Introduction

Monetary policy is a key instrument for maintaining economic stability, particularly amid increasingly complex global dynamics. In Indonesia, Bank Indonesia, as the monetary authority, plays a strategic role in controlling inflation and maintaining exchange rate stability to support sustainable economic growth [1]. The implementation of monetary policy is primarily conducted through the management of the benchmark interest rate, namely the BI-7 Day Reverse Repo Rate (BI7DRR), as well as exchange rate interventions. During the period 2016–2025, the Indonesian economy faced various external shocks, including the COVID-19 pandemic, global inflationary pressures, and heightened global economic uncertainty, which significantly affected exchange rate movements and the direction of interest rate policy [2].

These changes in macroeconomic conditions not only influence key economic indicators but also shape public expectations regarding future economic conditions. Public expectations are crucial, as they affect consumption, savings, and investment behavior [3]. In Indonesia, these expectations are reflected in the Consumer Expectations Index (CEI), which captures public perceptions and expectations of future economic conditions. Considering that household consumption is the largest contributor to Gross Domestic Product (GDP), changes in public expectations have direct implications for national economic growth [4].

The urgency of this research is increasingly evident, given that public expectations serve as a leading indicator in determining economic direction. Instability in consumer expectations can lead to a decline in household consumption, ultimately slowing economic growth [5]. On the other hand, fluctuations in exchange rates and adjustments in the benchmark interest rate by Bank Indonesia are not always immediately responded to by the public, creating uncertainty regarding the effectiveness of monetary policy transmission [6]. Furthermore, the 2016–2025 period, characterized by multiple global shocks, indicates that public expectations have become more sensitive to changes in economic conditions. Therefore, a deeper understanding of the factors influencing consumer expectations is essential, not only to enrich academic literature but also to provide a basis for more targeted and responsive monetary policy formulation [7].

From a theoretical perspective, exchange rates and interest rates are key macroeconomic variables that influence public expectations. The exchange rate, within the framework of Purchasing Power Parity (PPP), is closely related to price levels and purchasing power; thus, currency depreciation may reduce public optimism [8]. Meanwhile, interest rates, as explained by Keynes’s liquidity preference theory, reflect the interaction between money supply and demand, influencing consumption and investment decisions [9]. However, adaptive and rational expectations theories suggest that public responses to economic changes are not always immediate or uniform [10]. Additionally, behavioral economics highlights that psychological factors and limited information play a role in shaping expectations, making the relationship between macroeconomic variables and public expectations more complex [11].

Previous studies, such as [12] and [13], have examined the effects of exchange rates and interest rates on various macroeconomic indicators. However, studies specifically focusing on their impact on public expectations remain limited, particularly in the Indonesian context during the post-pandemic period. Moreover, prior research presents mixed findings, indicating a lack of consensus regarding the role of these variables in shaping consumer expectations. This highlights the existence of a research gap that warrants further investigation. Therefore, this study aims to analyze the effect of the exchange rate and the BI-7 Day Reverse Repo Rate on the Consumer Expectations Index (CEI). However, exchange rate stability is expected to play a more dominant role in shaping consumer expectations compared to interest rate policy in Indonesia. The findings are expected to provide empirical insights and serve as a reference for Bank Indonesia in formulating more effective and responsive policies to maintain the stability of public expectations amid global economic dynamics.

Met hods

This study employs a quantitative approach to analyze the impact of the exchange rate and the BI-7 Day Reverse Repo Rate on the Consumer Expectations Index (CEI) in Indonesia. The data used are secondary time series data covering the period 2016–2025, obtained from official publications of Bank Indonesia. The dependent variable in this study is public expectations, measured by the Consumer Expectations Index, while the independent variables consist of the exchange rate and the BI-7 Day Reverse Repo Rate. The analytical method used is the Vector Error Correction Model (VECM), which allows for the examination of both short-run and long-run relationships among variables. The stages of analysis are as follows:

1. Stationarity Test

The initial stage was to test the stationary of the data using the root test of the Augmented Dickey-Fuller (ADF) unit. This test aims to ensure that the data does not contain root units so as to avoid pseudo-regression [14]. The data is said to be stationary when the absolute value of the ADF t-statistic is greater than the critical value of MacKinnon at a certain level of significance [15].

2. Determination of Optimal Lag

The determination of lag length is carried out to find out the period of time that a variable takes in response to changes in other variables. In addition, the selection of the right lag is also important to overcome the autocorrelation problem in the VAR model. The optimal lag is determined based on several information criteria such as the Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn (HQ), by selecting the best criterion values [16].

