudden change in consumer preferences or behavior that affects the economy. The figure visually demonstrates how these variables react to the shock over a certain time horizon.

Output and Consumption:

A negative preference shock might lead to a decrease in consumer demand and spending, causing a reduction in Output (economic activity) and subsequently impacting Consumption. These curves might exhibit a downward trend, indicating a short-term contraction in economic output and consumption levels.

Inflation:

The shock’s effect on Inflation depends on various factors. A decrease in consumer spending might lead to reduced demand-pull inflation. However, supply-side factors could counterbalance this effect. The Inflation curve might show a mixed response, reflecting the interplay of demand and supply dynamics.

Policy Instrument:

In response to the negative preference shock, policymakers may adjust a Policy Instrument (such as interest rates) to mitigate its impact. The figure likely displays how the central bank’s policy rate or another relevant instrument is altered over time to counteract the shock’s negative effects. An increase in the Policy Instrument could be observed as the central bank seeks to stimulate economic activity.

The impulse responses in Figure 14 provide insights into the short-term adjustments and interactions among these variables following the preference shock. Such analyses help economists and policymakers understand the potential outcomes of various shocks on the economy, guiding them in formulating appropriate responses to stabilize and support economic conditions.

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Figure 15 depicts the impulse responses of crucial economic indicators—Credit, Investment, Nominal Policy Rate, and Loans—to a negative 1% preference shock. This figure illustrates how these variables react to the shock’s impact over a specified time horizon. A negative preference shock can disrupt borrowing and spending patterns. The figure likely shows a decrease in Credit and Loans, reflecting reduced lending and borrowing activity due to weakened consumer preferences. Investment, a critical driver of economic growth, might also decline as businesses respond to decreased demand. The Nominal Policy Rate, a tool of monetary policy, could be adjusted by policymakers to mitigate the shock’s effects, potentially depicted by an increase as the central bank seeks to stimulate economic activity. Overall, Figure 15 provides valuable insights into the interconnected adjustments and responses of these economic indicators following a negative preference shock, aiding policymakers and economists in understanding the potential implications for credit markets, investment, and monetary policy.

Figure 16 illustrates the impulse responses of key economic variables—Output, Consumption, Inflation, and Policy Instrument—to a negative 1% technology shock. Such a shock represents a sudden adverse change in technological advancements that affects the economy. The figure provides a visual representation of how these variables respond to the shock over a specific period. A negative technology shock could lead to a decrease in productivity and innovation, which might result in reduced economic activity and output (Output). Consumer sentiment could be impacted, leading to decreased spending and Consumption. The Inflation curve might exhibit mixed effects, as reduced productivity could counteract potential demand-pull inflation. Policymakers may respond to the shock by adjusting the Policy Instrument (e.g., interest rates) to support economic activity, which might be observed through changes in the Policy Instrument curve. Overall, Figure 16 offers insights into the dynamic interactions and adjustments of these variables in response to a negative technology shock, aiding in the understanding of the potential economic repercussions and guiding policymakers in formulating effective strategies to manage such shocks.

Figure 17 portrays the impulse responses of significant economic indicators—Credit, Investment, Nominal Policy Rate, and Loans—to a negative 1% technology shock. This figure depicts how these variables react to the shock’s impact over a specified time frame. A negative technology shock can disrupt innovation and technological progress, potentially leading to a contraction in Credit and Loans, as borrowing and lending activity declines due to decreased economic activity and uncertainty. Investment, a critical driver of growth, might also decrease as businesses react to reduced technological opportunities. Policymakers may adjust the Nominal Policy Rate, depicted by fluctuations in its curve, to counteract the shock’s effects. An increase in the Nominal Policy Rate could be observed as the central bank seeks to stimulate economic activity in response to the technology shock. In summary, Figure 17 provides insights into the intricate responses and adjustments of these economic indicators to a negative technology shock, offering valuable information for policymakers and economists to understand the potential repercussions for credit markets, investment, and monetary policy.

Figure 18 presents the results of a Bayesian estimation analysis covering the period from 2019 Q4 to 2022 Q4. This figure illustrates the smoothed or estimated values of key variables, along with the observables related to COVID-19 shocks. Bayesian estimation is a statistical method used to estimate unknown parameters in a model while taking into account prior information and observed data.

Smoothed (Estimated) Variables:

The figure likely displays the estimated values of various economic variables that have been “smoothed” over the specified period. Smoothing involves estimating variables by considering the entire dataset, which can help in identifying underlying trends and patterns while filtering out short-term noise. These smoothed variables could include GDP, inflation, interest rates, unemployment, or other relevant economic indicators.

