Price puzzle: Difference between revisions
Content deleted Content added
m Open access bot: hdl, doi updated in citation with #oabot. |
m Open access bot: hdl updated in citation with #oabot. |
||
| (3 intermediate revisions by 3 users not shown) | |||
| Line 5: | Line 5: | ||
The term "price puzzle" was first introduced by Lawrence Christiano in 1992,<ref>{{Cite journal |last1=Christiano |first1=Lawrence J. |date=1992 |title=Investigations of monetary policy rules |journal=Carnegie-Rochester Conference Series on Public Policy |volume=41 |pages=151–195 |doi=10.1016/0167-2231(94)90010-8 |issn=0167-2231|doi-access=free }}</ref> who observed this anomaly in SVAR models analyzing U.S. [[monetary policy]]. Early studies found that when using short-term interest rates, such as the [[federal funds rate]], as the primary indicator of monetary policy, SVAR models often produced results inconsistent with theoretical expectations. This sparked a series of investigations into the limitations of these models and the underlying causes of the puzzle. |
The term "price puzzle" was first introduced by Lawrence Christiano in 1992,<ref>{{Cite journal |last1=Christiano |first1=Lawrence J. |date=1992 |title=Investigations of monetary policy rules |journal=Carnegie-Rochester Conference Series on Public Policy |volume=41 |pages=151–195 |doi=10.1016/0167-2231(94)90010-8 |issn=0167-2231|doi-access=free }}</ref> who observed this anomaly in SVAR models analyzing U.S. [[monetary policy]]. Early studies found that when using short-term interest rates, such as the [[federal funds rate]], as the primary indicator of monetary policy, SVAR models often produced results inconsistent with theoretical expectations. This sparked a series of investigations into the limitations of these models and the underlying causes of the puzzle. |
||
== |
==Efforts to Resolve the Price Puzzle== |
||
| ⚫ | |||
The price puzzle is largely attributed to the structure and assumptions of SVAR models, which often fail to fully capture the information set available to central banks or the forward-looking nature of monetary policy. Key issues include: |
|||
One approach to resolving the price puzzle involves expanding the information set in SVAR models. For instance, including variables like [[commodity]] prices or [[Federal Reserve]] forecasts (e.g., Greenbook data) can provide additional context for policy decisions, reducing the puzzle's prevalence.<ref name="CEE1999">{{Cite journal |last1=Christiano |first1=Lawrence J. |last2=Eichenbaum |first2=Martin |last3=Evans |first3=Charles L. |date=1999 |title=Monetary policy shocks: What have we learned and to what end? |journal=Handbook of Macroeconomics |volume=1 |pages=65–148 |doi=10.1016/S1574-0048(99)01005-8 |issn=1574-0048}}</ref><ref>{{Cite journal |last1=Romer |first1=Christina D. |last2=Romer |first2=David H. |date=2004 |title=A new measure of monetary shocks: Derivation and implications |journal=American Economic Review |volume=94 |issue=4 |pages=1055–1084 |doi=10.1257/0002828042002651 |issn=0002-8282}}</ref> |
|||
=== Improved Identification Strategies for Monetary Policy Shocks=== |
|||
*Omitted Variables: Traditional SVAR models sometimes exclude variables that capture supply-side shocks, such as [[commodity]] prices, leading to biased results.<ref name="CEE1999">{{Cite journal |last1=Christiano |first1=Lawrence J. |last2=Eichenbaum |first2=Martin |last3=Evans |first3=Charles L. |date=1999 |title=Monetary policy shocks: What have we learned and to what end? |journal=Handbook of Macroeconomics |volume=1 |pages=65–148 |doi=10.1016/S1574-0048(99)01005-8 |issn=1574-0048}}</ref> |
|||
====High-Frequency Identification (HFI)==== |
|||
*Backward-Looking Dynamics: Many SVAR models assume delayed reactions to policy shocks, which neglect the anticipatory behavior of economic agents and policymakers.<ref name="Chen2025">{{Cite journal |last1=Chen |first1=Zhengyang |last2=Valcarcel |first2=Victor J. |date=January 2025 |title=Modeling inflation expectations in forward-looking interest rate and money growth rules |url=https://www.sciencedirect.com/science/article/pii/S016518892400191X |journal=Journal of Economic Dynamics and Control |language=en |volume=170 |pages=104999 |doi=10.1016/j.jedc.2024.104999 |issn=0165-1889|doi-access=free }}</ref> |
