Belief Distortions and Macroeconomic Fluctuations

Bianchi, Francesco; Ludvigson, Sydney C.; Ma, Stefan

doi:10.2139/ssrn.3632633

Cited by 7 publications

(12 citation statements)

References 59 publications

(83 reference statements)

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“…As we discuss below, the combination of these two features of investor beliefs (learning plus a fading memory distortion) implies that asset prices in the model respond to monetary policy regime changes by initially underreacting but eventually overreacting. These features of beliefs imply that the model is qualitatively consistent with independent empirical evidence showing that survey expectations—including those of professional forecasters—initially underreact to shocks but subsequently overreact (Angeletos, Huo, and Sastry (2020), Bianchi, Ludvigson, and Ma (2020)).…”

Section: A Macrofinance Model Of Monetary Transmissionsupporting

confidence: 80%

Monetary Policy and Asset Valuation

Bianchi¹,

Lettau²,

Ludvigson³

2022

The Journal of Finance

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We document large, longer-term, joint regime shifts in asset valuations and the real federal funds rate-r* spread. To interpret these findings, we estimate a novel macro-finance model of monetary transmission and find that the documented regimes coincide with shifts in the parameters of a policy rule, with long-term consequences for the real interest rate. Estimates imply that two-thirds of the decline in the real interest rate since the early 1980s is attributable to regime changes in monetary policy. The model explains how infrequent changes in the monetary policy stance can generate persistent changes in asset valuations and the equity premium.

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Section: A Macrofinance Model Of Monetary Transmissionsupporting

confidence: 80%

Monetary Policy and Asset Valuation

Bianchi¹,

Lettau²,

Ludvigson³

2022

The Journal of Finance

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“…For our purposes, these results motivate our interest in centering estimates of the term structure of expectations and uncertainty around the SPF. We acknowledge, however, that Bianchi, Ludvigson, and Ma (2022) have recently provided evidence that machine learning methods show more capability to improve on the accuracy of survey-based forecasts.…”

Section: Forecast Performance Without Mds Assumptionmentioning

confidence: 99%

“…In this spirit, Mertens and Nason (2020) propose a new unobserved components model of inflation that distinguishes trend and inflation gap components and features a sticky information forecast mechanism; their estimates reveal that the stickiness of survey forecasts is not invariant to the time series process governing actual inflation. Using machine learning algorithms, Bianchi, Ludvigson, and Ma (2022) find evidence of time-varying bias in survey expectations and forecasts and conclude that artificial intelligence can be used to improve forecast accuracy. Regarding the predictive value of density forecasts collected by surveys, Clements (2018), as well as Glas and Hartmann (2022) and others, points to potential shortcomings, for example, due to rounding of answers by respondents.…”

Section: Related Literaturementioning

confidence: 99%

“…Third, we develop an extended version of the baseline model that allows for bias in observed SPF forecasts, resulting in persistent forecast errors. Time-varying bias in SPF forecasts has been highlighted in studies including Bianchi, Ludvigson, and Ma (2022), Coibion and Gorodnichenko (2015), and Farmer, Nakamura, and Steinsson (2022). Our extended model treats SPF predictions as unbiased martingales only in its prior, while allowing for generic VAR dynamics of SPF forecasts in its posterior.…”

Section: Introductionmentioning

confidence: 99%

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Constructing fan charts from the ragged edge of SPF forecasts

Clark

Ganics

Mertens

2022

Working Paper (Federal Reserve Bank of Cleveland)

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We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicates a given data set of predictions from the SPF (or a similar forecast source) without measurement error. Our model captures fixed horizon and fixed-event forecasts, and can accommodate changes in the maximal forecast horizon available from the SPF. The model casts a decomposition of multi-period forecast errors into a sequence of forecast updates that may be partially unobserved, resulting in a multivariate unobserved components model. In our empirical analysis, we provide quarterly term structures of expectations and uncertainty bands. Our preferred specification features stochastic volatility in forecast updates, which improves forecast performance and yields model estimates of forecast uncertainty that vary over time. We conclude by constructing SPF-based fan charts for calendar-year forecasts like those published by the Federal Reserve.

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“…The high predictive value of SPF point forecasts is well documented. While quite a few studies point to some persistence in SPF forecast errors (e.g., Coibion & Gorodnichenko (2015) and Bianchi et al (2022)), there is consensus that SPF point forecasts are hard to beat in real-time accuracy (e.g., Ang et al (2007), Croushore (2010), Faust & Wright (2013), and Croushore & Stark (2019)).…”

Section: Introductionmentioning

confidence: 99%

What is the predictive value of SPF point and density forecasts?

Clark

Ganics

Mertens

2022

Working Paper (Federal Reserve Bank of Cleveland)

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This paper presents a new approach to combining the information in point and density forecasts from the Survey of Professional Forecasters (SPF) and assesses the incremental value of the density forecasts. Our starting point is a model, developed in companion work, that constructs quarterly term structures of expectations and uncertainty from SPF point forecasts for quarterly fixed horizons and annual fixed events. We then employ entropic tilting to bring the density forecast information contained in the SPF’s probability bins to bear on the model estimates. In a novel application of entropic tilting, we let the resulting predictive densities exactly replicate the SPF’s probability bins. Our empirical analysis of SPF forecasts of GDP growth and inflation shows that tilting to the SPF’s probability bins can visibly affect our model-based predictive distributions. Yet in historical evaluations, tilting does not offer consistent benefits to forecast accuracy relative to the model-based densities that are centered on the SPF’s point forecasts and reflect the historical behavior of SPF forecast errors. That said, there can be periods in which tilting to the bin information helps forecast accuracy. Replication files are available at https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts

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Belief Distortions and Macroeconomic Fluctuations

Cited by 7 publications

References 59 publications

Monetary Policy and Asset Valuation

Monetary Policy and Asset Valuation

Constructing fan charts from the ragged edge of SPF forecasts

What is the predictive value of SPF point and density forecasts?

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