Estimating Sentiment and Risk in a Consumption Model: A Factor Analysis Approach

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This empirical paper deals with the impacts of sentiment about the future, short-run risk, and long-run risk in a dynamic economic model of optimal consumption decisions with recursive preferences. The empirical strategy combines both a latent factor method and a democratic orthogonalization technique. The latent factor method is applied to a large database of macroeconomic indicators and a democratic orthogonalization technique is used to separate the relative importance of sentiment about the future and long-run risk channels in shaping optimal consumption decisions. This offers the opportunity to exploit a data rich information base in assessing uncertainty shocks and changes in the dynamics of the state of the economy over time. The empirical results suggest that consumers with recursive preferences are not indifferent to long-run uncertainty shocks to future consumption prospects. Endogenous consumption variations are driven by a multi-component mechanism, where on average the sentiment component accounts for 15.33%, the short-run risk accounts for 16.89%, and the long-run risk pertains to 34.51%. This suggests that channels of economic decisions relating to sentiment about the future and broader attitudes towards short-run and long-run risks are important features to be considered in analyzing dynamic stochastic economic models with recursive utility.


Type of resource text
Date created July 28, 2021


Author Bouaddi, Mohammed
Author Kakeu, Johnson
Organizer of meeting Judd, Kenneth
Organizer of meeting Pohl, Walter
Organizer of meeting Schmedders, Karl
Organizer of meeting Wilms, Ole


Subject optimal consumption,
Subject recursive utility
Subject sentiment
Subject short-run risk
Subject long-run risk
Subject latent factor analysis approach
Subject democratic orthogonalization
Genre Text
Genre Working paper
Genre Grey literature

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Bouaddi, M. and Kakeu, J. (2022). Estimating Sentiment and Risk in a Consumption Model: A Factor Analysis Approach. Stanford Digital Repository. Available at


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