The Term Structure of Inflation Expectations

Chernov, Mikhail; Mueller, Philippe

doi:10.2139/ssrn.1101513

Cited by 119 publications

(166 citation statements)

References 40 publications

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“…Duffee (2011a) takes a different approach, using only yields and filtering to infer the presence of a hidden factor. Both conclude hidden factors are important, as do Chernov and Mueller (2011), who use survey expectations of inflation to reveal such a factor. This preliminary evidence indicates that hidden factor models should be taken seriously.…”

Section: Hidden Factorsmentioning

confidence: 98%

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Forecasting Interest Rates

Duffee

2013

Handbook of Economic Forecasting

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This chapter discusses what the asset-pricing literature concludes about the forecastability of interest rates. It outlines forecasting methodologies implied by this literature, including dynamic, no-arbitrage term structure models and their macro-finance extensions. It also reviews the empirical evidence concerning the predictability of future yields on Treasury bonds and future excess returns to holding these bonds. In particular, it critically evaluates theory and evidence that variables other than current bond yields are useful in forecasting. Terms of use: Documents in EconStor mayJEL Code: G12

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Section: Hidden Factorsmentioning

confidence: 98%

“…An extension of this approach is Chernov and Mueller (2011). They include both observed inflation and survey expectations of future inflation to a yields-only model.…”

Section: Survey Datamentioning

confidence: 99%

Forecasting Interest Rates

Duffee

2013

Handbook of Economic Forecasting

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“…In order to gauge the relationship between additional macro variables and our latent factors, we correlate them with two measures of liquidity (AAA credit spread and the growth rate of the money zero maturity 5 More recent works by Chernov and Mueller (2008) and Mueller (2008) provide further examples of applications of our methodology. 6 In fact, there is currently a debate in the literature over this point.…”

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confidence: 99%

No-arbitrage macroeconomic determinants of the yield curve

Bikbov

Chernov

2010

Journal of Econometrics

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116

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To cite this version:Ruslan Bikbov, Mikhail Chernov. No-arbitrage macroeconomic determinants of the yield curve. Journal of Econometrics, Elsevier, 2010, 159 (1) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T AbstractNo-arbitrage macro-finance models use variance decompositions to gauge the extent of association between the macro variables and yields. We show that results generated by this approach are sensitive to the order of variables in the recursive identification scheme. In a four-factor model, one may obtain 18 different sets of answers out of 24 possible. We propose an alternative measure that is based on levels of macro variables as opposed to shocks. We account for the correlation between the macro and latent factors via projection of the latter onto the former. As a result, the association between macro variables and yields can be computed uniquely via an R 2 . Macro variables explain 80% of the variation in the short rate and 50% of the slope, and 54% to 68% of the term premia.JEL Classification: C13; C22; G12

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“…More recently, several studies have explored similar questions using no-arbitrage dynamic term structure models (e.g., Ang and Piazzesi (2003), Ang, Piazzesi, and Wei (2006), Diebold, Rudebusch, and Aruoba (2006), Duffee (2006), Hördahl, Tristani, and Vestin (2006), Moench (2008), Diebold, Piazzesi, and Rudebusch (2005), Piazzesi (2005), Rudebusch and Wu (2008)). Other contributions have extended these models to include market expectation in the form of survey forecasts (e.g., Chernov and Mueller (2008), Chun (2010), and Kim and Orphanides (2005)). …”

Section: Related Literaturementioning

confidence: 99%