|||
High-frequency identification exploits financial market reactions in narrow windows around monetary policy announcements (Gertler and Karadi, 2015<ref>{{Cite journal |last1=Gertler |first1=Mark |last2=Karadi |first2=Peter |date=2015 |title=Monetary Policy Surprises, Credit Costs, and Economic Activity |journal=American Economic Journal: Macroeconomics |volume=7 |issue=1 |pages=44-76 |doi=10.1257/mac.20130329 |issn=1945-7707}}</ref>; Nakamura and Steinsson, 2018<ref>{{Cite journal |last1=Nakamura |first1=Emi |last2=Steinsson |first2=Jón |date=2018 |title=High-Frequency Identification of Monetary Non-Neutrality: The Information Effect |journal=The Quarterly Journal of Economics |volume=133 |issue=3 |pages=1283-1330 |doi=10.1093/qje/qjy004 |issn=0033-5533}}</ref>). This approach leverages the fact that movements in financial instruments (like federal funds futures) during a tight window around Federal Open Market Committee (FOMC) announcements are likely driven by monetary policy news rather than other macroeconomic factors. |
|||
====Sign Restrictions==== |
|||
*Limited Monetary Policy Indicators: Using simple interest rate rules without considering broader [[monetary aggregates]] can result in incomplete representations of policy impacts, exacerbating the price puzzle.<ref name="Chen2021">{{Cite journal |last1=Chen |first1=Zhengyang |last2=Valcarcel |first2=Victor J. |date=October 2021 |title=Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle |url=https://www.sciencedirect.com/science/article/abs/pii/S0165188921001494 |journal=Journal of Economic Dynamics and Control |language=en |volume=131 |pages=104214 |doi=10.1016/j.jedc.2021.104214 | issn=0165-1889}}</ref> |
|||
Uhlig (2005)<ref>{{Cite journal |last1=Uhlig |first1=Harald |date=2005 |title=What are the effects of monetary policy on output? Results from an agnostic identification procedure |journal=Journal of Monetary Economics |volume=52 |issue=2 |pages=381-419 |doi=10.1016/j.jmoneco.2004.05.007 |issn=0304-3932}}</ref> pioneered the use of sign restrictions in monetary policy SVARs. This approach imposes theoretically motivated restrictions on impulse responses while remaining agnostic about the response of key variables of interest (like prices). Modern applications often combine sign restrictions with other identifying assumptions: |
|||
* Narrative restrictions (Antolín-Díaz and Rubio-Ramírez, 2018<ref>{{Cite journal |last1=Antolín-Díaz |first1=Juan |last2=Rubio-Ramírez |first2=Juan F. |date=2018 |title=Narrative Sign Restrictions for SVARs |journal=American Economic Review |volume=108 |issue=10 |pages=2802-2829 |doi=10.1257/aer.20161852 |issn=0002-8282|hdl=10419/172913 |hdl-access=free }}</ref>) |
|||
==Efforts to Resolve the Price Puzzle== |
|||
* Zero restrictions (Arias et al., 2019<ref>{{Cite journal |last1=Arias |first1=Jonas E. |last2=Rubio-Ramírez |first2=Juan F. |last3=Waggoner |first3=Daniel F. |date=2019 |title=Inference in Bayesian Proxy-SVARs |journal=Journal of Econometrics |volume=208 |issue=2 |pages=613-633 |doi=10.1016/j.jeconom.2018.09.016 |issn=0304-4076|hdl=10419/162505 |hdl-access=free }}</ref>) |
|||
| ⚫ | |||
* Long-run restrictions (Matthes and Schwartzman, 2019<ref>{{Cite journal |last1=Matthes |first1=Christian |last2=Schwartzman |first2=Felipe |date=2019 |title=What Do Sectoral Dynamics Tell Us About the Origins of Business Cycles? |journal=Journal of Monetary Economics |volume=104 |pages=67-82 |doi=10.1016/j.jmoneco.2019.03.008 |issn=0304-3932}}</ref>) |
|||
===New Explanation: Cost Channel of Monetary Policy=== |
|||
