A New Time‐Varying Parameter Autoregressive Model for U.S. Inflation Expectations

Lanne, Markku; Luoto, Jani

doi:10.1111/jmcb.12402

“…(2013 and 2016) introduce bounds on trend inflation and unemployment to capture more economically sensible representations that result from, for example, central bank targets for inflation or that unemployment cannot be negative. Lanne and Luoto (2017) apply this approach to obtain improved estimation of the new Keynesian Phillips curve and found strong evidence for noncausality. More recent applications include Cross and Poon (2020), Chan (2020), Dimitrakopoulos and Kolossiatis (2020) and Hou (2020).…”

Section: Non‐linear and Non‐gaussian Modelsmentioning

confidence: 99%

Bayesian State Space Models in Macroeconometrics

Chan

¹

,

Strachan

²

2020

Journal of Economic Surveys

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State space models play an important role in macroeconometric analysis and the Bayesian approach has been shown to have many advantages. This paper outlines recent developments in state space modelling applied to macroeconomics using Bayesian methods. We outline the directions of recent research, specifically the problems being addressed and the solutions proposed. After presenting a general form for the linear Gaussian model, we discuss the interpretations and virtues of alternative estimation routines and their outputs. This discussion includes the Kalman filter and smoother, and precision based algorithms. As the advantages of using large models have become better understood, a focus has developed on dimension reduction and computational advances to cope with high-dimensional parameter spaces. We give an overview of a number of recent advances in these directions. Many models suggested by economic theory are either non-linear or non-Gaussian, or both. We discuss work on the particle filtering approach to such models as well as other techniques that use various approximations -to either the time t state and measurement equations or to the full posterior for the states -to obtain draws.

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“…Chan, Potter (2013 and introduce bounds on trend in ‡ation and unemployment to capture more economically sensible representations that result from, for example, central bank targets for in ‡ation or that unemployment cannot be negative. Lanne and Luoto (2017) apply this approach to obtain improved estimation of the new Keynesian Phillips curve and found strong evidence for noncausality. More recent applications include Cross and Poon (2019), Chan (2020), Dimitrakopoulos and Kolossiatis (2020) and Hou (2020).…”

Section: Initializementioning

confidence: 99%

Bayesian State Space Models in Macroeconometrics

Chan

¹

,

Strachan

²

2020

View full text Add to dashboard Cite

State space models play an important role in macroeconometric analysis and the Bayesian approach has been shown to have many advantages. This paper outlines recent developments in state space modelling applied to macroeconomics using Bayesian methods. We outline the directions of recent research, specifically the problems being addressed and the solutions proposed. After presenting a general form for the linear Gaussian model, we discuss the interpretations and virtues of alternative estimation routines and their outputs. This discussion includes the Kalman filter and smoother, and precision based algorithms. As the advantages of using large models have become better understood, a focus has developed on dimension reduction and computational advances to cope with high-dimensional parameter spaces. We give an overview of a number of recent advances in these directions. Many models suggested by economic theory are either non-linear or non-Gaussian, or both. We discuss work on the particle filtering approach to such models as well as other techniques that use various approximations -to either the time t state and measurement equations or to the full posterior for the states -to obtain draws.

show abstract

“…In the recent economic and statistical literature there has been an increasing interest in non-causal processes with heavy tailed innovations, see e.g. Gourieroux and Zakoïan (2017), Hecq, Lieb and Telg (2016), Lanne and Luoto (2017) and the references therein. Despite their simplicity, these models, even in their simplest form, are capable of mimicking periods of explosive, bubble-type dynamics and other types of complex, nonlinear behavior as witnessed repeatedly in …nancial and economic series; see Hencic and Gourieroux (2015) and Hecq et al (2016).…”

Section: Introductionmentioning

confidence: 99%