Effect of Yield Spreads (State Bonds) on Economic Growth Performance in Indonesia
Faculty of Economics and Business, Universitas Trisakti, Jakarta 11440, Indonesia
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2023, 16(3), 175; https://doi.org/10.3390/jrfm16030175
Submission received: 26 December 2022
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Revised: 27 February 2023
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Accepted: 1 March 2023
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Published: 5 March 2023
(This article belongs to the Special Issue Financial Assets as Profit-Makers in Inflationary Periods)
Abstract
:This research analyzes the effect of the government bond yield curve spread on economic growth performance in Indonesia using the indicators of exchange rate, inflation, BI rate, foreign investment, portfolio investment, current account, and government accounts. Furthermore, it aims to prove the accuracy of the vector autoregression (VAR) or vector autoregression model in predicting economic growth from Q1 2010 to Q3 2020. The results showed that the yield curve spread has a significant effect on economic growth. Meanwhile, the exchange rate, inflation, and the BI rate have a negative effect on economic growth. Capital inflows such as foreign direct investment, portfolio investment, as well as the current account balance and government balance have a positive effect on economic growth. These results are useful to government policymakers, fund managers, and investors, as they provide further evidence of the potential use of yield curves as an indicator of future economic activity.
1. Introduction
The total population of Southeast Asia is 675 million people (in 2022) or 8.4% of the world’s population (The World Bank 2022a). Indonesia is the fourth largest country by population in the world with a diversity of culture, religion, ethnicity, and historical heritage. The largest economy in Southeast Asia, Indonesia, has charted impressive economic growth since overcoming the Asian financial crisis of the late 1990s (The World Bank 2022b).
Indonesia is also respected among ASEAN member countries and the E7 countries including China, India, Turkey, Brazil, and Mexico. A report published by PwC shows emerging markets will dominate the world’s top 10 economies (GDP at PPPs,) where Indonesia ranked eighth after China, US, India, Japan, Germany, Russia, Brazil, Indonesia, UK, and France in 2016, and it is predicted to be in fourth position after China, India, and the US in 2050 (Pricewaterhouse Coopers 2017). The Indonesian economy remains resilient with a promising outlook. Bank Indonesia projects solid national economic growth in Indonesia in 2023 in region of 4.5–5.3%. Consumer price index (CPI) inflation is projected to track a downward trend and return to the 3.0 ± 1% target in 2023, with a current account projected in the range of a 0.4% surplus–0.4% deficit of GDP in 2023, while the capital and financial account surplus will be supported by foreign direct investment (FDI) and portfolio investment (Bank Indonesia 2022).
The forecasting of short- and long-term economic conditions is carried out by economists in both business and the government. In the business sector, economists forecast conditions to enable companies to plan and respond to future changes in the economy. Mankiw (2022) stated that forecasting is carried out for two reasons: First, the economic environment affects the government in terms of the realization of tax revenues that can be used to finance development and the budget policies enacted. Second, the government can boost the economy using fiscal and monetary policies. Therefore, forecasting and understanding the economic cycle constitute the main information sources for policy planners.
Economists observe leading indicators to forecast conditions, specifically variables that tend to fluctuate ahead of the economy as a whole. Forecasts may differ because economists hold varying opinions about which major indicators are reliable. The most significant indicators used as a tool for predicting the economy involve the difference in interest rates on risk-free investment instruments or government bonds with long-term and short-term maturities. Estrella and Mishkin (1996) used the difference in interest rates on bonds with a tenor of 10 years and bonds with a tenor of 3 months. This difference was referred to as the slope of the yield curve, which reflects the market expectations of future interest rates, which in turn, reflect economic conditions. A large yield spread indicates expectations of an increase in interest rates which usually occurs when economic activity increases. The relationship between interest rates on bonds with different maturities is called the Term Structure of Interest Rate. Based on empirical data, there are three facts in the yield curve:
- Interest rates on bonds with different maturity dates eventually become contiguous.