3. VAR Stability Test

Stability tests are carried out to ensure that the VAR model used is stable. This test is carried out by looking at the roots of characteristic polynomial. The model is declared stable when all roots are within a unit circle, so the results of advanced analyses such as IRF and FEVD can be considered valid [17].

4. Granger's Causality Test

The Granger causality test is used to identify cause-and-effect relationships between variables in the model. This test is performed to see if an independent variable has the ability to improve the prediction accuracy of the dependent variable [18].

5. Cointegration Test

The cointegration test aims to find out if there is a long-term relationship between the variables used. In this study, the Johansen method was used, by looking at the trace statistic value and maximum eigenvalue. If the value is greater than the critical value at a certain level of significance, then it can be concluded that there is a cointegration relationship between the variables [19].

6. Uji Vector Error Correction Model (VECM)

After it was proven that there was a cointegration relationship between variables, the analysis was continued using the Vector Error Correction Model (VECM). This model is used to estimate the short-term and long-term relationships simultaneously between variables in the system [20].

7. Impulse Response Function (IRF) dan Forecast Error Variance Decomposition (FEVD)

It is used to analyze the response of an endogenous variable to the presence of shocks in other variables in the system. Through IRF, it can be known the direction and duration of the influence of a shock on the observed variable [21]. In contrast to IRFs that look at responses to shocks, FEVD describes how much of a relative role each variable plays in explaining changes in certain variables over the next few periods [22].

Results and Discussion

A. Results

1. Analysis of Variable Trends

Figure 1 illustrates the development of the Consumer Expectations Index (CEI) in Indonesia from 2016 to 2025, which reflects the movement of public expectations toward future economic conditions over the observed period.

Figure 1. Development of the Consumer Expectations Index, 2016–2025

The development of the Consumer Expectations Index (CEI) exhibits a fluctuating trend over the study period, reflecting dynamic changes in public perceptions of future economic conditions. As illustrated in Figure 1, a notable contraction occurred in 2020, primarily driven by the unprecedented economic disruption caused by the COVID-19 pandemic. This was followed by a gradual recovery phase in the subsequent years, although the pace of improvement varied across periods. The Consumer Expectations Index closely mirrors broader macroeconomic conditions and public sentiment toward economic stability. During the pre-pandemic period of 2016 to 2019, the index demonstrated a consistent upward trend, increasing from 126.36 to 139.07. This indicates a period of strengthening consumer confidence and rising optimism regarding Indonesia’s economic prospects. However, this positive trajectory was interrupted in 2020, when the index sharply declined to 119.05, reflecting heightened uncertainty, mobility restrictions, and weakened economic activity during the pandemic period. In 2021, the index continued to decline, albeit at a more moderate rate compared to the previous year, suggesting that although economic conditions remained under pressure, the level of deterioration in expectations began to stabilize. Subsequently, during the period 2022 to 2024, the CEI gradually recovered in line with improving macroeconomic conditions, increased economic activity, and policy normalization, reaching approximately 134.99. Nevertheless, in 2025, the index experienced a slight decline to 132.54, indicating a mild adjustment in consumer sentiment amid ongoing global and domestic uncertainties Overall, the Consumer Expectations Index demonstrates a high degree of sensitivity to macroeconomic shocks and external disruptions, particularly during crisis periods. This behavior highlights the importance of economic stability in maintaining public confidence and sustaining positive consumer expectations [23]. Moreover, this finding is consistent with previous studies which emphasize that consumer expectations tend to respond strongly to changes in economic uncertainty and crisis conditions [3].

Figure 2 illustrates the development of the Rupiah exchange rate against the US dollar (USD) in Indonesia from 2016 to 2025, reflecting fluctuations in currency value influenced by domestic and global economic conditions.

Figure 2. Development of the Rupiah Exchange Rate against USD 2016–2025

The development of the Rupiah exchange rate against the US dollar during the period 2016–2025 shows a generally depreciating trend with a fluctuating pattern, as illustrated in Figure 4.2. In 2016–2017, the exchange rate remained relatively stable in the range of 13.33–13.39. However, in 2018, a significant depreciation occurred, reaching 14.351, reflecting external pressures. In 2019, the Rupiah experienced a slight appreciation, but it weakened again in 2020 due to the impact of the COVID-19 pandemic, reaching 14.626. In 2021, the exchange rate slightly strengthened, yet the depreciation trend continued until 2025, reaching 16.504 [24]. Overall, highlights a persistent weakening trend of the Rupiah over the long term despite short-term fluctuations These movements indicate that the exchange rate is highly influenced by both global and domestic economic conditions [25]. This depreciation trend may also affect public perceptions of economic stability, particularly through its impact on prices and purchasing power [26].

