Thank you for the comment. State-dependent pricing is really what we somehow had in mind while applying Markov-Switching for price stickiness. In fact, even our interpretation of the results is based on menu cost.

While state-dependent pricing is theoretically appealing, I have not seen any empirical DSGE using it. Let me refer to the Dotsey-King-Wolman rigidity. The full state-dependent Phillips curve contains infinite number of inflation lags as well as unobservables (e.g. the price vintages deviations from the steady-state) [see Bakhshi, Khan, & Rudolf 2007, JME]. Moreover, we have both price and wage rigidity (and we think that switching both of these is the main paper’s contribution). I imagine, including state-dependent price and wages is really tough job. ]]>

Thanks for picking up our paper. You’re absolutely right that there are many interest rates out there and many other factors influence them. We like to think of demographics as a common factor that works at low frequency. We are doing empirical work to check if that prediction can be teased out of the data. ]]>

1) Linear approximation is for every system of equations. It is just that the approximation error has been shown to be unacceptably large for many economic models. The nonlinearities in the dynamics matter.

2) Further, many models have multiple stationary equilibria or have instabilities such as unbalanced growth or a more fundamental instability such as unsustainable fiscal policy. Standard methods cannot even obtain a solution in these cases.

Linearization about the current state (or CSL, current state linearization) is an iteratively updating linear approximation about the current state. This method has the computational speed of linear approximation, while still accounting for the nonlinear dynamics of the underlying problem. There are some potential issues with this method.

A) We have not proven a theoretical guarantee that CSL will converge to the truth asymptotically. That said, CSL performs very well in all the applications we check in the paper.

B) When you are linearizing around a point other than the steady state, the method of undermined coefficients has a strange problem. It is not clear which eigenvalues to use in order to find the P matrix. Linearizing around the steady-state, you just choose the stable roots. However, away from the steady-state, it is likely that the linear approximation is not stable. We therefore have an issue about which roots to use. But again, in the paper, all the examples we checked used the stable roots and performed very well in terms of Euler errors and in terms of deviations from the true solution, both in impulse response functions and across simulations.

We think this approach is very valuable for simulating models that have the instabilities that are so important to many current research questions.

]]>Adding a psycho-bias as a separate fundamental we are close to the literature of self-fulfilling a là Farmer. Yet, our extra term is different from the generic “confidence” usually mentioned. In fact, we expect that the sign of our bias is structurally positive. Also we predict that the bias is even larger in booms but zero amid crises. These a priori are at odds with the self-fulfilling literature that, as far as we know, argues that confidence is crucial especially in economic turmoil. Lastly, evidence supports our setting.

Thank you for commenting our paper.

A. Argentero, M. Bovi and R. Cerqueti ]]>

Sorry for liking your work that much.

]]>Cheers,

Eric

]]>Mohammad ]]>