- When the interest rate is short, the yield curve tends to be upward-sloping.
- The yield curve is generally always upward-sloping.
Yield curves on long- and short-term government bonds are a valuable forecasting tool and indicator in predicting economic growth.
2. Materials and Methods
2.1. Economic Growth
Understanding the nature of economic growth and identifying its components are two of the key problems in economics (Chlebisz and Mierzejewski 2020). Generally, economic growth is defined as an increase in the ability of an economy to produce goods and services (Gomez-Bengoechea and Arahuetes 2019; McGough and Berry 2020; Olivares Rios et al. 2019; Papadamou et al. 2020; Thazhugal Govindan Nair 2020). This is one of the important indicators in analyzing the development that occurs in a country (Perkins 2021). Economic growth shows the extent to which economic activity will generate additional income for a country in a certain period.
Indonesia adheres to an open economic system where financial markets and goods markets are closely linked. Basri (2016) applied the Keynesian theory to describe the relationship in an open market economy using the national income equation (GDP), namely:
where:
Y = C + I + G + X − M
- Y = National income (GDP);
- C = Consumption;
- I = Investment;
- G = Government spending;
- X = Exports of goods and services;
- M = Imports of goods and services.
2.2. Inflation
Inflation is a general and continuous increase in the price of goods (Amrial et al. 2019; Wahyudi et al. 2021; Warjiyo and Juhro 2019a, 2019b). From this definition, 3 components must be fulfilled to highlight the occurrence of inflation: price increases, general in nature, and ongoing (Wimanda 2014).
2.3. Exchange Rates
The exchange rate between 2 countries is the price level agreed upon by residents of both countries to trade with each other (Mankiw 2022; Wanasilp 2021; Wimanda 2014). Koh et al. (2014) defined it as the amount of a country’s currency that can be exchanged per unit of another country’s currency, or the price of 1 currency against that of another country.
2.4. BI Rates
The BI rate is an interest rate with a 1-month tenor announced by Bank Indonesia periodically which signifies the stance of monetary policy (Siamat 2005). The inflation targeting framework defined the BI rate as the interest rate of Bank Indonesia’s signaling instrument set at the quarterly RDG (Board of Governors meeting) to take effect during the current quarter (quarter 1) unless otherwise stipulated by the monthly RDG in the same quarter.
2.5. Foreign Direct Investment (FDI)
Foreign investment in Indonesia is divided into 3 as follows: portfolio, foreign direct investment (FDI), and export credit. Foreign direct investment (FDI) involves investors directly in the business operations being carried out (Bruhn et al. 2020; Gopalan et al. 2016; Oxford Analytica 2019; Paweenawat 2019; Stephenson et al. 2022). Therefore, business dynamics related to company objectives cannot be separated from interested parties/foreign investors.
2.6. Investment Portfolio
According to the Capital Market and Financial Institution Supervisory Agency, portfolio investment, also known as indirect investment, is made through the purchase of bonds, state treasury documents, shares issued by companies, and deposits. The investment is made by buying shares of national companies through the capital market, specifically stock exchanges.
2.7. Current Account Balance
The balance of payment (BOP) records all transactions of a country with other countries in a certain period, usually 1 year. The main components of the BOP include:
- 1.
- Current Account
This comprises transactions related to the export and import of goods and services. The current account helps to determine whether a country’s trade balance is in a surplus or a deficit.
- 2.
- Capital Account
It includes capital inflows as inflows and outflows. The inflow can include official or other forms of capital.
- 3.
- Errors and Omissions
Errors and omissions are faults that have not been taken into account or ignored. According to the calculation model formulated by the International Monetary Fund, it is a balancing account that signifies the meaning of a deficit or surplus in the balance of payments per year of the record.
- 4.
- Reserve
This is presented by the IMF in the development of foreign exchange reserves from the year before recording to the time of recording, commonly expressed as a monetary movement.