Figure 3 illustrates the development of the BI-7 Day Reverse Repo Rate (BI7DRR) in Indonesia from 2016 to 2025, reflecting the monetary policy stance of Bank Indonesia in responding to changing domestic and global economic conditions.

Figure 3. Development of the BI-7 Day Reverse Repo Rate 2016–2025 (%)

The development of the BI-7 Day Reverse Repo Rate (BI7DRR) shows a dynamic pattern and strong responsiveness to economic conditions, as illustrated in Figure 4.3. In 2016–2017, the interest rate declined from 5.58% to 4.56% as part of an expansionary monetary policy. Subsequently, in 2018–2019, the rate increased again, reaching 5.63% to maintain economic stability. During the pandemic period of 2020–2021, the BI7DRR was significantly reduced to 3.52% in order to support economic recovery. However, in the period 2022–2024, the interest rate was raised again, reaching 6.10% in response to inflationary pressures and exchange rate stabilization. In 2025, the rate was lowered once more to 5.27%. Overall, as shown in Figure 4.3, the BI7DRR tends to adjust flexibly following economic conditions, reflecting its role as an active monetary policy instrument [13].

2. Test Model

Before estimating using the Vector Error Correction Model (VECM), this study conducted several preliminary tests to ensure the feasibility of the model. These tests include stationarity tests, optimal lag determination, VAR stability tests, cointegration tests, and Granger causality tests. The results of this test are the basis for VECM estimation and further analysis.

a. Stationarity Test

Table 1 presents the results of the Augmented Dickey-Fuller (ADF) stationarity test for all variables used in this study to ensure the data are free from unit root problems before further analysis.

Table 1 . Stationary Test Results

The results of the stationarity test using the Augmented Dickey-Fuller (ADF) method showed that not all stationary variables were at the level level. The Consumer Expectations Index (CEI) variable has been stationary at the level, which is indicated by a probability value of 0.0191 which is smaller than the significance level of 5%. Meanwhile, the variable exchange rate (KURS) and BI-7 Day Reverse Repo Rate (BI7DRR) are not stationary at the level as they have probability values of 0.5892 and 0.1435, respectively, which are greater than 0.05. However, after the first differentiation is carried out, all variables become stationary. This is indicated by the probability value of each variable which is below the significance level of 5%, which is 0.0000 for all variables.

b. Determination of Optimal Lag

Table 2 presents the results of the optimal lag length determination based on several information criteria, including the Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn (HQ), which are used to identify the most appropriate lag structure in the model.

Table 2. Results of the Optimal Lag Determination Test

Based on the results of the optimal lag test, it was found that several information criteria provided different lag recommendations. The Akaike Information Criterion (AIC) and Final Prediction Error (FPE) show a minimum value at lag of 2, which is 10.72039 and 9.090803, respectively. Meanwhile, the Schwarz Criterion (SC) and Hannan-Quinn (HQ) show a minimum value at lag of 1.

Despite the differences between the criteria, this study chose lag 2 as the optimal lag, considering that AIC and FPE are better able to capture model dynamics, especially in time series analysis with a relatively limited number of observations. In addition, lag 2 is also still within reasonable limits so it does not cause overparameterization problems. Thus, lag 2 was used in the estimation of the Vector Error Correction Model (VECM) model to analyze the relationships between variables in this study.

c. VAR Stability Test

Table 3 presents the results of the VAR stability test, which is conducted to ensure that the estimated model is stable by examining whether all roots of the characteristic polynomial lie within the unit circle.

Table 3. VAR Stability Test Results

Based on the results of the VAR stability test, it is known that all characteristic root values have a modulus of less than 1. The largest modulus value was recorded at 0.662717, while the other values were in the range of 0.413059 to 0.583764. This indicates that all roots are within a unit circle. Thus, the VAR model used in this study was declared stable. This condition shows that the model has met the stability assumptions, so that the results of the resulting estimates can be considered valid and can be continued to the next stage of analysis, namely the cointegration test and the Vector Error Correction Model (VECM) estimation.

d. Granger's Causality Test

Table 4 presents the results of the Granger causality test to identify causal relationships among the variables in the model.

