Inference on stochastic time-varying coefficient models

Giraitis, Liudas; Kapetanios, George; Yates, Tony

doi:10.1016/j.jeconom.2013.10.009

Cited by 132 publications

(172 citation statements)

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“…(In our empirical work the condition was always satisfied). In addition, our kernel estimator has good 4 The kernel method was developed in Kapetanios and Yates (2014), (which reworked the analysis of evolving inflation persistence in Cogley and Sargent (2005) using kernel methods), in Giraitis, Kapetanios, and Yates (2014b) (which derives the theoretical results on consistency and asymptotic normality of the kernel estimator for an AR(1) model where the coefficients follow a bounded random walk), and latterly in Giraitis, Kapetanios, and Yates (2014a) (which extends consistency results to a VAR(1) with persistent stochastic volatility).…”

Section: Connections To Existing Workmentioning

confidence: 99%

“…Finally, using a rolling window which is a flat kernel has two disadvantages in our view. Firstly, it is not producing very smooth estimated parameter processes and therefore makes interpretations more difficult, and also Monte Carlo analysis in Giraitis, Kapetanios, and Yates (2014b) suggests that the normal kernel, we use, results in estimators with lower mean squared error compared to the flat kernel.…”

Section: Connections To Existing Workmentioning

confidence: 99%

“…We begin by estimating a time-varying reduced-form VAR model in the same 7 observed variables for the US as the DSGE model, using kernel methods that we proposed in previous work (Giraitis, Kapetanios, and Yates (2014b)). Unlike other methods, kernel estimation can handle without difficulty a large VAR model.…”

Section: Introductionmentioning

confidence: 99%

“…

AbstractFollowing Giraitis, Kapetanios, and Yates (2014b), this paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the data set constructed by Smets and Wouters (2007). We apply an indirect inference method to map from this TV VAR to time variation in implied Dynamic Stochastic General Equilibrium (DSGE) parameters.

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confidence: 99%

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Estimating Time-Varying DSGE Models Using Minimum Distance Methods

Giraitis¹,

Kapetanios

Theodoridis

et al. 2014

SSRN Journal

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Following Giraitis, Kapetanios, and Yates (2014b), this paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the data set constructed by Smets and Wouters (2007). We apply an indirect inference method to map from this TV VAR to time variation in implied Dynamic Stochastic General Equilibrium (DSGE) parameters. We find that many parameters change substantially, particularly those defining nominal rigidities, habits and investment adjustment costs. In contrast to the 'Great Moderation' literature our monetary policy parameter estimates suggest that authorities tried to deliver a low and stable inflation from 1975 onwards, however, the severe adverse supply shocks in the 70s could have caused these policies to fail.Keywords: DSGE, structural change, kernel estimation, time-varying VAR, monetary policy shocks JEL classification: E52, E61, E66, C14, C18 * The views expressed in this paper are those of the authors, and not necessarily those of the Bank of England or those of the Monetary or Financial Policy Committees. The authors wish to thank Andy Blake, Fabio Canova, Efrem Castelnuevo, Domenico Giannone, Alesandro Justiniano, Lutz Kilian, Matthias Paustian, Giorgio Primiceri, Juan RubioRamirez, Frank Schorfheide and Frank Smets, plus two anonymous referees for very helpful suggestions and comments. We also thank participants in presentations at the Bank of England, the Ghent workshop on empirical macro in May 2012, and the NBER workshop on methods and applications for DSGE models at the FRB Atlanta, October 2012. †

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Section: Connections To Existing Workmentioning

confidence: 99%

Section: Connections To Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…

…”

mentioning

confidence: 99%

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Estimating Time-Varying DSGE Models Using Minimum Distance Methods

Giraitis¹,

Kapetanios

Theodoridis

et al. 2014

SSRN Journal

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“…A large number of estimation and hypothesis testing methods have been developed from these seminal ideas, see for example, Chandler and Polonik (2017), Paparoditis and Preuss (2015), Guinness and Fuentes (2015), Chen et al (2018), Fiecas and Ombao (2016), Song et al (2016), Wu and Zhou (2011), Puchstein and Preuss (2016), Rosen et al (2012), Vogt and Dette (2015), Kreiss and Paparoditis (2015), , Zhou (2014), Nason (2013), Preuss et al (2013b) Guinness and Stein (2013), Giraitis et al (2014), Preuss et al (2013a), Zhou (2013), Roueff and Von Sachs (2011), Dette et al (2011), Van Bellegem and Dahlhaus (2006) and Beran (2009), among others.…”

Section: Introductionmentioning

confidence: 99%

On the Estimation of Locally Stationary Long-Memory Processes

Chan¹,

Manríquez²

2019

STAT SINICA

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This paper establishes the statistical properties of a spectrum-based Whittle parameter estimation procedure for long-range dependent locally stationary processes. Both theoretical and empirical behaviors are investigated. In particular, a central limit theorem for the Whittle likelihood estimation method is derived under mild distributional conditions, extending its application to a wide range of non-Gaussian time series. The finite sample properties of the estimators are examined via Monte Carlo experiments with Gamma and Log-normal noise distributions. These simulation studies demonstrate that the proposed method behaves properly even for small to moderate sample sizes. Finally, the practical application of this methodology is illustrated by means a well-known real-life data example.

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Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market

Giraitis

Kapetanios

Wetherilt

et al. 2015

J of Applied Econometrics

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Summary We propose a novel methodology for dynamic econometric modelling of large financial networks subject to persistence, structural changes and sparsity. We estimate bivariate dynamic Tobit‐type models for each pair of banks, allowing for deterministic or stochastic time‐varying parameters, and then aggregate across all bank pairs. To tackle the high dimensionality of the model, we construct a few lagged variables that efficiently summarize the position of a bank pair in the network. We propose a simple and computationally easy kernel‐based local maximum likelihood estimator of the time‐varying parameters of the model and establish its asymptotic properties. We then apply the model to the time series of daily overnight money market network in the UK 2003–2012. The results show that our model can successfully accommodate the numerous structural breaks arising from changes to the monetary framework and captures well the dynamics of the interbank lending relationships in this period. Copyright © 2015 John Wiley & Sons, Ltd.

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Inference on stochastic time-varying coefficient models

Cited by 132 publications

References 39 publications

Estimating Time-Varying DSGE Models Using Minimum Distance Methods

Estimating Time-Varying DSGE Models Using Minimum Distance Methods

On the Estimation of Locally Stationary Long-Memory Processes

Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market

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