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Ignorance, Uncertainty, and Strategic Consumption-Portfolio Decisions
The University of Hong Kong Jun Nie†
Federal Reserve Bank of Kansas City
Shanghai University of Finance and Economics.
September 4, 2017
This paper constructs a recursive utility version of a canonical Merton (1971) model with
uninsurable labor income and unknown income growth to study how the interaction between
two types of uncertainty due to ignorance affects strategic consumption-portfolio rules and pre-
cautionary savings. Specifically, after solving the model explicitly, we theoretically and quanti-
tatively explore (i) how these ignorance-induced uncertainties interact with intertemporal sub-
stitution, risk aversion, and the correlation between the equity return and labor income, and
(ii) how they jointly affect strategic asset allocation, precautionary savings, and the equilibrium
asset returns. Furthermore, we use data to test our model’s predictions on the relationship be-
tween ignorance and asset allocation and quantitatively show that the interaction between the
two types of uncertainty is the key to explain the data. Finally, we find that the welfare costs of
ignorance can be very large.
JEL Classification Numbers: C61, D81, E21.
Keywords: Ignorance, Unknown Income Growth, Induced Uncertainty, Strategic Asset Al-
∗Faculty of Business and Economics, University of Hong Kong, Hong Kong. Email address: firstname.lastname@example.org. †Research Department, Federal Reserve Bank of Kansas City, U.S.A. E-mail: email@example.com. ‡School of Mathematics and Shanghai Key Laboratory of Financial Information Technology, Shanghai University of
Finance and Economics, China. E-mail: firstname.lastname@example.org
Though most macroeconomic and financial models assume agents have a good understanding of
the economic model they use to make optimal decisions, in reality, many ordinary investors/consumers
are ignorant about details of the economic model including the structure and parameters of the
model and the current state of the model economy their decisions are based on. For example,
van Rooija, Lusardic, and Alessie (2010) found that some households do not know the basics of
risk diversification when making financial decisions.1 Mitchell and Lusardi (2014), in a carefully
designed survey, found that many respondents, not only in the US but around the world, lack fi-
nancial literacy, meaning that they do not have necessary skills and knowledge that allows them to
make informed and effective investment decisions.2 Brennen (1998) argued that investors have in-
complete information about the investment opportunity set. Guvenen (2007) empirically showed
that individuals may not have complete information about their own income growth.3
Different types of uncertainty arise due to ignorance about different aspects of the economic
model. In a recent paper, Hansen and Sargent (2015) argued that ignorance provides a useful
way to summarize different types of uncertainty through specifying the details the decision maker
is ignorant about. They examined the implications of ignorance using a simple Friedman (1953)
model to “fine tune" an economy. Specifically, they discussed two types of ignorance: (i) the agent
is ignorant about the conditional distribution of the next period’s state and (ii) the agent is ignorant
about the probability distribution of a response coefficient (a parameter) in an otherwise fully
trusted specification of the conditional distribution of the next period’s state. In other words, the
first type of ignorance represents model uncertainty (or MU) as the agent does not know the shock
distribution, while the second type of ignorance represents parameter uncertainty (or PU) as the
agent does not know model parameters. Bernanke (2007) also argued that policy-makers usually
care about the uncertainty about the structure of the economy (including both the transmission
mechanism of monetary policy and the model parameters) and the current state of the economy.
Inspired by Hansen and Sargent (2015), in this paper we build a recursive utility version of
a basic Merton (1971) model to study the implications of these two major types of ignorance
for intertemporal consumption-saving-asset allocation, a fundamental topic in modern macroeco-
nomics and finance. Our central goal is to provide a unified framework to study how the two major
types of ignorance (or the two types of uncertainty induced by ignorance: parameter uncertainty
and model uncertainty) interact with each other and then affect the optimal consumption-saving-
1They used the De Nederlandsche Bank (DNB) Household Survey data to study the relationship between financial literacy and stock market participation, and found that financial literacy affects financial decision-making: Those with low literacy are much less likely to invest in stocks.
2It is not surprising that people who get any of the three simple questions wrong are unlikely to master more chal- lenging investment strategies.
3Although both the expected mean of labor income growth and that of the equity return are crucial for financial decision-making, they are not known a priori and are usually estimated with errors.
portfolio decisions as well as the equilibrium asset returns. Specifically, we construct a continuous-
time Merton (1971)-Wang (2009) type model with uninsurable labor income and unknown income
growth in which the investors have recursive utility and a preference for robustness. In our recur-
sive utility framework, we also disentangle two distinct aspects of preferences: the agent’s elas-
ticity of intertemporal substitution (EIS; attitudes towards variation in consumption across time)
with the coefficient of absolute risk aversion (CARA; attitudes towards variation in consumption
across states), which are shown to have different roles in driving consumption saving and portfolio
choice decisions.4 As explained below, our model delivers not only rich theoretical results but also
Hansen and Sargent (1995) first discussed how to model MU due to the preference for robust-
ness (RB) within the linear-quadratic-Gaussian (LQG) economic framework.5 In robust control
problems, agents are concerned about the possibility that their true model is misspecified in a
manner that is difficult to detect statistically; consequently, they make their decisions as if the
subjective distribution over shocks was chosen by an evil agent to minimize their utility.6 In addi-
tion, many empirical and experimental studies have repeatedly supported that individual agents
are ambiguity averse (or have robustness preferences). For example, Ahn, Choi, Gale, and Kariv
(2014) used a rich experiment data set to estimate a portfolio-choice model and found that about 40
percent of subjects display either statistically significant pessimism or ambiguity aversion. Bhan-
dari, Borovička, and Ho (2016) identified ambiguity shocks using survey data, and showed that in
the data, the ambiguity shocks are an important source of variation in labor market variables. It is
worth noting that canonical consumption-portfolio choice models with uninsurable labor income
generally do not distinguish between risk and uncertainty, but emphasize the key role of uncer-
tainty about future labor income in determining optimal consumption-saving-portfolio decisions.
(See, e.g., Viceira 2001 and Wang 2009.)
In our model economy, ordinary investors not only have incomplete information about income
growth but also are concerned about the model misspecification. In other words, they face both
parameter uncertainty and model uncertainty. Compared with the full-information case in which
income growth is known, parameter uncertainty due to unknown income growth creates an addi-
tional demand for robustness. Furthermore, in the standard problem with parameter uncertainty,
4Constant-relative-risk-aversion (CRRA) utility functions are more common in macroeconomics, mainly due to balanced-growth requirements. CRRA utility would greatly complicate our analysis because the intertemporal con- sumption model with CRRA utility and stochastic labor income has no explicit solution and leads to non-linear con- sumption rules. See Kasa and Lei (2017) for a recent application of RB in a continuous-time Blanchard-Yaari model with CRRA utility and wealth hetergeneity.
5See Hansen and Sargent (2007) for a textbook treatment on robustness and model uncertainty. 6There are three main ways to model ambiguity and robustness in the literature: the multiple priors model (Gilboa
and Schmeidler 1989), the “smooth ambiguity” model (Klibanoff, Marinacci, and Mukerji 2005), and the multiplier util- ity and robust control/filtering model (Hansen and Sargent 2001). See Epstein and Schneider (2010) for a recent review on this topic. In this paper, we follow the line of Hansen and Sargent to introduce robustness and model uncertainty into our model.
the agent combines a pre-specified prior over the parameter with the observable variables to con-
struct the perceived value of the parameter, and is assumed to have onl