COVID-19 Shocks Observables:

In the context of the COVID-19 pandemic, the figure might also highlight observables related to the shocks caused by the pandemic. This could include variables that capture the impact of the pandemic on the economy, such as changes in consumer spending patterns, shifts in supply and demand, government policy responses, and more. These observables provide insight into how the economy has been influenced by the unprecedented events of the pandemic.

The figure allows researchers, policymakers, and analysts to visualize the estimated trajectory of various economic variables while accounting for the effects of COVID-19 shocks. It provides a comprehensive view of how the economy evolved during the pandemic period, capturing both the smoothed underlying trends and the short-term deviations caused by the pandemic-related shocks. This information is invaluable for understanding the economic dynamics during a period of significant disruption and for formulating appropriate policy responses.

Figure 19 & 20 presents a detailed analysis of policy-mix counterfactuals along with the effects of COVID-19 shocks on various monetary policy and regulatory instruments. The figure focuses on five key measures: Monetary Policy (MP), Capital Requirement (CR), Reserve Requirement (RR), Monetary Policy Lending Benchmark (MPLB), and Loan-to-Value Ratio (LTV). These measures reflect different tools used by policymakers and regulators to influence the economy, particularly in the context of the COVID-19 pandemic.

Monetary Policy (MP):

This refers to the central bank’s decisions regarding interest rates, money supply, and other tools to manage economic growth and inflation. The figure likely assesses how different policy-mix scenarios, or combinations of these tools, could impact the economy under the influence of COVID-19 shocks. For instance, it might explore how adjustments in interest rates interact with other policies to mitigate the pandemic’s effects.

Capital Requirement (CR):

This pertains to the amount of capital that banks are required to hold in relation to their total assets. The figure may analyze the impact of varying capital requirements on financial stability and lending capacity during the pandemic. It could show how changes in CR affect banks’ ability to absorb losses and provide credit to support economic recovery.

Reserve Requirement (RR):

This involves the mandatory reserves that banks must hold with the central bank. The figure could illustrate how adjustments in RR influence banks’ liquidity and ability to lend, thereby affecting monetary conditions and credit availability in response to the COVID-19 shocks.

Monetary Policy Lending Benchmark (MPLB):

This refers to the interest rate that central banks charge on loans extended to financial institutions. The figure might explore how changes in MPLB impact banks’ borrowing costs, liquidity, and lending behavior, contributing to the overall monetary policy stance during pandemic-related disruptions.

Loan-to-Value Ratio (LTV):

This represents the ratio of a loan amount to the appraised value of the asset being financed. The figure could examine how adjustments in LTV requirements affect borrowing and lending decisions, particularly in sectors such as real estate, and how these adjustments interact with other policy measures in response to COVID-19 shocks.

Overall, Figure 19 & 20 provides a comprehensive analysis of policy-mix scenarios and their interactions with COVID-19 shocks. By considering various combinations of monetary policy and regulatory tools, the figure offers insights into how policymakers can address economic challenges and enhance resilience during times of crisis. It provides a nuanced understanding of the potential outcomes of different policy responses, aiding in the formulation of effective strategies to support economic stability and recovery in the face of the COVID-19 pandemic.

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Conclusion and Recommendations

The posterior mean forecast error variance decompositions during the COVID-19 period reveal the substantial impact of technology, preference, and cost-push shocks on output growth fluctuations in Indonesia. Moreover, these shocks, along with financial shocks, have significant implications for inflation, the monetary policy interest rate, and credit growth. Understanding the dynamics of these macro shocks is crucial for policymakers to develop effective strategies and policies that can mitigate economic volatility, promote stable growth, and safeguard financial stability in Indonesia.

This study successfully builds and estimates a small open-economy DSGE model for Indonesia with banking and financial frictions. Through the model, we investigate the relative effectiveness of different monetary-macroprudential policy mixes in responding to COVID-19 pandemic shocks, identified as a combination of adverse supply-side (technology) and demand-side (preference and foreign-output) shocks. Our findings highlight the significance of technology shocks as the primary drivers of business-cycle fluctuations in Indonesia, both during the entire inflation-targeting period and the ongoing COVID-19 period. Consequently, a policy mix combining monetary policy with countercyclical macroprudential policies directly impacting credit supply, such as capital requirement, reserve requirement ratio, or liquidity coverage ratio regulation, would be more beneficial and improve welfare during the pandemic-induced economic contraction.

