Pass-Through of Shocks into Different U.S. Prices

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Pass-Through of Shocks into Different U.S. Prices

One sentence summary: There is evidence for heterogeneous pass-through of different shocks into different U.S. prices.

The corresponding academic paper by Hakan Yilmazkuday is available as a working paper here.

 
Abstract

This paper estimates the pass-through of different shocks into alternative U.S. prices that are important for policy makers. The results based on the implications of a structural vector autoregression model show that oil price pass-through (OPPT) into import prices and producer prices is about 11%, whereas OPPT into consumer prices is about 4%. Exchange rate pass-through (XRPT) into import prices is about 39%, while XRPT into producer prices is about 32%. XRPT into consumer prices is about 2%, although this is the only pass-through estimate that is statistically insignificant. Import price pass-through (IPPT) into producer prices is about 82%, whereas IPPT into consumer prices is about 18%. Producer price pass-through into consumer prices is about 34%. Regarding the importance of each pass-through measure, the volatility in import prices, producer prices and consumer prices are mostly explained by oil prices (by up to 55%), followed by import prices (by up to 19%), producer prices (by 7%), and exchange rates (by up to 6%).

 

Non-technical Summary

The prices in the U.S. economy are affected by several international shocks as well as domestic shocks. Understanding the effects of these shocks for alternative U.S. prices is essential for understanding the transmission channel of these shocks and thus for conducting optimal monetary policy. Within this context, this paper estimates the oil price pass-through and exchange rate pass-through into import prices, producer prices and consumer prices. The pass-through of import prices into producer prices and consumer prices as well as the pass-through of producer prices into consumer prices are also estimated.

The estimation is based on the implications of a structural vector autoregression model, where quarterly data on global oil prices, U.S. import prices, U.S. real gross domestic product (GDP), U.S. real imports, U.S. producer price index, U.S. consumer price index, the (shadow) federal funds rate, and the U.S. nominal effective exchange rate are used. The pass-through measures are estimated by using the cumulative impulse response (CIR) of U.S. prices following specific shocks, which are divided by CIR of the shock variable to consider the developments in that variable over time.

The corresponding results show that oil price pass-through into import prices and producer prices is about 11%, whereas oil price pass-through into consumer prices is about 4%. Exchange rate pass-through into import prices is about 39%, while exchange rate pass-through into producer prices is about 32%. Exchange rate pass-through into consumer prices is about 2%, although this is the only pass-through estimate that is statistically insignificant. Import price pass-through into producer prices is about 82%, whereas import price pass-through into consumer prices is about 18%. Producer price pass-through into consumer prices is about 34%. It is implied that the pass-through measures are highly different for alternative U.S. prices. 

 

When we further investigate the importance of each pass-through measure based on the estimated forecast error variance decomposition measures, the volatility of import prices is mostly explained by oil prices with a contribution of about 54%, followed by exchange rates with a contribution of about 6%. The volatility of producer prices is mostly explained by oil prices with a contribution of about 55%, followed by import prices with a contribution of about 19%, and exchange rates with a contribution of about 6%. The volatility of consumer prices is mostly explained by oil prices with a contribution of about 55%, followed by import prices with a contribution of about 16%, producer prices with a contribution of about 7%, and exchange rates with a contribution of about 6%. It is implied that oil price shocks explain the lion's share of changes in U.S. prices.

 

The corresponding academic paper by Hakan Yilmazkuday is available as a working paper here.

 

 

 

 

Shock-Dependent Phillips Curve: Evidence from the U.S.

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Shock-Dependent Phillips Curve: Evidence from the U.S.

One sentence summary: The negatively-sloped Phillips curve has flattened and switched sign over time mostly due to first lower positive and then negative oil price shocks.

The corresponding academic paper by Hakan Yilmazkuday is available as a working paper here.

 
Abstract

This paper estimates the slope of the (price) Phillips curve for the U.S. by using the implications of a structural vector autoregression model. The slope of the Phillips curve is measured by the ratio of cumulative impulses of inflation and unemployment, following alternative shocks. The results show that oil price shocks that are consistent with a demand-pull inflation due to higher global demand result in a negatively-sloped Phillips curve, whereas unemployment or inflation shocks that are consistent with a cost-push inflation result in a positively-sloped Phillips curve. These results are combined with historical estimated shocks to further show that the negatively-sloped Phillips curve has flattened and switched sign over time mostly due to first lower positive and then negative oil price shocks, although the negatively-sloped Phillips curve is still alive and well when the full sample is considered.

 

Non-technical Summary

The slope of the Phillips curve represents the relationship between economic slack and inflation. This relationship is important as it is commonly used by central banks to conduct monetary policy, where the interest rate and other policy instruments are used to affect the aggregate demand (and thus the slack) in an economy so that inflation can be stabilized. Within this context, several studies in the literature have shown that the Phillips curve has flattened or even switched sign over time, which suggests that the connection between economic slack and inflation is becoming weaker or even reversed (i.e., the so-called missing inflation or deflation puzzle), conducting monetary policy has become more delicate, especially under changing economic conditions.

This paper attempts to understand the reasons behind the flattening of the (price) Phillips curve over time. The key innovation is to focus on alternative shocks that can affect the relationship between economic slack and inflation, which we call as the shock-dependent Phillips curve. This approach is in line with studies such as by <cite>mcleay2020optimal</cite> who suggest to control for supply shocks that result in a cost-push inflation to recover the Phillips curve, although this paper takes one more step to identify shocks that result in both a cost-push and a demand-pull inflation. The investigation is based on a structural vector autoregression (SVAR) model, where U.S. monthly data on inflation, unemployment, federal funds (policy) rate and global oil prices are used. The shock-dependent slope of the Phillips curve is measured by the ratio of cumulative impulse responses of inflation and unemployment, both following a common shock.