One prominent explanation is the cost channel of monetary transmission <ref>{{Cite journal |last1=Barth |first1=Marvin J. |last2=Ramey |first2=Valerie A. |date=2001 |title=The Cost Channel of Monetary Transmission |journal=NBER Macroeconomics Annual |volume=16 |pages=199-240 |doi=10.1086/654443 |issn=0889-3365}}</ref>. Higher interest rates increase firms' borrowing costs for working capital (used to pay wages and intermediate inputs), which can lead to higher production costs that are passed on to consumers in the form of higher prices, at least in the short run <ref>{{Cite journal |last1=Christiano |first1=Lawrence J. |last2=Eichenbaum |first2=Martin |last3=Evans |first3=Charles L. |date=2005 |title=Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy |journal=Journal of Political Economy |volume=113 |issue=1 |pages=1-45 |doi=10.1086/426038 |issn=0022-3808}}</ref>. The existence of this channel has important implications for the conduct of optimal monetary policy <ref>{{Cite journal |last1=Ravenna |first1=Federico |last2=Walsh |first2=Carl E. |date=2006 |title=Optimal monetary policy with the cost channel |journal=Journal of Monetary Economics |volume=53 |issue=2 |pages=199-216 |doi=10.1016/j.jmoneco.2005.01.004 |issn=0304-3932}}</ref>. |
|||
===Divisia Monetary Aggregates=== |
===Divisia Monetary Aggregates=== |
||
The study of [[Divisia monetary aggregates]] as superior policy indicators has its roots in the work of Keating et al.<ref>{{Cite journal |last1=Keating |first1=John W. |last2=Kelly |first2=L.J. |last3=Smith |first3=A.L. |last4=Valcarcel |first4=Victor J. |date=February 2019 |title=A model of monetary policy shocks for financial crises and normal conditions |journal=Journal of Money, Credit and Banking |volume=51 |issue=1 |pages=227–259 |doi=10.1111/jmcb.12510}}</ref> and Belongia and Ireland,<ref>{{Cite journal |last1=Belongia |first1=Michael T. |last2=Ireland |first2=Peter N. |date=2014 |title=The Barnett critique after three decades: A new Keynesian analysis |journal=Journal of Econometrics |volume=183 |issue=1 |pages=5–21 |doi=10.1016/j.jeconom.2014.06.008 |issn=0304-4076|hdl=10419/101887 |hdl-access=free }}</ref> who emphasized the importance of incorporating broad monetary aggregates into economic models to better understand monetary policy effects. Their research demonstrated that Divisia aggregates outperform traditional simple-sum measures, such as M1 and M2, by resolving anomalies like the price puzzle and establishing a more stable relationship between [[money supply]] and macroeconomic variables. |
The study of [[Divisia monetary aggregates]] as superior policy indicators has its roots in the work of Keating et al.<ref>{{Cite journal |last1=Keating |first1=John W. |last2=Kelly |first2=L.J. |last3=Smith |first3=A.L. |last4=Valcarcel |first4=Victor J. |date=February 2019 |title=A model of monetary policy shocks for financial crises and normal conditions |journal=Journal of Money, Credit and Banking |volume=51 |issue=1 |pages=227–259 |doi=10.1111/jmcb.12510}}</ref> and Belongia and Ireland,<ref>{{Cite journal |last1=Belongia |first1=Michael T. |last2=Ireland |first2=Peter N. |date=2014 |title=The Barnett critique after three decades: A new Keynesian analysis |journal=Journal of Econometrics |volume=183 |issue=1 |pages=5–21 |doi=10.1016/j.jeconom.2014.06.008 |issn=0304-4076|hdl=10419/101887 |hdl-access=free }}</ref> who emphasized the importance of incorporating broad monetary aggregates into economic models to better understand monetary policy effects. Their research demonstrated that Divisia aggregates outperform traditional simple-sum measures, such as M1 and M2, by resolving anomalies like the price puzzle and establishing a more stable relationship between [[money supply]] and macroeconomic variables. |
||
Building on this foundation, Chen and Valcarcel expanded the application of Divisia aggregates, highlighting their ability to account for the liquidity services of monetary components and provide richer insights into monetary policy transmission. Their inclusion of Divisia aggregates in SVAR models has consistently mitigated the price puzzle in both historical and modern samples, reinforcing their value as robust tools in monetary economics.<ref name="Chen2021" /> |
|||
===Rational Expectations Augmented SVAR (RE-SVAR)=== |
|||