Table 4. Granger Causality Test Results

The results of the Granger causality test show that there is no causal relationship between the exchange rate (KURS) and the Consumer Expectations Index (CEI), because the probability of both > 0.05. The BI-7 Day Reverse Repo Rate (BI7DRR) also has no effect on CEI. However, there is a one-way causality relationship from CEI to BI7DRR (probability 0.0334). Meanwhile, no causal relationship was found between BI7DRR and KURS. Thus, a significant causality relationship in the model occurs in only one direction, i.e. from CEI to BI7DRR.

e. Cointegration Test

Table 5 presents the results of the Johansen cointegration test to examine the long-run relationship among the variables.

Table 5 . Cointegration Test Results

Based on the results of the Johansen cointegration test, it was found that the trace statistic value on all hypotheses was greater than the critical value at a significance level of 5%, and had a probability value of 0.0000. This shows that there is a cointegration relationship between variables in the model. Thus, it can be concluded that the variables in this study have a long-term relationship, so the Vector Error Correction Model (VECM) can be used to analyze these relationships.

f. Vector Error Correction Model (VECM)

Table 6 presents the results of the Vector Error Correction Model (VECM) for the long-run relationship, which is used to examine the equilibrium relationship between the exchange rate, the BI-7 Day Reverse Repo Rate, and the Consumer Expectations Index.

Table 6 . Vector Error Correction Model (VECM) Long -Term

In the long run, the error correction term (ECT) coefficient of -1.121163 with a t-statistic of -7.43903 is negative and significant, indicating a strong adjustment mechanism toward long-term equilibrium. This suggests that any short-term disequilibrium will be corrected relatively quickly. The exchange rate remains negatively significant at lag 1 and lag 2, confirming its consistent influence on CEI over time. In contrast, BI7DRR remains statistically insignificant in the long run, as reflected by low t-statistics in both lags. Overall, these results indicate that the exchange rate plays a more dominant and persistent role in shaping public expectations compared to the benchmark interest rate, both in the short and long term[27]. Overall, these results indicate that the exchange rate plays a more dominant and persistent role in shaping public expectations compared to the benchmark interest rate, both in the short and long term. This further suggests that external value stability of the Rupiah is more closely perceived by households than monetary policy signals through interest rate adjustments, making exchange rate movements a key driver of expectation formation in Indonesia.

Table 7 presents the short-run estimation results of the Vector Error Correction Model (VECM).

Table 7 . Vector Error Correction Model (VECM) Short -Term

Based on the results of VECM estimates, in the short term the exchange rate (KURS) has a negative and significant effect on the Consumer Expectations Index (CEI), with a t-statistic of -2.99215, which shows that exchange rate depreciation lowers public expectations. In contrast, the BI-7 Day Reverse Repo Rate (BI7DRR) has no significant effect in the short term (t-statistic 1.31776). In the long term, the error correction term (ECT) of -1.121163 with a t-statistic of -7.43903 indicates a significant adjustment mechanism towards equilibrium. The exchange rate also remained negative and significant on some lags (lags 1 and 2), while BI7DRR was insignificant in both the short and long term. Overall, the exchange rate is more dominant in influencing public expectations than the benchmark interest rate.

g. Impulse Response Function (IRF)

Figure 4 illustrates the results of the Impulse Response Function (IRF) analysis showing the response of the Consumer Expectations Index to shocks in the variables of the model.

Figure 4. Impulse Response Function (IRF) Test Results

The IRF results show that the response of the Consumer Expectations Index (CEI) to exchange rate shocks fluctuated at the beginning, briefly increased sharply and then decreased, and finally stabilized at a positive level. This signifies a strong initial but temporary impact and a return to equilibrium in the long run, indicating that the effect of exchange rate shocks is absorbed gradually by the system over time. The initial volatility reflects the sensitivity of consumer expectations to sudden changes in currency value, particularly in the short-term horizon. In contrast, the CEI response to the BI-7 Day Reverse Repo Rate (BI7DRR) shock tends to be negative at the beginning, with small fluctuations before stabilizing. The magnitude of this response is relatively weaker compared to the exchange rate, suggesting that interest rate shocks have a limited and less immediate effect on shaping public expectations. This may indicate that monetary policy transmission through interest rates is not directly or quickly reflected in consumer sentiment. Overall, the exchange rate is more dominant in influencing public expectations, although both variables eventually converge to a stable condition in the long term. This reinforces the finding that exchange rate movements play a more critical role in shaping expectation dynamics in Indonesia compared to interest rate adjustments.

h. Forecast Error Variance Decomposition (FEVD)

Table 8 presents the results of the Forecast Error Variance Decomposition (FEVD) used to analyze the contribution of each variable in explaining the variance of the Consumer Expectations Index.