2.8. Government Balance
According to (Basri 2016), the government balance is state income after deducting state spending/expenses. It is also referred to as the State Revenue and Expenditure Budget commonly known as APBN (Anggaran Pendapatan Belanja Negara in Indonesia term). The annual financial plan of the Indonesian state government is approved by the parliament (Ministry of Finance 2022). Furthermore, the government balance contains a systematic and detailed list of plans in state revenues and expenditures for 1 fiscal year (January 1–December 31). The annual changes and accountability of the State Revenue and Expenditure Budget are stipulated by law.
Below is the conceptual framework in Figure 1.
Hypothesis Formulation
Based on the literature above, the following hypotheses are made:
H1.
There is an effect of the Yield Curve Spread on Economic Growth in the long term.
H2.
There is an effect of the Exchange Rate, Inflation, BI Rate, Foreign Direct Investment, Current Account Balance, and Government Balance on long-term Economic Growth.
This research aims to predict Indonesia’s economic growth by using proxies and other financial indicators. The main variable applied was the yield curve spread to predict GDP change. The method involved the use of a risk-free investment instrument in the form of government bonds. Furthermore, it followed the spread between the yield curve of bonds with a long maturity period of 10-year tenor and similar instruments with a short maturity period of 5 years. Other variables included exchange rate, inflation, BI rate, foreign direct investment, investment portfolio, current account balance, and government balance which are significant economic indicators. Moreover, this research used the hypothesis testing method to analyze the factors that affect Indonesia’s economic growth with secondary data. The tool used was the ordinary least square (OLS), and the software used for this analysis is Eviews 8.0.
The nondirectional hypothesis was used to test the positive or negative direction of the relationship. The correlational investigation was used to establish a causal relationship through regression analysis (Sekaran and Bougie 2016). The data used includes national economic data such as yield curve spread, economic growth, exchange rate, inflation, BI rate, foreign direct investment, portfolio investment, current account balance, and government balance. These constitute the quarterly time series economic data indicators, namely from quarter I—2010 to quarter III—2020.
The analytical method used in this research was vector autoregression (VAR) when the data used were stationary and not cointegrated, followed by the vector error correction model (VECM) method when the data used were not stationary and cointegrated.
3. Results
Cointegration relationships were found in nine research variables; therefore, the next step was to construct the VECM model. The significance of the lag of a variable to other endogenous variables can be evaluated using the absolute value of the t-statistic (2.021) with a confidence level of 0.05 and a degree of freedom at 43. The VECM estimation results showed in Table 1.
Granger Causality Results
Based on the estimation results of the VAR equation above, only the model or pattern of the relationship between two variables was determined. Furthermore, the equation highlighted the relationship of one variable with another but did not signify the effect of this relationship with other variables. This necessitated causality testing which was carried out using the Granger causality test approach. The results of this test are shown below in the Table 2.
The Granger causality test method showed that there was a unidirectional relationship between the (1) yield curve spread (SPREAD), economic growth (GDP), and foreign direct investment (FDI); (2) the exchange rate (KURS) and economic growth (GDP); (3) between portfolio investment (INVST) and the yield curve spread (SPREAD); (4) the current account (CA), the exchange rate (KURS), and portfolio investment (INVST); (5) foreign direct investment (FDI) on the exchange rate (KURS) and current account (CA); (6) the BI rate and inflation, the current account (CA), and foreign direct investment (FDI); (7) inflation and the current account (CA); as well as (8) a unidirectional relationship between the government balance (APBN) and foreign direct investment (FDI).
There was also a two-way relationship between the current account (CA) and economic growth (GDP). Therefore, the movement of the CA enhanced the movement of GDP and vice versa. There was also a two-way relationship between portfolio investment (INVST) and the exchange rate (KURS), as well as between portfolio investment (INVST) and foreign direct investment (FDI). A two-way relationship can cause a multiplier effect on changes in one of these variables. The relationship patterns are described below in the Figure 2.
