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Mr. Mohammadjavad Khosrosereshki, Dr Reza Najarzadeh, Dr Hassan Heydari,
Volume 22, Issue 2 (summer 2022 2022)
Volume 22, Issue 2 (summer 2022 2022)
Abstract
The purpose of this study is to investigate the impact of adding a non-Ricardian household to a DSGE model in choosing the Ramsey optimal monetary policy and consequently the effects on macroeconomic variables (such as output gap, consumption gap, inflation, and rising nominal exchange rate). Therefore, after estimating a model for the Iranian economy, the Ramsey optimal monetary policy was selected from 6 monetary policy alternatives. Then, in two scenarios, a non-Ricardian household is added to the model. In the first scenario, the non-Ricardian household consists of 20% of households and in the second, it consists of 40% of households. Then, Ramsey optimal monetary policy was selected for these two scenarios. The results show that the when the percentage of non-Ricardian households in the model increases, monetary policy-maker deviates from targeting monetary variables and gives more importance to production targeting. Second, if Ramsey optimal monetary policy is chosen without considering the non-Ricardian household in the model, in facing the shock of falling oil prices, the shock of declining money demand and the shock of rising external inflation, the responses of the production and consumption sectors in scenarios 1 and 2 are significantly different from the baseline model. But the consumption and production sectors have almost the same reactions in three models in response to the shock of the rising nominal exchange rate.
Mohammadjavad Khosrosereshki, Dr Alireza Keikha,
Volume 22, Issue 4 (winter 1401 2022)
Volume 22, Issue 4 (winter 1401 2022)
Abstract
Introduction:
Exchange rate pass-through (ERPT) is one of the most important indicators for monetary policymakers that shows the impact of exchange rate volatility on price indices (such as CPI, PPI, etc.). The economic stability and inflation environment are two factors affecting ERPT. The lower the inflation environment, the lesser the ERPT. In an oil-exporting country, the long-run situation of oil revenues can be a state variable of the economy and affect the expectations of economic agents. Therefore, the purpose of this study is to investigate the effect of sanctions against Iran and oil revenues situation on the ERPT from 1990Q2 to 2021Q1.
Methodology:
Regarding the implementing date of sanctions (2012Q1), the sanction period is from 2012Q1 to 2021Q1. Considering Lucas' critique, the switching models are not appropriate, and separated models are preferred. Therefore, by using the Bai-Perron (2003) method and taking oil revenues as a state variable of economy, the rest of the period is separated into two periods. The first period (from 1990Q1 to 2000Q4) is the phase of shortage in oil revenues and the second period (from 2001Q1 to 2011Q4) is the phase of abundance in oil revenues. The inflation environment during sanctions and shortage in oil revenues was high, and it was low in the period of abundance in oil revenues.
The ERPT for each period was calculated using the Structural Vector Autoregressive (SVAR) model. Oil price gap is the exogenous variable and the endogenous variables are respectively as follows: USA GDP, USA CPI, domestic GDP, exchange rate, liquidity and domestic CPI. All variables are in the first difference of logarithmic form. The Cholesky decomposition were used. The optimal lags for each model were selected by Hannan-Quinn information criterion (HQ), Akaike information criterion (AIC) and Final Prediction Error (FPE).
In this model, ERPT is the ratio of the accumulated response of CPI to exchange rate structural shock.
To investigate the effect of endogenous variables shocks on domestic CPI, variance and historical decomposition are used. Finally, the autoregressive trend of imports for each period is calculated to explain the status of imports versus different oil revenues. These equations can explain the dependency of CPI to imports.
Results and Discussion:
Only the ERPT in the sanctions period has a long-run effect on the economy. This effect is about 43%. The ERPT is 9.9% for the period of shortage in oil revenues, 25.1% for the period of abundance in oil revenues and 10.1% for the sanctions period. Unlike most previous studies, the results show that the lower the inflation environment, the higher the ERPT, and the higher the inflation environment, the lower the ERPT. The main cause of these unexpected changes in ERPT is related to share of imports in consumption basket. The import trend, either in the sanctions or the shortage oil revenues period, was decreasing while in the abundant oil revenues period, was increasing.
The results of the variance and historical decomposition show that in the period of sanctions, the exchange rate structural shocks have the largest share in inflation shocks, while in the other two periods, the inflation structural shock has the largest share in inflation shocks.
Conclusion:
The central bank of Iran is using the nominal exchange rate as an anchor to limit inflation and, finally, increase the monetary policymaker's credibility. In Iran, increasing oil revenues leads to implementing the crawling peg exchange rate system instead of the managed floating exchange rate system, and consequently, not only the PPI inflation will be greater than the imported goods inflation, but also the imports will increasingly grow. Therefore, it is expected that the share of imports in the consumption basket grows and CPI will be more sensitive to imports. These results can explain the ERPT changes.
