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Andreasen, Martin M.; Kronborg, Anders F.

The extended perturbation method: with applications to the New Keynesian model and the zero lower bound. (English) Zbl 07766829

Quant. Econ. 13, No. 3, 1171-1202 (2022).

Cited in 1 Document

MSC:

91-XX Game theory, economics, finance, and other social and behavioral sciences

Keywords:

stability of perturbation approximations; upper bound on inflation; zero lower bound on policy rate
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[1] Adjemian, Stephane and MichelJuillard (2010), “Dealing with ZLB in DSGE models: An application to the Japanese economy.” ESRI Discussion Paper No. 258.
[2] Adjemian, Stephane and MichelJuillard (2013), “Stochastic extended path approach.” Working Paper.
[3] Ajevskis, Viktors (2017), “Semi‐global solutions to DSGE models: Perturbation around a deterministic path.” Studies in Nonlinear Dynamics and Econometrics, 21 (2), 1-28. · Zbl 1507.91109
[4] An, Sungbau and FrankSchorfheide (2007), “Bayesian analysis of DSGE models.” Econometric Review, 26 (2-4), 113-172. · Zbl 1112.62015
[5] Andreasen, Martin M. (2010), “How to maximize the likelihood function for a DSGE model.” Computational Economics, 35 (2), 127-154. · Zbl 1183.91092
[6] Andreasen, Martin M. (2012), “On the effects of rare disasters and uncertainty shocks for risk premia in non‐linear DSGE models.” Review of Economic Dynamics, 15 (3), 295-316.
[7] Andreasen, Martin M. (2013), “Non‐linear DSGE models and the central difference Kalman filter.” Journal of Applied Econometrics, 28, 929-955.
[8] Andreasen, Martin M., JesusFernandez‐Villaverde, and Juan F.Rubio‐Ramirez (2018), “The pruned state space system for non‐linear DSGE models: Theory and empirical applications to estimation.” Review of Economic Studies, 85 (1), 1-49. · Zbl 1405.91361
[9] Andreasen, Martin M., Anders F.Kronborg (2022), “Supplement to ‘The extended perturbation method: With applications to the New Keynesian model and the zero lower bound’.” Quantitative Economics Supplemental Material, 13, https://doi.org/10.3982/QE1102. · Zbl 07766829 · doi:10.3982/QE1102
[10] Arouba, S. Boragan, PabloCuba‐Borda, and FrankSchorfheide (2018), “Macroeconomic dynamics near the ZLB: A tale of two countries.” Review of Economic Studies, 85 (1), 87-118. · Zbl 1405.91430
[11] Beaudry, Paul, DanaGalizia, and FranckPortier (2020), “Putting the cycle back into the business cycle analysis.” American Economic Review, 110 (1), 1-47.
[12] Benhabib, Jess, StephanieSchmitt‐Grohe, and MartinUribe (2001), “The perils of Taylor rules.” Journal of Economic Theory, 96, 40-69. · Zbl 0981.91042
[13] Black, Fischer (1995), “Interest rates as options.” The Journal of Finance, 50 (5), 1371-1376.
[14] Blasques, Francisco and MarcNientker (2020), “Transformed perturbation solutions for dynamic stochastic general equilibrium models,” 1-57. Working Paper.
[15] Boucekkine, Raouf (1995), “An alternative methodology for solving nonlinear forward looking models.” Journal of Economic Dynamic and Control, 19 (4), 711-734. · Zbl 0875.90100
[16] Burnside, Craig (1998), “Solving asset pricing models with Gaussian shocks.” Journal of Economic Dynamics and Control, 22, 329-340. · Zbl 0902.90017
[17] Calvo, Guillermo A. (1983), “Staggered prices in a utility‐maximizing framework.” Journal of Monetary Economics, 12 (3), 383-398.
[18] Christiano, Lawrence J., MartinEichenbaum, and Charles L.Evans (2005), “Nominal rigidities and the dynamic effects of a shock to monetary policy.” Journal of Political Economy, 113 (1), 1-45.
[19] Creel, Michael and DennisKristensen (2013), “Indirect likelihood inference.” BSE Working Paper.
[20] Duffie, Darrell and Kenneth J.Singleton (1993), “Simulated moments estimation of Markov models of asset prices.” Econometrica, 61 (4), 929-952. · Zbl 0783.62099
[21] Fair, Ray C. and John B.Taylor (1983), “Solution and maximum likelihood estimation of dynamic nonlinear rational expectations models.” Econometrica, 51 (4), 1169-1185. · Zbl 0516.62097