The dynamic behavior of the VAR model can be seen through the response to the shock of a specific variable and other endogenous variables. In this model, the response of changes in each variable in the presence of new information is measured with a 1-standard deviation. The horizontal axis represents the period in the next day after the shock occurs, while the vertical axis denotes the response value. Fundamentally, this analysis showed the positive or negative response of a variable to other variables. The response in the short term is usually quite significant and tends to change. Furthermore, in the long term, the response tends to be consistent and keeps getting smaller. The impulse response function provides an overview of the response of a variable in the future if there is a disturbance in another variable.
The following Table 3 presents the variance decomposition for the next 24 periods for each variable.
4. Discussion
Table 1 showed that the foreign direct investment or FDI variable had a significant positive effect on economic growth or GDP with a t-statistic value of −9.10930. However, from the long-term equation, it was observed that a 1% change in foreign direct investment (FDI) increased the GDP by 0.0014%. The current account or CA had a significant positive effect on GDP with a t-statistic value of −4.29444. Meanwhile, from the long-term equation, a change of 1% CA was observed to increase the GDP by 0.083%. The inflation variable had a significant negative effect on GDP with a t-statistic value of 4.07643. From the long-term equation, a 1% change in inflation reduced the GDP by 0.42%. The portfolio investment (INVST) variable had a significant negative effect on the GDP with a t-statistic value of 8.31748. From the long-term equation, a 1% change in portfolio investment (INVST) reduced the GDP by 0.000591%. Moreover, the BI rate variable (BIRATE) had a significant positive effect on the GDP with a t-statistic value of −4.55054. From the long-term equation, a 1% change in BIRATE increased the GDP by 1.12%. Furthermore, the government balance (APBN) variable had a significant positive effect on the GDP with a t-statistic value of −6.20292. From the long-term equation, a change of 1% in the government balance (APBN) increased the GDP by 5.29 × 10−5%. Additionally, the exchange rate (KURS) variable had a significant positive effect on the GDP with a t-statistic value of −3.37351. From the long-term equation, a 1% change in the exchange rate (KURS) increased the GDP by 0.000613%. The yield curve spread (SPREAD) variable had a significant positive effect on the GDP with a t-statistic value of −2.16091. From the long-term equation, a change of 1% in the exchange rate (KURS) increased the GDP by 1.72%.
Previous research has rarely analyzed this equation due to its similarity with the VAR parameter analysis, where it is difficult to determine reasons in financial science that sufficiently explain the relationship of all the lag variables. In terms of VAR, this is referred to as a theory because this structure is used to obtain the Γ matrix arrangement which is useful for further analytical processes such as Granger causality, the impulse response function, and variance decomposition. In this research, the main focus is the analysis of the impulse response function and variance decomposition.
Based on the table above, the analysis of variance decomposition showed that the forecast error variance of the GDP in the first period was determined by the GDP itself, namely 100%. Meanwhile, the variable contributions of exchange rate (KURS), inflation, BI rate, yield curve spread (SPREAD), foreign direct investment (FDI), portfolio investment (INVST), government balance (APBN), and current account (CA) were unable to explain the variability in the GDP at 0%.
In the second period, the GDP was determined by itself and began to decline compared to the first period at 83.80%. Meanwhile, the others were influenced by variables of foreign direct investment (FDI) at 9.35% and yield curve spread (SPREAD) at 5.42%. The GDP continued to decline until the 24th period and was recorded at 28.94%. Meanwhile, the others at the end of the period were affected by the variables of exchange rate (KURS), inflation, current account (CA), and foreign direct investment (FDI) of 35.14%, 9.36%, 7.36%, and 7.01%, respectively.
Considering the results of the variance decomposition test, the contribution made by the GDP variable was also affected by the yield curve spread (SPREAD), exchange rate (KURS), inflation, BI rate, foreign direct investment (FDI), portfolio investment (INVST), current account balance (CA), and government balance (APBN). Therefore, the sole contribution of the GDP variable became smaller with time.