In order to increase the credibility of the monetary policy maker and reduce the ERPT sensitivity to oil revenue situations, instead of using the nominal exchange rate anchor, the central bank should be more independent, commit to implementing monetary policy. So, according to the real sector of the economy, the central bank should announce its goals in the short-run and commit to them and announce the status report at the appointed times, and in the medium run, the central bank should pursue only its goals implicitly and increase its credibility among economic agents by making the economy more predictable. The more independent the central bank is, the easier it will be to follow the above policy.
Dr Mohammadjavad Khosrosereshki, Dr Yavar Dashtbany,
Volume 24, Issue 4 (Winter 2024)
Volume 24, Issue 4 (Winter 2024)
Abstract
Aim and Introduction
In managed floating exchange rate systems, one of the important issues facing monetary policymakers is defining the exchange rate corridor and committing to it. In developing countries (with a low flow of foreign capital and limited access to international financial markets), it is difficult to follow committed policies. Iran is not only an oil-exporting country but also has limited access to export earnings due to sanctions. The central bank is heavily dependent on the government policies. Consequently, these causes have led to 5 currency crises since 1991. Therefore, having an early warning system for Iran’s exchange market is essential for monetary policymakers. One of the prominent aspects of this study is the definition of crisis index, which considers not only the direct intervention of the central bank in the exchange market but also both the exchange rate growth and the central bank’s foreign reserve growth rate. Finally, using the Probit approach, an early warning system with a low noise-to-signal ratio is proposed.
Methodology
Quarterly data from 1990 to 2023 for Iran and the United States were used in the model. Domestic data represent the real and monetary sectors of Iran’s economy, and U.S. data represent the world economy.
First, using Bai-Perron (1997) and oil revenue as a state variable of the economy, the duration was divided into three periods: low oil revenue, high oil revenue, and sanctions. The random walk equation was estimated.
logoil t = a + b logoil t - 1 + ε t (1)
Second, for each period, according to Weymark's (1995) model, the exchange market pressure (EMP) and direct intervention indices were calculated. Then the average and standard deviation of both the exchange rate growth and the central bank’s foreign reserve growth rate were calculated.
Third, the situation in each quarter is determined as shown in Tables 1 and 2:
e t r t e σ e r σ r
Fifth, equation 2 was proposed as an early warning system.
CRI t ß 1 CRI t - 3 ß 2 SCN t ß 3 COV t ß 4 FRG t - 2 ß 5 ERG t - 2 ß 6 ERGEN t - 2 ß 7 BFRLiq t - 2 ß 8 GBDLiqG t - 2 ß 9 GDBLiqG t - 2 ß 10 LiqG t - 2 ß 11 GDPG t - 2 ß 12 OilGDP t - 2 + ß 13 OilG t - 2 ß 14 USAGDPG t - 2 ß 15 USA Inf t - 2 + ε t
All variables are I(0).CRI t SCN t COV t
FRG is the central bank's foreign reserve growth. ERG is the exchange rate growth. ERGEN is the deviation of exchange rate growth from its long-term trend. BFRLiq is the ratio of the central bank's foreign reserve to liquidity. GBDliqG is the growth of the ratio of the government budget deficit to liquidity. GDBliqG is the growth of the ratio of the government debt to the central bank to liquidity. LiqG is the liquidity growth. GDPG is the growth of GDP. OilGDP is the ratio of oil revenue to GDP. OilG is the Brent crude oil growth. USAGDPG is the growth of the USA's GDP. USAInf is the USA's CPI.
Sixth, the forecasted variable is compared with the actual. Then, the NSR was calculated. NSR is between [0,1]. The less NSR, the better fitted the forecasted variable.
Findings
There were no crisis type 1 and also mega-crises type 1 and 2. Therefore, the situation in each quarter is either crisis type 2 or ordinary. The crises are 1994q1, 1995q2, 2012q1, 2012q4, 2018q3 and q4, 2020q1 to q4, and 2023q1.
The Probit model is: (2)
CRI t = -48.154 - 4.717* CRI t - 3 + 1.718* SCN t + 11.496* COV t + 2.859* FRG t - 2 + 302.002* ERG t - 2 - 308.849* ERGEN t - 2 - 40.659* BFRLiq t - 2 - 36.540* GBDLiqG t - 2 + 40.174* GDBLiqG t - 2 + 99.098* LiqG t - 2 - 14.066* GDPG t - 2 + 98.677* OilGDP t - 2 - 0.482* OilG t - 2 - 4.932* USAGDPG t - 2 + 9.820* USA Inf t - 2 + ε t
The above equation can predict all crises. The NSR is 0.143 which means that the model can good predict the crises.
