The YUIMA Project: A Computational Framework for Simulation and Inference of Stochastic Differential Equations

Brouste, Alexandre; Fukasawa, Masaaki; Hino, Hideitsu; Iacus, Stefano Maria; Kamatani, Kengo; Koike, Yuta; Masuda, H.; Nomura, Ryosuke; Ogihara, Teppei; Shimuzu, Yasutaka; Uchida, Masayuki; Yoshida, Nakahiro

doi:10.18637/jss.v057.i04

Cited by 64 publications

(53 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These include sde (Iacus 2016), yuima (Brouste et al 2014), SIM.DiffProc (Guidoum and Boukhetala 2017), cts (Wang 2013), POMP (King, Nguyen, and Ionides 2016). For multisubject approaches, OpenMx (Neale et al 2016) now includes the function mxExpectationStateSpaceContinuousTime, which can be combined with the function mxFitFunctionMultigroup for fixed effects based group analysis.…”

Section: Introductionmentioning

confidence: 99%

Continuous Time Structural Equation Modeling with R Package ctsem

Driver¹,

Oud²,

Voelkle³

2017

J. Stat. Soft.

193

253

View full text Add to dashboard Cite

We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1) and time series (N = 1) data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models) in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem.

show abstract

Section: Introductionmentioning

confidence: 99%

Continuous Time Structural Equation Modeling with R Package ctsem

Driver¹,

Oud²,

Voelkle³

2017

J. Stat. Soft.

193

253

View full text Add to dashboard Cite

show abstract

“…In this work we focus on the argument method that identifies the type of discretization scheme when the object belongs to the class of 'yuima.cogarch', while for the remaining arguments we refer to Brouste et al (2014) and yuima's documentation (YUIMA Project Team 2017). The default value "euler" means that the simulation of a sample path is based on the EulerMaruyama discretization of the stochastic differential equations.…”

Section: Classes and Methods For The Simulation Of A Cogarch(p Q) Modelmentioning

confidence: 99%

“…For the general COGARCH(p, q), the main results are given in Brockwell et al (2006) and Chadraa (2009). The aim of this paper is to describe the simulation and the estimation schemes for a COGARCH(p, q) model in the yuima package version 1.0.2 developed by Brouste, Fukasawa, Hino, Iacus, Kamatani, Koike, Masuda, Nomura, Ogihara, Shimuzu, Uchida, and Yoshida (2014). Based on our knowledge, the yuima version 1.6.8 (YUIMA Project Team 2017) used in this paper is the only R package available on the Comprehensive R Archive Network (CRAN; https://CRAN.R-project.org/package=yuima) that allows the user to manage a higher order COGARCH(p, q) model driven by a general Lévy process.…”

Section: Introductionmentioning

confidence: 99%

COGARCH (p,q) : Simulation and Inference with the yuima Package

Iacus¹,

Mercuri²,

Rroji³

2017

J. Stat. Soft.

Self Cite

View full text Add to dashboard Cite

In this paper we show how to simulate and estimate a COGARCH(p, q) model in the R package yuima. Several routines for simulation and estimation are introduced. In particular, for the generation of a COGARCH(p, q) trajectory, the user can choose between two alternative schemes. The first is based on the Euler discretization of the stochastic differential equations that identify a COGARCH(p, q) model while the second considers the explicit solution of the equations defining the variance process.Estimation is based on the matching of the empirical with the theoretical autocorrelation function. Three different approaches are implemented: minimization of the mean squared error, minimization of the absolute mean error and the generalized method of moments where the weighting matrix is continuously updated. Numerical examples are given in order to explain methods and classes used in the yuima package.

show abstract

“…30 Such a process would incorporate both small and large changes in financial confidence and have been implemented in models of heterogeneous, interacting agents (e.g., Delli Gatti et al 2003). In any case, we can estimate either process as embedded in their broader deterministic context using Monte Carlo methods and the EulerMaruyama method (Korn et al 2010) or the asymptotic expansion method (Brouste et al 2014 …”

Section: Future Workmentioning

confidence: 99%

Inside Money in a Kaldor-Kalecki-Steindl Fiscal Policy Model: The Unit of Account, Inflation, Leverage, and Financial Fragility

Hannsgen

Young-Taft

2015

SSRN Journal

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes.You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. licence. www.econstor.eu The idea of including a model of greenhouse gas accumulation in the model developed in this paper was inspired by the presentations at a conference at SCEPA, The New School for Social Research, New York City, which was attended by several Levy Institute scholars. This conference took place on April 25 and 26, 2014. We thank the organizers of that conference. † hannsgen@levy.org ‡ tyoungtaft@simons-rock.edu If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicatedThe Levy Economics Institute Working Paper Collection presents research in progress by Levy Institute scholars and conference participants. The purpose of the series is to disseminate ideas to and elicit comments from academics and professionals. 1 AbstractWe hope to model financial fragility and money in a way that captures much of what is crucial in Hyman Minsky's financial fragility hypothesis. This approach to modeling Minsky may be unique in the formal Minskyan literature. Namely, we adopt a model in which a psychological variable we call financial prudence (P) declines over time following a financial crash, driving a cyclical buildup of leverage in household balance sheets. High leverage or a low safe-asset ratio in turn induces high financial fragility (FF). In turn, the pathways of FF and capacity utilization (u) determine the probabilistic risk of a crash in any time interval. When they occur, these crashes entail discrete downward jumps in stock prices and financial sector assets and liabilities.To the endogenous government liabilities in Hannsgen (2014), we add common stock and bank loans and deposits. In two alternative versions of the wage-price module in the model (wagePhillips curve and chartalist, respectively), the rate of wage inflation depends on either unemployment or the wage-setting policies of the government sector. At any given time t, goods prices also depend on endogenous markup and labor productivity variables. Goods inflation affects aggregate demand through its impact on the value of assets and debts. Bank rates depend...

show abstract

The YUIMA Project: A Computational Framework for Simulation and Inference of Stochastic Differential Equations

Cited by 64 publications

References 30 publications

Continuous Time Structural Equation Modeling with R Package ctsem

Continuous Time Structural Equation Modeling with R Package ctsem

COGARCH (p,q) : Simulation and Inference with the yuima Package

Inside Money in a Kaldor-Kalecki-Steindl Fiscal Policy Model: The Unit of Account, Inflation, Leverage, and Financial Fragility

Contact Info

Product

Resources

About