{"title":"Nonlinear Phillips curves, mixing feedback rules and the distribution of inflation and output","authors":"Luisa Corrado , Sean Holly","doi":"10.1016/S0165-1889(02)00184-7","DOIUrl":null,"url":null,"abstract":"<div><div><span><span>Optimal nominal interest rate rules are usually set assuming that the underlying world is linear. In this paper, we consider the performance of ‘optimal’ rules when the underlying relationship between </span>inflation<span> and the output gap may be nonlinear. In particular if the inflation–output trade-off exhibits nonlinearities<span> this will impart a bias to inflation when a linear rule is used. By deriving some analytical results for the higher moments and in particular the skewness of the distribution of output and inflation, we show that the sign of the skewness of the distribution of inflation and output depends upon the nature of the nonlinearity. For the convex modified hyperbolic function used by Chadha et al. (IMF Staff Papers 39(2) (1992) 395) and Schaling (Bank of England Working Paper Series, 1999) inflation is positively and output negatively skewed. Whereas, if a concave–convex form is used the skewness of inflation and output is reversed. To correct this bias we propose a piecewise linear rule, which can be thought of as an approximation to the nonlinear rule of </span></span></span><span><span>Schaling (1999)</span></span>. In order to evaluate the relevance of these results, we turn to some illustrative empirical results for the US and the UK. We show that this reduces the bias, but at the expense of an increase in the volatility of the nominal interest rate.</div></div>","PeriodicalId":48314,"journal":{"name":"Journal of Economic Dynamics & Control","volume":"28 3","pages":"Pages 467-492"},"PeriodicalIF":2.3000,"publicationDate":"2003-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economic Dynamics & Control","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165188902001847","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Optimal nominal interest rate rules are usually set assuming that the underlying world is linear. In this paper, we consider the performance of ‘optimal’ rules when the underlying relationship between inflation and the output gap may be nonlinear. In particular if the inflation–output trade-off exhibits nonlinearities this will impart a bias to inflation when a linear rule is used. By deriving some analytical results for the higher moments and in particular the skewness of the distribution of output and inflation, we show that the sign of the skewness of the distribution of inflation and output depends upon the nature of the nonlinearity. For the convex modified hyperbolic function used by Chadha et al. (IMF Staff Papers 39(2) (1992) 395) and Schaling (Bank of England Working Paper Series, 1999) inflation is positively and output negatively skewed. Whereas, if a concave–convex form is used the skewness of inflation and output is reversed. To correct this bias we propose a piecewise linear rule, which can be thought of as an approximation to the nonlinear rule of Schaling (1999). In order to evaluate the relevance of these results, we turn to some illustrative empirical results for the US and the UK. We show that this reduces the bias, but at the expense of an increase in the volatility of the nominal interest rate.
期刊介绍:
The journal provides an outlet for publication of research concerning all theoretical and empirical aspects of economic dynamics and control as well as the development and use of computational methods in economics and finance. Contributions regarding computational methods may include, but are not restricted to, artificial intelligence, databases, decision support systems, genetic algorithms, modelling languages, neural networks, numerical algorithms for optimization, control and equilibria, parallel computing and qualitative reasoning.