The RE-SVAR methodology developed by Chen and Valcarcel incorporates forward-looking rational expectations directly into the SVAR framework. This method addresses the puzzle by aligning the model's assumptions with the forward-looking nature of monetary policy. By comparing interest rate rules with money growth rules, RE-SVAR demonstrates that models using Divisia aggregates are more robust in capturing monetary shocks without generating puzzling price responses.<ref name="Chen2025" /> |
|||
== References == |
== References == |
||
Latest revision as of 07:05, 30 December 2024
The price puzzle is a phenomenon in monetary economics observed within structural vector autoregression (SVAR) models. It refers to the counterintuitive result where a contractionary monetary policy shock—typically modeled as an increase in short-term interest rates—is followed by an increase, rather than a decrease, in the price level. This anomaly challenges conventional macroeconomic theories that predict a decline in prices as monetary tightening reduces aggregate demand.
Historical Context
[edit]The term "price puzzle" was first introduced by Lawrence Christiano in 1992,[1] who observed this anomaly in SVAR models analyzing U.S. monetary policy. Early studies found that when using short-term interest rates, such as the federal funds rate, as the primary indicator of monetary policy, SVAR models often produced results inconsistent with theoretical expectations. This sparked a series of investigations into the limitations of these models and the underlying causes of the puzzle.
Efforts to Resolve the Price Puzzle
[edit]Augmented Information Sets
[edit]One approach to resolving the price puzzle involves expanding the information set in SVAR models. For instance, including variables like commodity prices or Federal Reserve forecasts (e.g., Greenbook data) can provide additional context for policy decisions, reducing the puzzle's prevalence.[2][3]
Improved Identification Strategies for Monetary Policy Shocks
[edit]High-Frequency Identification (HFI)
[edit]High-frequency identification exploits financial market reactions in narrow windows around monetary policy announcements (Gertler and Karadi, 2015[4]; Nakamura and Steinsson, 2018[5]). This approach leverages the fact that movements in financial instruments (like federal funds futures) during a tight window around Federal Open Market Committee (FOMC) announcements are likely driven by monetary policy news rather than other macroeconomic factors.
Sign Restrictions
[edit]Uhlig (2005)[6] pioneered the use of sign restrictions in monetary policy SVARs. This approach imposes theoretically motivated restrictions on impulse responses while remaining agnostic about the response of key variables of interest (like prices). Modern applications often combine sign restrictions with other identifying assumptions:
- Narrative restrictions (Antolín-Díaz and Rubio-Ramírez, 2018[7])
- Zero restrictions (Arias et al., 2019[8])
- Long-run restrictions (Matthes and Schwartzman, 2019[9])
New Explanation: Cost Channel of Monetary Policy
[edit]One prominent explanation is the cost channel of monetary transmission [10]. Higher interest rates increase firms' borrowing costs for working capital (used to pay wages and intermediate inputs), which can lead to higher production costs that are passed on to consumers in the form of higher prices, at least in the short run [11]. The existence of this channel has important implications for the conduct of optimal monetary policy [12].
Divisia Monetary Aggregates
[edit]The study of Divisia monetary aggregates as superior policy indicators has its roots in the work of Keating et al.[13] and Belongia and Ireland,[14] who emphasized the importance of incorporating broad monetary aggregates into economic models to better understand monetary policy effects. Their research demonstrated that Divisia aggregates outperform traditional simple-sum measures, such as M1 and M2, by resolving anomalies like the price puzzle and establishing a more stable relationship between money supply and macroeconomic variables.