5. Conclusions
This research showed that economic growth or GDP is affected by several variables including the yield curve spread of long-tenor government bonds of 10 years and short-tenor bonds of 5 years. This variable is the main signal in predicting economic growth. When the yield curve spread decreases, economic growth will also decrease in the coming periods. Conversely, if the yield curve spread increases, economic growth will increase in the coming periods.
Furthermore, the exchange rate variable was observed to have a negative effect on economic growth. When the rupiah exchange rate increases due to depreciation, then economic growth will decrease. This is related to international trade where the price of goods exported from Indonesia will be relatively cheaper in the US, which tends to increase exports. Conversely, the price of goods from the US will be relatively expensive, and imports tend to decrease, thereby affecting Indonesia’s economic growth.
The inflation variable was shown to negatively affect economic growth. This occurs when inflation is categorized as high, namely above 10%, which causes a decrease in economic growth. The BI rate variable also has a negative effect on economic growth, and its regulation will significantly influence banking interest rates. Additionally, by lowering the BI rate, bank lending rates also decrease, thereby boosting real sector liquidity and driving economic growth.
The foreign direct investment and portfolio investment variables constitute the capital inflows that positively affect Indonesia’s economic growth. In the long term, foreign direct investment is beneficial for development in Indonesia. Although portfolio investment positively affects GDP, this is an indirect investment where investors can withdraw their funds at any time if unstable political or economic conditions occur. Therefore, the government is expected to create a stable economic and political climate to increase investor confidence.
Based on the international trade aspect, the current account balance variable regulates the export and import of goods and services. When this variable is in a surplus, economic growth increases. Conversely, if the current account balance is in a deficit, economic growth declines. The surplus condition indicates that the level of exports is higher than imports, which strengthens the rupiah exchange rate for exported goods.
Finally, the government balance variable was shown to have a positive effect on economic growth. This is contrary to previous research which suggested that it had a negative effect. The deficit is used to accelerate economic growth, specifically by accelerating infrastructure development which requires large funding. Furthermore, government balance in Indonesia is channeled more towards consumption, namely, fuel subsidies in quite large quantities since 2005. These allocated funds for fuel subsidies can be channeled for infrastructural development in Indonesia.
From the above conclusions, the results of this research should be used by government policymakers to determine both monetary and fiscal policies to ensure good economic growth and stability in Indonesia. They can also be useful for investment managers in determining investment policy steps, as well as providing macro views which affect investment allocations accurately.
This research only examined the response of economic growth to the variables of exchange rate, inflation, BI rate, foreign direct investment, portfolio investment, current account balance, and government balance with the vector autoregression (VAR) analysis tool. It is recommended that further research analyzes the responses of other variables with various analytical methods to determine their effects and relationships.
Author Contributions
Main conceptualization and idea, K.C. and D.Y.S.; methodology, K.C. and D.Y.S.; formal analysis, W.R.S.; resources, W.R.S.; data curation, M.R.A.; writing—original draft preparation, K.C.; writing—review and editing, W.R.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research and the APC were funded by the Faculty of Economics and Business, Universitas Trisakti, grant number 45/Ak/3/FEB/VI/2022.
Institutional Review Board Statement
The study was conducted according to the guidelines of the Faculty Featured Programs or Program Unggulan Fakultas (PUF) in Indonesia term, reviewed by the Faculty and the University and approved by the Research Institutions and Community Service or Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPKM), Faculty of Economics and Business, Universitas Trisakti in Indonesia term with the number 0043/PUF/FEB/2021-2022.
Informed Consent Statement
Not applicable.
Acknowledgments
The author would like to thank the Data Processing and Statistics Institute or Lembaga Pengolahan Data dan Statistik (LPDS), Faculty of Economics and Business, Universitas Trisakti in Indonesia term which contributed to data collection and processing. The author also would like to thank the professional proofreading company, Native-Proofreading, managed by PT. Teknologi Translasi Utama can be visited through https://native-proofreading.com/ accessed on 5 December 2022 which contributed to translating this paper.
Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1.
Conceptual Framework.
