Log-linearizing optimal price in New Keynesian model

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SUMMARY

The discussion focuses on log-linearizing the optimal price in a New Keynesian model around a zero-inflation flexible price steady state. The key equation presented is \(\frac{P_{t}^{*}}{P_{t}}E_{t}\sum_{k=0}^{\infty}\theta^{k}\beta^{k}C_{t+k}^{1-\sigma}\left(\frac{P_{t+k}}{P_{t}}\right)^{\epsilon-1}\), with the steady state condition \(P_{t} = P^{*}_{t} = P_{t+k} \equiv \bar{P}\). The solution derived involves a complex expression that incorporates expectations and steady state values, specifically \(\frac{\bar{C}^{1-\sigma}}{1-\theta\beta} +\frac{\bar{C}^{1-\sigma}}{1-\theta\beta}(\hat{p}^{*}_{t} - \hat{p}_{t}) + \bar{C}^{1-\sigma}\sum_{k=0}^{\infty}\theta^{k}\beta^{k}[(1-\sigma)E_{t}\hat{c}_{t+k} + (\epsilon-1)(E_{t}\hat{p}_{t+k} - \hat{p}_{t})]\).

PREREQUISITES
  • Understanding of New Keynesian economic models
  • Familiarity with log-linearization techniques
  • Knowledge of steady state analysis in economics
  • Proficiency in mathematical notation and expectations in economic contexts
NEXT STEPS
  • Study the derivation of log-linearization in New Keynesian frameworks
  • Explore the implications of zero-inflation steady states in economic models
  • Learn about the role of expectations in dynamic economic models
  • Investigate the mathematical properties of the summation \(\sum_{k=0}^{\infty}\theta^{k}\beta^{k}\)
USEFUL FOR

Economists, graduate students in economics, and researchers focusing on New Keynesian models and price dynamics will benefit from this discussion.

Charlotte87
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Homework Statement


I am going to do a log-linearization around a zero-inflation flexible price steady state of:

[itex]\frac{P_{t}^{*}}{P_{t}}E_{t}\sum_{k=0}^{\infty}\theta^{k}\beta^{k}C_{t+k}^{1-\sigma}\left(\frac{P_{t+k}}{P_{t}}\right)^{\epsilon-1}[/itex]

Zero-inflation flexible price steady state implies that [itex]P_{t} = P^{*}_{t} = P_{t+k} \equiv \bar{P}[/itex].


The Attempt at a Solution



I know the solution is:
[itex]\frac{\bar{C}^{1-\sigma}}{1-\theta\beta} +\frac{\bar{C}^{1-\sigma}}{1-\theta\beta}(\hat{p}^{*}_{t} - \hat{p}_{t}) + \bar{C}^{1-\sigma}\sum_{k=0}^{\infty}\theta^{k}\beta^{k}[(1-\sigma)E_{t}\hat{c}_{t+k} + (\epsilon-1)(E_{t}\hat{p}_{t+k} - \hat{p}_{t})][/itex]

where [itex]\hat{x}_{t} = x_{t} - \bar{x}, x_{t} = \log(X_{t}), \bar{x} = \log(\bar{X})[/itex] where [itex]\bar{X}[/itex] is the steady state value of X. And we have used [itex]\sum_{k=0}^{\infty}\theta^{k}\beta^{k} = \frac{1}{1-\theta\beta}[/itex].

I've now tried for several hours to do this approximation, but I just do not get to the solution. Can anyone help me on the way?
 
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It is straightforward. Please let me know if you have not figured this out yet, since your post is quite old.
Abe
 

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