References
[edit]- ^ Christiano, Lawrence J. (1992). "Investigations of monetary policy rules". Carnegie-Rochester Conference Series on Public Policy. 41: 151–195. doi:10.1016/0167-2231(94)90010-8. ISSN 0167-2231.
- ^ Christiano, Lawrence J.; Eichenbaum, Martin; Evans, Charles L. (1999). "Monetary policy shocks: What have we learned and to what end?". Handbook of Macroeconomics. 1: 65–148. doi:10.1016/S1574-0048(99)01005-8. ISSN 1574-0048.
- ^ Romer, Christina D.; Romer, David H. (2004). "A new measure of monetary shocks: Derivation and implications". American Economic Review. 94 (4): 1055–1084. doi:10.1257/0002828042002651. ISSN 0002-8282.
- ^ Gertler, Mark; Karadi, Peter (2015). "Monetary Policy Surprises, Credit Costs, and Economic Activity". American Economic Journal: Macroeconomics. 7 (1): 44–76. doi:10.1257/mac.20130329. ISSN 1945-7707.
- ^ Nakamura, Emi; Steinsson, Jón (2018). "High-Frequency Identification of Monetary Non-Neutrality: The Information Effect". The Quarterly Journal of Economics. 133 (3): 1283–1330. doi:10.1093/qje/qjy004. ISSN 0033-5533.
- ^ Uhlig, Harald (2005). "What are the effects of monetary policy on output? Results from an agnostic identification procedure". Journal of Monetary Economics. 52 (2): 381–419. doi:10.1016/j.jmoneco.2004.05.007. ISSN 0304-3932.
- ^ Antolín-Díaz, Juan; Rubio-Ramírez, Juan F. (2018). "Narrative Sign Restrictions for SVARs". American Economic Review. 108 (10): 2802–2829. doi:10.1257/aer.20161852. hdl:10419/172913. ISSN 0002-8282.
- ^ Arias, Jonas E.; Rubio-Ramírez, Juan F.; Waggoner, Daniel F. (2019). "Inference in Bayesian Proxy-SVARs". Journal of Econometrics. 208 (2): 613–633. doi:10.1016/j.jeconom.2018.09.016. hdl:10419/162505. ISSN 0304-4076.
- ^ Matthes, Christian; Schwartzman, Felipe (2019). "What Do Sectoral Dynamics Tell Us About the Origins of Business Cycles?". Journal of Monetary Economics. 104: 67–82. doi:10.1016/j.jmoneco.2019.03.008. ISSN 0304-3932.
- ^ Barth, Marvin J.; Ramey, Valerie A. (2001). "The Cost Channel of Monetary Transmission". NBER Macroeconomics Annual. 16: 199–240. doi:10.1086/654443. ISSN 0889-3365.
- ^ Christiano, Lawrence J.; Eichenbaum, Martin; Evans, Charles L. (2005). "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy". Journal of Political Economy. 113 (1): 1–45. doi:10.1086/426038. ISSN 0022-3808.
- ^ Ravenna, Federico; Walsh, Carl E. (2006). "Optimal monetary policy with the cost channel". Journal of Monetary Economics. 53 (2): 199–216. doi:10.1016/j.jmoneco.2005.01.004. ISSN 0304-3932.
- ^ Keating, John W.; Kelly, L.J.; Smith, A.L.; Valcarcel, Victor J. (February 2019). "A model of monetary policy shocks for financial crises and normal conditions". Journal of Money, Credit and Banking. 51 (1): 227–259. doi:10.1111/jmcb.12510.
- ^ Belongia, Michael T.; Ireland, Peter N. (2014). "The Barnett critique after three decades: A new Keynesian analysis". Journal of Econometrics. 183 (1): 5–21. doi:10.1016/j.jeconom.2014.06.008. hdl:10419/101887. ISSN 0304-4076.