Furthermore, we find that countercyclical credit demand-channel macroprudential policy, such as loan-to-value ratio regulation, may exacerbate aggregate fluctuations and lead to welfare reductions. Our results align with related literature treating COVID-19 shocks as a combination of supply-side and demand-side shocks, with technology shocks being dominant in driving economic fluctuations during the pandemic, similar to findings for the Euro area.

Lastly, our study demonstrates that easing macroprudential policy involving credit supply-channel regulations is welfare-improving during the COVID-19 period, complementing the central bank’s accommodative monetary policy stance in response to the economic contraction. However, credit demand-channel regulations, particularly loan-to-value ratio regulations, are found to be welfare-reducing and exacerbate business-cycle fluctuations. This finding is attributed to the prevalence of technology shocks over other COVID-19 shocks and underscores the importance of considering the specific shock composition when designing effective macroprudential policies during times of economic crisis. Overall, our study provides valuable insights into the optimal policy mix for navigating pandemic-induced economic contractions in Indonesia and underscores the significance of technology shocks in shaping economic outcomes.

Here are some recommendations to guide your approach:

1. Theoretical Framework and Model Specification:
   1. Begin by defining the key features of the model, including the economic agents, markets, and frictions you want to capture. Specify the equations that govern agents’ behavior, firms’ production, consumption, investment, and the financial sector.
   2. Incorporate banking and financial frictions relevant to Indonesia’s context during the COVID-19 pandemic, such as credit constraints, loan defaults, liquidity shocks, and their impact on the banking sector’s operations.
   3. Integrate the small open-economy framework to capture interactions between the Indonesian economy and the rest of the world, including trade, capital flows, and exchange rates.
2. Data Collection and Preprocessing:
   1. Gather relevant macroeconomic data for Indonesia, including GDP, inflation, interest rates, trade data, and financial variables. Ensure that the data covers the period of interest, specifically the COVID-19 pandemic period.
   2. Preprocess and transform the data as needed, ensuring consistency and compatibility with the model’s theoretical framework.
3. Model Calibration and Parameterization:
   1. Estimate model parameters using econometric techniques or relevant literature. Pay particular attention to the parameters related to financial frictions, banking sector behavior, and COVID-19 shocks.
   2. Validate the model’s parameter estimates against empirical evidence and economic intuition. Use available information to guide your choices, especially regarding parameter values related to financial disruptions caused by the pandemic.
4. Model Solution and Simulation:
   1. Utilize MATLAB’s DSGE modeling tools to solve the model and generate simulated time series data for key variables under various scenarios.
   2. Incorporate stochastic shocks, including COVID-19-related shocks, into the simulation to assess the model’s response to different economic conditions.
5. Estimation and Model Comparison:
   1. Implement Bayesian estimation techniques to estimate the model’s parameters. MATLAB provides Bayesian estimation functions that can handle DSGE models.
   2. Compare the estimated model’s simulated outcomes with actual data, using statistical methods like likelihood-based comparisons and impulse response functions.
6. Model Validation and Sensitivity Analysis:
   1. Validate the model by examining its ability to replicate key empirical moments, such as moments of macroeconomic variables and financial indicators.
   2. Conduct sensitivity analysis to assess the robustness of the model’s results to changes in parameter values, assumptions, and shock magnitudes.
7. Policy Analysis and Interpretation:
   1. Once the model is well-calibrated and estimated, use it to conduct policy analysis. Simulate the effects of different policy responses to COVID-19 shocks, such as monetary policy interventions, fiscal measures, and financial sector support.
   2. Interpret the results in the context of Indonesia’s economic situation, considering implications for inflation, GDP growth, financial stability, and other relevant indicators.
8. Documentation and Communication:
   1. Document your model’s equations, assumptions, parameter estimates, and results thoroughly. This documentation will be crucial for transparency and replicability.
   2. Clearly present your findings and policy recommendations in a structured and accessible manner, taking into account the audience’s level of expertise.

Remember that building and estimating a DSGE model is a sophisticated task that requires a deep understanding of both economic theory and empirical methods. It’s recommended to seek guidance from experienced economists or researchers and leverage available resources, such as academic papers, textbooks, and MATLAB’s DSGE modeling functionalities.

References

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Keywords: DSGE Model, Indonesia, Banking, Financial Frictions, MATLAB

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