Abstract
State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the state variables. In this chapter, we show that a classic linear-quadratic macroeconomic framework which incorporates two new assumptions can be analytically solved and explicitly mapped to a state-space representation. The two assumptions we consider are the model uncertainty due to concerns for model misspecification (robustness) and the state uncertainty due to limited information constraints (rational inattention). We show that the state-space representation of the observable and unobservable can be used to quantify the key parameters on the degree of model uncertainty. We provide examples on how this framework can be used to study a range of interesting questions in macroeconomics and international economics.
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Notes
- 1.
Note that here “linear” means that the state transition equation is linear, “quadratic” means that the objective function is quadratic, and “Gaussian” means that the exogenous innovation is Gaussian.
- 2.
We take a small-open economy version of Hall’s model as we’ll use it to address some small-open economy issues in later sectors.
- 3.
It is worth noting that the tax-smoothing hypothesis (TSH) model is an analogy with the permanent income hypothesis (PIH) model in which consumers smooth consumption over time; tax rates respond to permanent changes in the public budgetary burden rather than transitory ones.
- 4.
- 5.
- 6.
n is assumed to be constant.
- 7.
Formally, this setup is a game between the decision-maker and a malevolent nature that chooses the distortion process ν t . \(\vartheta \geq 0\) is a penalty parameter that restricts attention to a limited class of distortion processes; it can be mapped into an entropy condition that implies agents choose rules that are robust against processes which are close to the trusted one. In a later section we will apply an error detection approach to calibrate \(\vartheta\).
- 8.
This will be clearer when we go to the applications in later sections.
- 9.
More generally, agents choose the joint distribution of consumption and current permanent income subject to restrictions about the transition from prior (the distribution before the current signal) to posterior (the distribution after the current signal). The budget constraint implies a link between the distribution of consumption and the distribution of next period permanent income.
- 10.
We regard κ as a technological parameter. If the base for logarithms is 2, the unit used to measure information flow is a “bit,” and for the natural logarithm e the unit is a “nat.” 1 nat is equal to log2 e ≈ 1. 433 bits.
- 11.
Convergence requires that \(\kappa >\log \left (R\right ) \approx R - 1\); see Luo and Young (2010) for a discussion.
- 12.
Luo, Nie, and Young (2011a) use this approach to study the joint dynamics of consumption, income, and the current account.
- 13.
- 14.
See Luo, Nie, and Young (2011a) for the proof.
- 15.
It is worth noting that the special case that λ = 1 can be viewed as a representative-agent model in which we do not need to discuss the aggregation issue.
- 16.
For example, Backus, Kehoe, and Kydland (1992) solve a two-country real business cycles model and argue that the puzzle that empirical consumption correlations are actually lower than output correlations is the most striking discrepancy between theory and data.
- 17.
For example, Muth (1960), Lucas (1972), Morris and Shin (2002), and Angeletos and La’O (2009). It is worth noting that this assumption is also consistent with the rational inattention idea that ordinary people only devote finite information-processing capacity to processing financial information and thus cannot observe the states perfectly.
- 18.
See Luo (2008) for details about the welfare losses due to information imperfections within the partial equilibrium permanent income hypothesis framework.
- 19.
Note that θ measures how much uncertainty about the state can be removed upon receiving the new signals about the state.
- 20.
Note that when θ = 1, \(var\left [\xi _{t+1}\right ] = 0\).
- 21.
- 22.
See Luo and Young (2010) for details about the welfare losses due to imperfect observations in the RB model; they are uniformly small.
- 23.
- 24.
For the examples of the model equations describing the inflation and output dynamics in a closed economy, see Leitemo and Soderstrom (2008a).
- 25.
This includes the two versions of the model presented in previous sections which incorporates the model uncertainty due to RB: one uses the regular Kalman filter; the other one assumes that the agent does not trust the Kalman filter either (robust filtering).
- 26.
Formally, one can view risk-sensitive agents as ones who have non-state-separable preferences, as in, but with a value for the intertemporal elasticity of substitution equal to one.
- 27.
Note that the OE becomes
$$\displaystyle{ \frac{\alpha \theta } {1 -\left (1-\theta \right ){R}^{2}} = \frac{1} {2\vartheta }, }$$if we assume that the agents distrust the income process hitting the budget constraint, but trust the RI-induced noise hitting the Kalman filtering equation.
- 28.
HST (1999) derive the observational equivalence result by fixing all parameters, including R, except for the pair \(\left (\alpha,\boldsymbol{\eta }\right )\).
- 29.
As shown in HST (1999), the two models have different implications for asset prices because continuation valuations would alter as one alters \(\left (\alpha,\boldsymbol{\eta }\right )\) within the observationally equivalent set of parameters.
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Acknowledgments
Luo thanks the Hong Kong GRF under grant No. 748209 and 749510 and HKU seed funding program for basic research for financial support. All errors are the responsibility of the authors. The views expressed here are the opinions of the authors only and do not necessarily represent those of the Federal Reserve Bank of Kansas City or the Federal Reserve System.
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Appendix
Appendix
1.1 Solving the Current Account Model Explicitly Under Model Uncertainty
To solve the Bellman equation (4.7), we conjecture that
where A, B, and C are undetermined coefficients. Substituting this guessed value function into the Bellman equation gives
We can do the min and max operations in any order, so we choose to do the minimization first. The first-order condition for ν t is
which means that
Substituting (4.49) back into (4.48) gives
where
The first-order condition for c t is
Using the solution for ν t the solution for consumption is
Substituting the above expressions into the Bellman equation gives
Given η R = 1, collecting and matching terms, the constant coefficients turn out to be
Substituting (4.51) and (4.52) into (4.50) yields the consumption function. Substituting (4.53) into the current account identity and using the expression for s t yields the expression for the current account.
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Luo, Y., Nie, J., Young, E.R. (2013). Model Uncertainty, State Uncertainty, and State-Space Models. In: Zeng, Y., Wu, S. (eds) State-Space Models. Statistics and Econometrics for Finance, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7789-1_4
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