Figure 2.
Relationship pattern of Granger causality test results.
Table 1.
VECM Estimation Results.
| Variable | Coefficient | t-Statistics | Description |
|---|---|---|---|
| Long-term | |||
| GDP (−1) | 1.000000 | ||
| FDI (−1) | −0.001444 | −9.10930 | Significant |
| CA (−1) | −0.083343 | −4.29444 | Significant |
| INFLATION (−1) | 0.420471 | 4.07643 | Significant |
| INVST (−1) | 0.000591 | 8.31748 | Significant |
| BIRATE (−1) | −1.123377 | −4.55054 | Significant |
| APBN (−1) | −5.29 × 10−5 | −6.20292 | Significant |
| KURS (−1) | −0.000613 | −3.37351 | Significant |
| SPREAD (−1) | −1.719830 | −2.16091 | Significant |
| C | 9.394046 |
Source: Data processed (2022).
Table 2.
Granger Causality Test Results.
| Ho | F-Statistic | Probability |
|---|---|---|
| SPREAD does not Granger Cause GDP | 2.57516 | 0.08871 |
| KURS does not Granger Cause GDP | 5.62683 | 0.00703 |
| CA does not Granger Cause GDP | 3.07390 | 0.05730 |
| GDP does not Granger Cause CA | 4.85138 | 0.01299 |
| SPREAD does not Granger Cause FDI | 2.57069 | 0.08906 |
| INVST does not Granger Cause SPREAD | 4.01232 | 0.02582 |
| CA does not Granger Cause KURS | 3.76796 | 0.03168 |
| FDI does not Granger Cause KURS | 2.52799 | 0.09250 |
| INVST does not Granger Cause KURS | 5.42492 | 0.00823 |
| KURS does not Granger Cause INVST | 5.01200 | 0.01142 |
| BIRATE does not Granger Cause INFLATION | 10.49320 | 0.00022 |
| INFLATION does not Granger Cause CA | 2.85121 | 0.06957 |
| BIRATE does not Granger Cause CA | 3.31178 | 0.04668 |
| BIRATE does not Granger Cause FDI | 3.91000 | 0.02812 |
| APBN does not Granger Cause FDI | 2.50067 | 0.09477 |
| FDI does not Granger Cause CA | 2.79530 | 0.07306 |
| CA does not Granger Cause INVST | 4.92615 | 0.01223 |
| INVST does not Granger Cause FDI | 3.79363 | 0.03100 |
| FDI does not Granger Cause INVST | 4.61682 | 0.01570 |
Source: Data processed (2022).
Table 3.
Variance Decomposition Test Results of GDP.
| Period | S.E | GDP | KURS | INFLATION | BIRATE | SPREAD | FDI | INVST | APBN | CA |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.337 | 100.0 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| 2 | 0.426 | 83.80 | 5.247 | 1.030 | 0.014 | 5.428 | 9.350 | 0.518 | 0.268 | 4.720 |
| 3 | 0.620 | 53.09 | 33.35 | 0.615 | 0.712 | 2.738 | 0.644 | 0.252 | 1.381 | 7.200 |
| 4 | 0.743 | 35.68 | 44.93 | 2.193 | 1.178 | 2.036 | 0.457 | 0.196 | 1.885 | 7.440 |
| 5 | 0.813 | 35.03 | 48.63 | 4.110 | 1.674 | 1.934 | 0.406 | 0.164 | 1.723 | 6.336 |
| 6 | 0.834 | 33.49 | 47.88 | 4.778 | 2.553 | 1.898 | 1.041 | 0.162 | 2.036 | 6.155 |
