The Stochastic Stationary Root Model

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The Stochastic Stationary Root Model. / Hetland, Andreas.

I: Econometrics, Bind 6, Nr. 3, 21.08.2018.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Hetland, A 2018, 'The Stochastic Stationary Root Model', Econometrics, bind 6, nr. 3. https://doi.org/10.3390/econometrics6030039

APA

Hetland, A. (2018). The Stochastic Stationary Root Model. Econometrics, 6(3). https://doi.org/10.3390/econometrics6030039

Vancouver

Hetland A. The Stochastic Stationary Root Model. Econometrics. 2018 aug 21;6(3). https://doi.org/10.3390/econometrics6030039

Author

Hetland, Andreas. / The Stochastic Stationary Root Model. I: Econometrics. 2018 ; Bind 6, Nr. 3.

Bibtex

@article{ff8005751dbd466c8a2f66276cef601d,
title = "The Stochastic Stationary Root Model",
abstract = "We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018.",
keywords = "Faculty of Social Sciences, cointegration, particle filtering, random coefficient autoregressive model, state space model, stochastic approximation",
author = "Andreas Hetland",
year = "2018",
month = "8",
day = "21",
doi = "10.3390/econometrics6030039",
language = "English",
volume = "6",
journal = "Econometrics",
issn = "2225-1146",
publisher = "MDPI AG",
number = "3",

}

RIS

TY - JOUR

T1 - The Stochastic Stationary Root Model

AU - Hetland, Andreas

PY - 2018/8/21

Y1 - 2018/8/21

N2 - We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018.

AB - We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018.

KW - Faculty of Social Sciences

KW - cointegration

KW - particle filtering

KW - random coefficient autoregressive model

KW - state space model

KW - stochastic approximation

U2 - 10.3390/econometrics6030039

DO - 10.3390/econometrics6030039

M3 - Journal article

VL - 6

JO - Econometrics

JF - Econometrics

SN - 2225-1146

IS - 3

ER -

ID: 222620162