Nonlinearity in the Fed's monetary policy rule

Kim, Dong Heon; Osborn, Denise R.; Sensier, Marianne

doi:10.1002/jae.792

Cited by 70 publications

(45 citation statements)

References 32 publications

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“…Most of this research has focused on the estimation of non-linear policy reaction functions (for a variety of samples and with a variety of non-linear specifications) exploiting the well-known result that if an asymmetry in central bank F o r P e e r R e v i e w 5 preferences exists, then the optimal policy rule is non-linear -see Bec et al (2002), Kim et al (2005), Cukierman and Muscatelli (2002), Martin and Milas (2004), Karagedikli and Lees (2004), and Bruinshoofd and Candelon (2005).…”

Section: The Case For Asymmetric Monetary Policymaker's Preferencesmentioning

confidence: 99%

Testing for asymmetries in the preferences of the euro-area monetary policymaker

Aguiar

Martins

2008

Applied Economics

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This article tests for asymmetries in the preferences of the euro-area monetary policymaker with 1995:1-2005:2 data from the latest update of the European Central Bank's (ECB's) Area-wide database. Following the relevant literature, we distinguish between three types of asymmetry: precautionary demand for expansions, precautionary demand for price stability and interest rate smoothing asymmetry. Based on the joint generalized method of moments (GMM) estimation of the Euler equation of optimal policy and the aggregate supply-aggregate demand (AS-AD) structure of the macroeconomy, we find evidence of precautionary demand for price stability in the preferences revealed by the monetary policymaker. This type of asymmetry is consistent with the ECB's definition of price stability and with the priority of credibility-building by a recently created monetary authority.

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Section: The Case For Asymmetric Monetary Policymaker's Preferencesmentioning

confidence: 99%

Testing for asymmetries in the preferences of the euro-area monetary policymaker

Aguiar

Martins

2008

Applied Economics

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“…In some cases, however, it may be important to establish whether the rejection of the null hypothesis is due to neglected nonlinearity in mean or whether the rejection is merely a consequence of heteroskedastic data. An example of one such instance is the growing literature on the importance of asymmetric loss functions in the context of the conduct of monetary policy (Kim et al, 2002, Elliot et al, 2003. This type of loss function could imply a nonlinear policy reaction function but testing for nonlinearity will be complicated by the fact that the macroeconomic time series data used in the estimation is likely to be heteroskedastic.…”

Section: Introductionmentioning

confidence: 99%

Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity

Becker

Hurn

2009

Economic Analysis and Policy

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This paper considers an important practical problem in testing time-series data for nonlinearity in mean. Most popular tests reject the null hypothesis of linearity too frequently if the the data are heteroskedastic. Two approaches to redressing this size distortion are considered, both of which have been proposed previously in the literature although not in relation to this particular problem. These are the heteroskedasticityrobust-auxiliary-regression approach and the wild bootstrap. Simulation results indicate that both approaches are e¤ective in reducing the size distortion and that the wild bootstrap o¤ers better performance in smaller samples. Two practical examples are then used to illustrate the procedures and demonstrate the potential pitfalls encountered when using non-robust tests.Keywords nonlinearity in mean, heteroskedasticity, wild bootstrap, empirical size and power JEL Classi…cation C12, C22, C52

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“…On the other hand, there is considerable evidence that the interest rate reaction function is smoothly asymmetric over each business cycle -see e.g. Schaling (2004), Dolado, María-Dolores, and Ruge-Murcia (2004), Kim, Osborn, and Sensier (2005), Kesriyeli, Osborn, and Sensier (2006). In our framework, we can allow for both features jointly.…”

Section: Star Models With Breaksmentioning

confidence: 99%

Estimation and inference in unstable nonlinear least squares models

Boldea

Hall

2013

Journal of Econometrics

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There is compelling evidence that many macroeconomic and financial variables are not generated by linear models. This evidence is based on testing linearity against either smooth nonlinearity or piece-wise linearity, but there is no framework that encompasses both. This paper provides an econometric framework that allows for both breaks and smooth nonlinearity in-between breaks.We estimate the unknown break-dates simultaneously with other parameters via nonlinear least-squares. Using new central limit results for nonlinear processes, we provide inference methods on break-dates and parameter estimates and several instability tests. We illustrate our methods via simulated and empirical smooth transition models with breaks.JEL classification: C12, C13, C22

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Nonlinearity in the Fed's monetary policy rule

Cited by 70 publications

References 32 publications

Testing for asymmetries in the preferences of the euro-area monetary policymaker

Testing for asymmetries in the preferences of the euro-area monetary policymaker

Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity

Estimation and inference in unstable nonlinear least squares models

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