Table 8. Forecast Error Variance Decomposition (FEVD) Test Results

Based on the results of the FEVD, in the initial period the variability of the Consumer Expectation Index (IEK) was fully explained by itself (100%). However, over time the contribution decreased. The exchange rate (KURS) is starting to exert an increasing influence, from almost zero to around 28.97% in the 20th period, showing its important role in the long run. Meanwhile, the contribution of the BI-7 Day Reverse Repo Rate (BI7DRR) is relatively small, although it increased to around 5.71%. Overall, the IEK is still dominated by itself, but the exchange rate is more influential than the interest rate in explaining the variation.

B. Discussion

The results of the study indicate that the exchange rate has a significant and negative effect on the Consumer Expectations Index (CEI), particularly in the short run, as reflected in the VECM estimation where the exchange rate variable is statistically significant. This suggests that exchange rate depreciation leads to a decline in public expectations regarding future economic conditions. From an economic perspective, a weaker exchange rate increases the cost of imported goods, reduces purchasing power, and creates uncertainty, which in turn affects public Expectations. This finding is consistent with the Impulse Response Function (IRF), where shocks to the exchange rate generate a relatively strong response in CEI in the early periods before gradually stabilizing in the long run.

Furthermore, the Forecast Error Variance Decomposition (FEVD) results strengthen this finding, showing that the contribution of the exchange rate to the variation in CEI increases over time, reaching approximately 28% in the long run. Initially, fluctuations in CEI are largely explained by the variable itself; however, over time, the role of the exchange rate becomes more substantial. This indicates that the exchange rate is a key determinant in shaping public expectations, highlighting the importance of maintaining exchange rate stability to sustain economic Expectations.

On the other hand, the BI-7 Day Reverse Repo Rate (BI7DRR) does not exhibit a significant effect on CEI in either the short or long term, as indicated by the insignificant t-statistics in the VECM results. This implies that changes in the policy interest rate are not directly perceived or immediately responded to by the public in forming economic expectations. The IRF results also show that the response of CEI to interest rate shocks is relatively weak and short-lived. In addition, the FEVD results reveal that the contribution of BI7DRR remains relatively small, at around 5% in the final period, indicating its limited role compared to the exchange rate.

In the long run, the significance of the error correction term (ECT) confirms the presence of a strong adjustment mechanism toward equilibrium. This means that although short-term fluctuations may occur, the system will gradually return to its long-run equilibrium path. The negative and significant ECT coefficient also indicates a relatively fast speed of adjustment. Therefore, the relationship between exchange rates, interest rates, and public expectations is stable in the long term, even though the short-term dynamics differ.

From a theoretical perspective, these findings are consistent with the Purchasing Power Parity (PPP) theory, which states that exchange rate movements are closely linked to price levels and purchasing power, thereby influencing public expectations. Meanwhile, the insignificant effect of interest rates can be explained by Keynes’s liquidity preference theory and expectation formation theories, where the transmission of interest rate policy to the real sector requires time and is not always immediately reflected in public perception. Additionally, behavioral economics suggests that individuals tend to respond more strongly to variables that are directly observable and easily understood, such as exchange rates, rather than more abstract variables like interest rates.Top of Form

Conclusion

This study concludes that Rupiah stability, as reflected in the exchange rate, plays a significant and dominant role in influencing the Consumer Expectations Index (CEI) in Indonesia, both in the short run and long run, where Rupiah depreciation tends to reduce public economic expectations. In contrast, the BI-7 Day Reverse Repo Rate does not have a significant impact on public expectations, indicating that interest rate policy is not directly perceived or strongly responded to by society in forming expectations. The existence of a significant long-term equilibrium relationship confirms that the variables move together over time despite short-term fluctuations. Overall, these findings highlight that exchange rate stability is the main driver of public expectations in Indonesia, while also reinforcing the relevance of Purchasing Power Parity and expectation formation theories, although the study is limited by the use of a restricted set of macroeconomic variables and a specific time period.

Acknowledgments

The author would like to express sincere gratitude to the academic supervisor for invaluable guidance, advice, and support throughout this research. The author also acknowledges the Faculty of Economics and Business, Universitas Pembangunan Nasional “Veteran” East Java, for providing academic support. In addition, appreciation is extended to Bank Indonesia for providing the data used in this study.