The empirical results show that positive oil price shocks that are consistent with a demand-pull inflation due to higher global demand result in an increase in inflation and a reduction in unemployment, which corresponds to having a negatively-sloped Phillips curve. In contrast, positive inflation or unemployment shocks that are consistent with a cost-push inflation result in an increase in both inflation and unemployment, especially in the long run, suggesting a positively-sloped Phillips curve. Therefore, it is implied that the slope of the Phillips curve depends on the shock experienced by the economy.

After showing empirical evidence for the shock-dependent Phillips curve, we move to the slope of the Phillips curve over time. This investigation is achieved by using average (over time) historical shocks estimated by the SVAR model as they are considered as typical shocks representing a certain time period. Specifically, the slopes of the shock-dependent Phillips curve are combined with the average (over time) historical shocks during a specific time period to obtain the slope of the Phillips curve for that time period.

 

The corresponding results show that there is evidence for a negatively-sloped Phillips curve for the full sample representing the monthly period between 1990 and 2021. When subsamples of 1990s, 2000s, 2010s and the COVID-19 period are investigated, it is shown that the Phillips curve has been negatively sloped during 1990s, statistically insignificant during 2000s, and it has switched sign by having a positive slope in 2010s and the COVID-19 period. When we further investigate whether any specific shock is responsible for the flattening of the Phillips curve, we show that oil price shocks explain the lion's share of changes over time.

 

Important policy implications follow. Specifically, based on the results in this paper, central banks are supposed to determine the drivers of inflation and conduct their monetary policy accordingly. On one hand, if a higher inflation is driven by oil price shocks that are consistent with a demand-pull inflation, central banks would have control over inflation as higher interest rates and other policy instruments can be used to lower the aggregate demand in their economy without getting away from the potential output level. On the other hand, if a higher inflation is driven by inflation or unemployment shocks that are consistent with a cost-push inflation, although higher policy rates and other policy instruments would still reduce inflation, this would be at the cost of getting away from the potential output level. 

 

The corresponding academic paper by Hakan Yilmazkuday is available as a working paper here.

 

 

Drivers of Turkish Inflation

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Drivers of Turkish Inflation

One sentence summary: A conventional monetary policy increasing policy rates following an increase in Turkish inflation or a depreciation of Turkish lira would be optimal to achieve and maintain price stability in Turkey.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Quarterly Review of Economics and Finance.

Working paper version is available here.

 
Abstract

This paper investigates the drivers of Turkish inflation by using a structural vector autoregression model, where monthly data on global oil prices, unemployment rate, inflation rate, policy rate and exchange rate are used. The empirical results show that Turkish inflation increases following a negative policy rate shock, a positive exchange rate shock, or a positive global oil price shock. The volatility of Turkish inflation is mostly explained by global oil prices and exchange rate movements in the long run, while the contribution of exchange rate shocks to Turkish inflation has continuously increased over time. As additional empirical results show that exchange rate depreciation can be reduced by positive policy rate shocks, it is implied that a conventional monetary policy increasing policy rates following an increase in inflation or a depreciation of Turkish lira would be optimal to achieve and maintain price stability in Turkey, which is the primary objective of the Central Bank of the Republic of Turkey.

 

Non-technical Summary

Although inflation rates in many emerging markets have decreased over time due to having successful monetary policies, the current Turkish inflation deviates by having one of the highest rates among emerging markets (i.e., the second following Argentina). As Turkey has an inflation targeting regime with an independent central bank which states "The primary objective of the Bank is to achieve and maintain price stability." on its webpage, where price stability is highlighted, the drivers of Turkish inflation are important to understand to form optimal policy not only in Turkey but in also other emerging markets that have an inflation targeting regime.

Accordingly, this paper attempts to understand the drivers of Turkish inflation by using a structural vector autoregression (SVAR) model, where monthly data on global oil prices, unemployment rate, inflation rate, policy rate and exchange rate are used. The empirical investigation is based on monthly data covering the period between 2005:M1 and 2021:M8. The results based on impulse response functions suggest that policy rate pass-through into Turkish inflation is negative and significant, where 1% of a change in the policy rate results in about 0.7% of a reduction in inflation in the long run. The exchange rate-pass through into Turkish inflation is about 26%, whereas the oil price pass-through into Turkish inflation is about 14% in the long run. 

 

 

The historical decomposition analysis further suggests that Turkish inflation has historically been driven by shocks of global oil prices and exchange rates, where the contribution of the latter has increased over time. Although policy rate shocks have also contributed to inflation historically, this contribution has been limited compared to those by shocks of exchange rates and global oil prices. The forecast error variance decomposition of Turkish inflation additionally suggests that about 40% of its variance is explained by global oil prices, whereas about 17% of its variance is explained by exchange rate movements.

 

To summarize, the empirical results suggest that Turkish inflation is mostly driven by shocks of global oil prices and exchange rates. The empirical results also show that the contribution of positive policy rate shocks to Turkish inflation is negative and significant, although the magnitude of the contribution is relatively less compared to those of global oil prices and exchange rates. As additional results show that exchange rate depreciation can be reduced by higher policy rates, it is implied that a conventional monetary policy increasing policy rates following an increase in inflation or a depreciation of Turkish currency (lira) would be optimal to achieve and maintain price stability in Turkey, which is the primary objective of CBRT. 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Quarterly Review of Economics and Finance.

Working paper version is available here.