[22] Fernandez‐Villaverde, Jesus, GreyGordon, PabloGuerron‐Quintana, and Juan F.Rubio‐Ramirez (2015), “Nonlinear adventures at the zero lower bound.” Journal of Economic Dynamic and Control, 57, 182-204. · Zbl 1401.91362
[23] Fernández‐Villaverde, Jesús, PabloGuerrón‐Quintana, Juan F.Rubio‐Ramírez, and MartinUribe (2011), “Risk matters: The real effects of volatility shocks.” American Economic Review, 101 (6), 2530-2561.
[24] Fernández‐Villaverde, Jesús and Juan F.Rubio‐Ramírez (2007), “Estimating macroeconomic models: A likelihood approach.” Review of Economic Studies, 74 (4), 1-46. · Zbl 1206.91064
[25] Gust, Christopher, EdwardHerbst, DavidLopez‐Salido, and Matthew E.Smith (2017), “The empirical implications of the interate‐rate lower bound.” American Economic Review, 107 (7), 1971-2006.
[26] Haan, Wouter J. Den and JorisDe Wind (2012), “Nonlinear and stable perturbation‐based approximations.” Journal of Economic Dynamic and Control, 36 (10), 1477-1497. · Zbl 1345.91021
[27] Jermann, Urban J. (1998), “Asset pricing in production economics.” Journal of Monetary Economics, 41 (2), 257-275.
[28] Judd, Kenneth L. and Sy‐MingGuu (1997), “Asymptotic methods for aggregate growth models.” Journal of Economic Dynamics and Control, 21 (6), 1025-1042. · Zbl 0901.90039
[29] Juillard, Michel and SebastienVillemot (2011), “Multi‐country real business cycle models: Accuracy tests and test bench.” Journal of Economic Dynamic and Control, 35, 178-185. · Zbl 1231.91363
[30] Kim, Jae‐Young (2002), “Limited information likelihood and Bayesian analysis.” Journal of Econometrics, 107, 175-193. · Zbl 1030.62016
[31] Kim, Jinill, SunghyunKim, ErnstSchaumburg, and Chrisopher A.Sims (2008), “Calculating and using second‐order accurate solutions of discrete time dynamic equilibrium models.” Journal of Economic Dynamics and Control, 32 (11), 3397-3414. · Zbl 1181.91142
[32] Kim, Jinill and Francisco J.Ruge‐Murcia (2009), “How much inflation is necessary to grease the wheels?” Journal of Monetary Economics, 56 (3), 365-377.
[33] King, Rober G. and Sergio T.Rebelo (1999), “Resuscitating real business cycles.” Handbook of Macroeconomics, 1, 927-1007.
[34] Levintal, Oren (2017), “Fifth‐order perturbation solution to DSGE models.” Journal of Economics Dynamic and Control, 80, 1-16. · Zbl 1401.91245
[35] Lombardo, Giovanni and HaraldUhlig (2018), “A theory of pruning.” International Economic Review, 59 (4), 1825-1836. · Zbl 1419.91453
[36] Lubik, Tohomas A. and FrankSchorfheide (2004), “Testing for indeterminancy: An application to U.S. monetary policy.” American Economic Review, 94 (1), 190-217.
[37] Lucas, Robert E. (1978), “Asset prices in an exchange economy.” Econometrica, 46 (6), 1429-1445. · Zbl 0398.90016
[38] Pötscher, Benedikt M. and IngmarPrucha (1997), Dynamic Nonlinear Econometric Models: Asymptotic Theory. Springer‐Verlag, Berlin Heidelberg, New York. · Zbl 0923.62121
[39] Rietz, Thomas A. (1988), “The equity premium: A solution.” Journal of Monetary Economics, 22, 117-131.
[40] Rudebusch, Glenn D. and Eric T.Swanson (2012), “The bond premium in a DSGE model with long‐run real and nominal risks.” American Economic Journal: Macroeconomics, 4 (1), 1-43.
[41] Ruge‐Murcia, Francisco (2012), “Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles.” Journal of Economics Dynamic and Control, 36 (6), 914-938. · Zbl 1243.91072
[42] Ruge‐Murcia, Francisco J. (2007), “Methods to estimate dynamic stochastic general equilibrium models.” Journal of Economic Dynamics and Control, 31 (8), 2599-2636. · Zbl 1163.91488
[43] Schmitt‐Grohé, Stephanie and MartinUribe (2004), “Solving dynamic general equilibrium models using a second‐order approximation to the policy function.” Journal of Economic Dynamics and Control, 28 (4), 755-775. · Zbl 1179.91132
[44] Smith, Anthony A.Jr. (1993), “Estimating nonlinear time‐series models using simulated vector autoregressions.” Journal of Applied Econometrics, 8 (Supplement: Special Issue on Econometric Inference Using Simulation Techniques), S63-S84.
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