Discussion and Conclusion
In this article, in addition to examining the exchange rate seasonal growth and the central bank's foreign reserves' seasonal growth, the central bank's performance in the face of currency crises has been determined. Then, a model to predict currency crises with the Probit approach is also provided. The results show that the central bank and other economic agents do not consider the same seasons as critical. The economic agents expected a crisis in any season in which the sanction is approved, regardless of the time of its implementation. In most of the currency crises, the central bank has aggravated the currency crisis by buying foreign currency to increase its foreign exchange reserves. The crisis period has not exceeded two seasons in all cases except the Corona period. Among the internal variables of the model, the variables of liquidity growth, exchange rate growth, foreign exchange reserve growth, oil income to GDP ratio, and sanctions are effective factors in predicting a currency crisis. GDP growth, the Brent crude oil growth, and the difference in exchange rate growth from the long-term trend are also more effective variables in reducing the possibility of currency crises. The proposed early warning currency crisis system could predict all currency crises. Finally, as a policy recommendation, it is suggested that instead of stabilizing the exchange rate level, the central bank should stabilize the exchange rate growth in the mid-term to increase its credibility among economic agents by reducing the inflationary environment and currency fluctuations
In managed floating exchange rate systems, one of the important issues facing monetary policymakers is defining the exchange rate corridor and committing to it. In developing countries (with a low flow of foreign capital and limited access to international financial markets), it is difficult to follow committed policies. Iran is not only an oil-exporting country but also has limited access to export earnings due to sanctions. The central bank is heavily dependent on the government policies. Consequently, these causes have led to 5 currency crises since 1991. Therefore, having an early warning system for Iran’s exchange market is essential for monetary policymakers. One of the prominent aspects of this study is the definition of crisis index, which considers not only the direct intervention of the central bank in the exchange market but also both the exchange rate growth and the central bank’s foreign reserve growth rate. Finally, using the Probit approach, an early warning system with a low noise-to-signal ratio is proposed.
Methodology
Quarterly data from 1990 to 2023 for Iran and the United States were used in the model. Domestic data represent the real and monetary sectors of Iran’s economy, and U.S. data represent the world economy.
First, using Bai-Perron (1997) and oil revenue as a state variable of the economy, the duration was divided into three periods: low oil revenue, high oil revenue, and sanctions. The random walk equation was estimated.
Second, for each period, according to Weymark's (1995) model, the exchange market pressure (EMP) and direct intervention indices were calculated. Then the average and standard deviation of both the exchange rate growth and the central bank’s foreign reserve growth rate were calculated.
Third, the situation in each quarter is determined as shown in Tables 1 and 2:
Table 1: The situation in each quarter in leaning with the wall intervention type
| Type of intervention: leaning with the wall |
||
| Crisis type (1) | Mega-Crisis type (1) | |
| Favorable | Ordinary |
Table 2. The situation in each quarter in leaning against the wall intervention type
| Type of intervention: leaning against the wall |
||
| Ordinary | Mega-Crisis type (2) | |
| Favorable | Crisis type (2) |
Fifth, equation 2 was proposed as an early warning system.
All variables are I(0).
FRG is the central bank's foreign reserve growth. ERG is the exchange rate growth. ERGEN is the deviation of exchange rate growth from its long-term trend. BFRLiq is the ratio of the central bank's foreign reserve to liquidity. GBDliqG is the growth of the ratio of the government budget deficit to liquidity. GDBliqG is the growth of the ratio of the government debt to the central bank to liquidity. LiqG is the liquidity growth. GDPG is the growth of GDP. OilGDP is the ratio of oil revenue to GDP. OilG is the Brent crude oil growth. USAGDPG is the growth of the USA's GDP. USAInf is the USA's CPI.
Sixth, the forecasted variable is compared with the actual. Then, the NSR was calculated. NSR is between [0,1]. The less NSR, the better fitted the forecasted variable.
Findings
There were no crisis type 1 and also mega-crises type 1 and 2. Therefore, the situation in each quarter is either crisis type 2 or ordinary. The crises are 1994q1, 1995q2, 2012q1, 2012q4, 2018q3 and q4, 2020q1 to q4, and 2023q1.
The Probit model is: (2)
The above equation can predict all crises. The NSR is 0.143 which means that the model can good predict the crises.
Discussion and Conclusion
In this article, in addition to examining the exchange rate seasonal growth and the central bank's foreign reserves' seasonal growth, the central bank's performance in the face of currency crises has been determined. Then, a model to predict currency crises with the Probit approach is also provided. The results show that the central bank and other economic agents do not consider the same seasons as critical. The economic agents expected a crisis in any season in which the sanction is approved, regardless of the time of its implementation. In most of the currency crises, the central bank has aggravated the currency crisis by buying foreign currency to increase its foreign exchange reserves. The crisis period has not exceeded two seasons in all cases except the Corona period. Among the internal variables of the model, the variables of liquidity growth, exchange rate growth, foreign exchange reserve growth, oil income to GDP ratio, and sanctions are effective factors in predicting a currency crisis. GDP growth, the Brent crude oil growth, and the difference in exchange rate growth from the long-term trend are also more effective variables in reducing the possibility of currency crises. The proposed early warning currency crisis system could predict all currency crises. Finally, as a policy recommendation, it is suggested that instead of stabilizing the exchange rate level, the central bank should stabilize the exchange rate growth in the mid-term to increase its credibility among economic agents by reducing the inflationary environment and currency fluctuations
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