Solvability of perturbation solutions in DSGE models

Lan, Hong; Meyer-Gohde, Alexander

doi:10.1016/j.jedc.2014.06.005

Cited by 15 publications

(10 citation statements)

References 40 publications

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“…28 Thus, E [εt] = 0 follows directly and gσ consequentially, see Schmitt-Grohe and Uribe ( 2004), Jin and Judd (2002), or Lan and Meyer-Gohde (2014). 29 The notation x ⊗ [n] represents Kronecker powers, x ⊗[n] is the n'th fold Kronecker product of x with itself: 30 The second line follows as gyσ and gεσ are zero, see Schmitt-Grohe and Uribe ( 2004), Jin and Judd (2002), or Lan and Meyer-Gohde (2014).…”

Section: B1 Risk-adjusted Linear Approximationmentioning

confidence: 99%

(Un)expected monetary policy shocks and term premia

Kliem

Meyer-Gohde

2021

J of Applied Econometrics

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The term structure of interest rates is crucial for the transmission of monetary policy to financial markets and the macroeconomy. Disentangling the impact of monetary policy on the components of interest rates, expected short rates and term premia, is essential to understanding this channel. To accomplish this, we provide a quantitative structural model with endogenous, time-varying term premia that are consistent with empirical findings. News about future policy, in contrast to unexpected policy shocks, has quantitatively significant effects on term premia along the entire term structure. This provides a plausible explanation for partly contradictory estimates in the empirical literature.

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Section: B1 Risk-adjusted Linear Approximationmentioning

confidence: 99%

(Un)expected monetary policy shocks and term premia

Kliem

Meyer-Gohde

2021

J of Applied Econometrics

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“…The model contains two unit roots but cannot be normalized in a standard manner. Because of it, the loglinearization relies on the technique described in Kim et al (2008) and Lan and Meyer-Gohde (2014).…”

Section: Model Solution and Estimationmentioning

confidence: 99%

Forecasting for the Russian Economy Using Small-Scale DSGE Models

Kreptsev¹,

Seleznev²

2018

RJMF

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This study examines the ability of a small-scale DSGE model to forecast the dynamics of key macroeconomic variables for the Russian economy. The study uses two versions of a standard model of a small open economy, adding a stochastic oil price trend under various assumptions about exchange rate policy. Comparison with the same size BVAR model shows DSGE models to be superior as regards exchange rate, price and interest rate forecasting and slightly inferior with respect to GDP forecasting.

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“…where the invertibility of B + AΛ follows from Assumption 3.1, see Lan and Meyer-Gohde (2014). (6) starting with Φ p−1 and working backwards to Φ 1 .…”

Section: Proof See Appendix Amentioning

confidence: 99%