Abstract
The onset of the COVID-19 pandemic in the United States may have led investors or other individuals to expect sharp drops in output and rising prices, as well as drastic changes in fiscal and/or monetary to deal with the crisis. This paper analyses the co-movement between expected inflation and interest in the U.S. by using a battery of wavelet tools over the period from January 21, 2020 to March 28, 2022. Wavelet methods are used to examine the linkages between expected inflation and nominal interest rates of varying terms, focusing on the direction of co-movement and their sub-time horizons. Both bivariate wavelet and partial wavelet models that incorporate daily COVID-19 case counts or a financial stress variable find that the relationship holds primarily in the longer short-run (more than 6 months), with connections stronger for maturities of 5 years than for 1 year or less. The expectation related to the ‘inflation–interest rate’ nexus and type of bond maturity seem to be significantly shaped by the pandemic peak and anticipated duration of the disease. More precisely, the longer the anticipated duration of the pandemic is, the higher the expected inflation rate, bond yield rate, and maturity are. The interaction between expected inflation and interest seems to be very sensitive to pandemic and financial stress in terms of lead-lag status, in the very short to short-run, for 5 and 10 years bond maturity. This seems to be explained by investor hazard to a new particular unknown stimulus caused by the pandemic and its socio-economic consequences.
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Source: performed based on FRED (2022) and New York Times (2022) databases, accessed in May 2022, from https://fred.stlouisfed.org/ and https://github.com/nytimes/covid-19-data
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Notes
The time-horizons are purely conventional, the used terms—very short, short, medium short and longer short-runs—being constructed just to facilitate the interpretation of co-movements over daily sub-periods of time.
The World Health Organization (WHO) officially declared the COVID-19 as a world pandemic in March 11, 2020.
References
Adrian T (2022) Financial stability risks grow as war complicates push to contain inflation. IMF Blog. April 19, 2022. https://www.imf.org/en/Blogs/Articles/2022/04/19/blog-gfsr-financial-stability-risks-grow-as-war-complicates-push-to-contain-inflation
Aguiar-Conraria L, Azevedo N, Soares MJ (2008) Using wavelets to decompose the time-frequency effects of monetary policy. Physica A 387:2863–2878
Aguiar-Conraria L, Martins M, Soares MJ (2023) The Phillips curve at 65: time for time and frequency. J Econ Dyn Control 151:104620
Albulescu CT, Tiwari AK, Miller SM, Gupta R (2019) Time–frequency relationship between US inflation and inflation uncertainty: evidence from historical data. Scott J Political Econ 66:673–702
Alola AA, Alola UV, Sarkodie SA (2021) The nCOVID-19 and financial stress in the USA: health is wealth. Environ Dev Sustain 23:9367–9378
Beirne J, Renzhi N, Sugandi E, Volz U (2021) COVID-19, asset markets and capital flows. Pac Econ Rev 26(4):498–538
Binder C (2020) Coronavirus fears and macroeconomic expectations. Rev Econ Stat 102(4):721–730
Bismut C, Ramajo I (2021) Nominal and real interest rates in OECD countries, changes in sight after covid-19? IEEP 18:493–516
Del Rio-Chanona RM, Mealy P, Pichler A, Lafond F, Farmer JD (2020) Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective. Oxford Rev Econ Policy. https://doi.org/10.1093/oxrep/graa033
Detmers G-A, Ho S-J, Karagedikli Ö (2022) Understanding consumer inflation expectations during the COVID-19 Pandemic. Aust Econ Rev 55(1):141–154
Ebrahimy E, Igan D, Peria SM (2020) The impact of COVID-19 on inflation: potential drivers and dynamics. IMF Special Notes Series on COVID-19, September 10, 2020
Farge M (1992) Wavelet transforms and their applications to turbulence. Annu Rev Fluid Mech 24:395–457
Finck D, Tillmann P (2022) Pandemic shocks and household spending. Oxford Bull Econ Stat 84:273–299
Foster, (1979) Interest rates and inflation expectations: the British experience. Oxford Bull Econ Stat 41(2):145–164
FRED (2022) Federal reserve economic data. Federal Reserve Bank of St. Louis, online database, accessed in May, 2022
Galati G, Poelhekke S, Zhou C (2011) Did the crisis affect inflation expectations? Int J Cent Bank 7(1):167–207
Goodhart C, Pradhan M (2020) Future imperfect after coronavirus. VoxEU.org, 27 March. https://cepr.org/voxeu/columns/future-imperfect-after-coronavirus
Grinsted A, Moore SJ, Jevrejeva C (2004) Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Process Geophys 11:561–566
Mensi W, Rehman MU, Al-Yahyaee KH (2020) Time-frequency co-movements between oil prices and interest rates: evidence from a wavelet-based approach. North American J Econ Finance 51:100836
Mihanović H, Orlić M, Pasrić Z (2009) Diurnal thermocline oscillations driven by tidal flow around an island in the Middle Adriatic. J Mar Syst 78:S157–S168
Mutascu M (2018) A time-frequency analysis of trade openness and CO2 emissions in France. Energy Policy 115:443–455
New York Times (2022) New York Times Coronavirus (Covid-19) data in the United States. Accessed in May 2022, from https://github.com/nytimes/covid-19-data.
Ng EKW, Chan JKL (2012) Geophysical applications of partial wavelet coherence and multiple wavelet coherence. J Atmos Oceanic Tech 29:1845–1853
OFR (2022) OFR index. Office of Financial Research online dataset. U.S Department of the Treasury. accessed in May, 2022
Percival D, Walden A (2000) Wavelet methods for time series analysis. Cambridge University Press
Rua A (2010) Measuring comovement in the time–frequency space. J Macroecon 32(2):685–691
Santacreu AM, LaBelle J (2022) Supply chain disruptions and inflation during COVID-19. Econ Synop. https://doi.org/10.20955/es.2022.11
Si D-K, Li X-L, Ge X (2020) On the link between the exchange rates and interest rate differentials in China: evidence from an asymmetric wavelet analysis. Empirical Economics 59(6):2925–2946
Söderlind P (1998) Nominal interest rates as indicators of inflation expectations. Scand J Econ 100(2):457–472
Tiwari A, Mutascu M, Andries A (2013) Decomposing time-frequency relationship between producer price and consumer price indices in Romania through wavelet analysis. Econ Model 31:151–159
Torrence DC, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteor Soc 79:61–78
Torrence C, Webster PJ (1999) Interdecadal changes in the ENSO–monsoon system. J Clim 12:2679–2690
Ueda K (2009) Determinants of households’ inflation expectations, Bank of Japan Institute for Monetary and Economic Studies Discussion Paper No. 2009-E-8
Van der Wielen W, Barrios S (2021) Economic sentiment during the COVID pandemic: evidence from search behaviour in the EU. J Econ Bus 115:105970
Xu Y (2019) The anchoring of inflation expectations in time and frequency domains. Econ Res-Ekonomska Istraživanja 32(1):2044–2062
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Mutascu, M.I., Hegerty, S.W. Expected inflation and interest-rate dynamics in the COVID era: evidence from the time–frequency domain. Empirica 51, 559–582 (2024). https://doi.org/10.1007/s10663-024-09610-6
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DOI: https://doi.org/10.1007/s10663-024-09610-6