| 7 | 0.846 | 32.60 | 46.53 | 4.856 | 3.823 | 1.882 | 1.496 | 0.212 | 1.977 | 6.616 |
| 8 | 0.860 | 32.94 | 45.35 | 4.713 | 4.018 | 1.989 | 2.352 | 0.221 | 1.949 | 6.474 |
| 9 | 0.879 | 33.64 | 44.33 | 5.209 | 3.909 | 2.155 | 2.371 | 0.287 | 1.867 | 6.232 |
| 10 | 0.911 | 33.81 | 41.89 | 6.337 | 3.644 | 2.500 | 2.472 | 0.752 | 2.143 | 6.447 |
| 11 | 0.942 | 33.24 | 39.84 | 7.915 | 3.427 | 2.895 | 2.309 | 1.315 | 2.334 | 6.724 |
| 12 | 0.965 | 32.87 | 38.26 | 8.722 | 3.309 | 3.341 | 2.256 | 1.762 | 2.384 | 7.084 |
| 13 | 0.980 | 32.27 | 37.33 | 9.227 | 3.227 | 3.481 | 2.722 | 1.845 | 2.374 | 7.512 |
| 14 | 0.994 | 31.70 | 36.56 | 9.488 | 3.145 | 3.476 | 3.621 | 1.828 | 2.372 | 7.800 |
| 15 | 1.005 | 31.13 | 35.98 | 9.549 | 3.077 | 3.432 | 4.899 | 1.787 | 2.336 | 7.802 |
| 16 | 1.012 | 30.78 | 35.79 | 9.492 | 3.048 | 3.393 | 5.685 | 1.789 | 2.305 | 7.714 |
| 17 | 1.019 | 30.39 | 35.83 | 9.391 | 3.057 | 3.347 | 6.231 | 1.842 | 2.293 | 7.609 |
| 18 | 1.026 | 30.06 | 35.93 | 9.326 | 3.121 | 3.304 | 6.609 | 1.865 | 2.276 | 7.509 |
| 19 | 1.031 | 29.78 | 35.96 | 9.335 | 3.273 | 3.273 | 6.820 | 1.862 | 2.256 | 7.436 |
| 20 | 1.035 | 29.56 | 35.90 | 9.360 | 3.572 | 3.246 | 6.870 | 1.851 | 2.241 | 7.393 |
| 21 | 1.039 | 29.35 | 35.73 | 9.382 | 3.961 | 3.224 | 6.902 | 1.838 | 2.228 | 7.376 |
| 22 | 1.042 | 29.16 | 35.49 | 9.406 | 4.358 | 3.202 | 6.946 | 1.828 | 2.213 | 7.386 |
| 23 | 1.045 | 29.02 | 35.29 | 9.402 | 4.698 | 3.184 | 6.985 | 1.819 | 2.200 | 7.385 |
| 24 | 1.048 | 28.95 | 35.15 | 9.362 | 4.991 | 3.173 | 7.013 | 1.811 | 2.193 | 7.360 |
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MDPI and ACS Style
Chandra, K.; Sari, W.R.; Sriwulan, D.Y.; Adhimukti, M.R. Effect of Yield Spreads (State Bonds) on Economic Growth Performance in Indonesia. J. Risk Financial Manag. 2023, 16, 175. https://doi.org/10.3390/jrfm16030175
AMA Style
Chandra K, Sari WR, Sriwulan DY, Adhimukti MR. Effect of Yield Spreads (State Bonds) on Economic Growth Performance in Indonesia. Journal of Risk and Financial Management. 2023; 16(3):175. https://doi.org/10.3390/jrfm16030175
Chicago/Turabian StyleChandra, Kristian, Wahyuni Rusliyana Sari, Dwi Yantik Sriwulan, and Muhammad Raditya Adhimukti. 2023. "Effect of Yield Spreads (State Bonds) on Economic Growth Performance in Indonesia" Journal of Risk and Financial Management 16, no. 3: 175. https://doi.org/10.3390/jrfm16030175
APA StyleChandra, K., Sari, W. R., Sriwulan, D. Y., & Adhimukti, M. R. (2023). Effect of Yield Spreads (State Bonds) on Economic Growth Performance in Indonesia. Journal of Risk and Financial Management, 16(3), 175. https://doi.org/10.3390/jrfm16030175
