2 edition of **Second-order approximation of dynamic models with time-varying risk** found in the catalog.

Second-order approximation of dynamic models with time-varying risk

Gianluca Benigno

- 308 Want to read
- 5 Currently reading

Published
**2010** by National Bureau of Economic Research in Cambridge, MA .

Written in English

**Edition Notes**

Statement | Gianluca Benigno, Pierpaolo Benigno, Salvatore Nisticò |

Series | NBER working paper series -- working paper 16633, Working paper series (National Bureau of Economic Research : Online) -- working paper no. 16633. |

Contributions | Benigno, Pierpaolo, Nisticò, Salvatore, National Bureau of Economic Research |

Classifications | |
---|---|

LC Classifications | HB1 |

The Physical Object | |

Format | Electronic resource |

ID Numbers | |

Open Library | OL24837751M |

LC Control Number | 2011655865 |

Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using p-splines. Biometrics. [Google Scholar] Andrinopoulou E-R, Rizopoulos D, Takkenberg JJ, and Lesaffre E (). Joint modeling of two longitudinal outcomes and competing risk data. Statistics in medic –Author: Jie Zhou, Jiajia Zhang, Alexander C. Mclain, Wenbin Lu, Xuemei Sui, James W. Hardin. Modeling, analysis and simulation of chemical processes is increasingly central to the work of chemical engineers -- but it is rarely covered in depth in process design guides. This book fills that gap. It is a comprehensive introduction to process modeling and dynamics using the powerful MATLAB and SIMULINK analysis TOPICS:Start by understanding the rationale for process modeling. ESTIMATION OF TIME-VARYING PARAMETERS 2. A nonlinear regression model In this section, we demonstrate how convex optimization algorithms can be used for non-linear regression analysis. Let X be a random vector taking values in a set X‰model (1) in which the.

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The first-order approximation is consistent with a conditionally linear model in which risk is still time-varying but has no distinct role – separated from the primitive stochastic disturbances – in influencing the endogenous variables.

The second-order approximation of Cited by: Second-Order Approximation of Dynamic Models with Time-Varying Risk Gianluca Benigno, Pierpaolo Benigno, and Salvatore Nisticò NBER Working Paper No. December JEL No.

C63 ABSTRACT This paper provides first and second-order approximation methods for the solution of non-linear dynamic stochastic models in which the exogenous state variables. Second-order approximation of dynamic models with time-varying risk Gianluca Benignoa, Pierpaolo Benignob,c,n, Salvatore Nisticòb,d a London School of Economics, United Kingdom b LUISS Guido Carli, Rome, Italy c EIEF, Rome, Italy d Università degli Studi di Roma “La Sapienza”, Rome, Italy article info Article history: Received 15 December Received in revised form.

This paper provides first and second-order approximation methods for the solution of non-linear dynamic stochastic models in which the exogenous state variables follow conditionally-linear.

This paper provides first and second-order approximation methods for the solution of non-linear dynamic stochastic models in which the exogenous state variables follow conditionally linear stochastic processes displaying time-varying by: This paper provides first and second-order approximation methods for the solution of non-linear dynamic stochastic models in which the exogenous state variables follow conditionally-linear stochastic processes displaying time-varying : BENIGNO G, BENIGNO P and S.

NISTICO&#. This paper provides first and second-order approximation methods for the solution of non-linear dynamic stochastic models in which the exogenous state variables follow conditionally linear stochastic processes displaying time-varying risk.

The first-order approximation is consistent with a conditionally linear model in which risk is still time-varying but has no distinct role - separated from the primitive stochastic disturbances - in influencing the endogenous variables.

This paper provides first and second-order approximation methods for the solution of non-linear dynamic stochastic models in which the exogenous state variables follow conditionally-linear stochastic processes displaying time-varying risk.

Second-order approximation of dynamic models without the use of tensors Paul Kleina,∗ aUniversity of Western Ontario First draft: This version: Janu Abstract Several approaches to ﬁnding the second-order approximation to a dynamic model have been proposed by: "Second-order approximation of dynamic models with time-varying risk," Journal of Economic Dynamics and Control, Elsevier, vol.

37(7), pages "Second-order approximation of dynamic models with time-varying risk," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages Gianluca Benigno & Pierpaolo Benigno & Salvatore Nisticò, Second-order approximation of dynamic models with time-varying risk Benigno, Gianluca, Benigno, Pierpaolo and Nisticò, Salvatore () Second-order approximation of dynamic models with time-varying risk.

Journal of Economic Dynamics and Control, 37 (7). A model is a system of partial di⁄erential equations describing the evolu-tion of (economic) variables over time (dynamic approach) Di⁄erent models are judged by their implications (variances, covariances, impulse responses etc.) "Good" models (models whose implications match empirical facts under study) are used for normative analysis.

Dynamic Models D.1 ii METHOD OF MOMENTS Real processes have complex dynamic responses and require models with many parameters to be characterized accurately. However, the engineer often seeks a simple model with few parameters to describe the main aspects of the dynamic behavior.

Examples throughout this book demonstrate that the ﬁrst-order-with. The first-order approximation is consistent with a conditionally-linear model in which risk is still time-varying but has no distinct role - separated from the primitive stochastic disturbances.

Second-order approximation of dynamic models with time-varying risk. [Gianluca Benigno; Pierpaolo Benigno; Salvatore Nisticò; National Bureau of Economic Research.] -- "This paper provides first and second-order approximation methods for the solution of non-linear dynamic stochastic models in which the exogenous state variables follow conditionally-linear.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper provides first and second-order approximation methods for the solu-tion of non-linear dynamic stochastic models in which the exogenous state variables follow conditionally-linear stochastic processes displaying time-varying risk.

The first-order approximation is consistent with a conditionally-linear model in which risk. Research article Full text access Second-order approximation of dynamic models with time-varying risk. this paper. We demonstrate the ability of this code to deliver accurate second-order approximations by applying it to a number of example economies.

The rst example considered is the standard, one-sector, stochastic growth model. Sims () computes a second-order approximation to this economy, which we are able to replicate.

" Second-order approximation of dynamic models with time-varying risk," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages Gianluca Benigno & Pierpaolo Benigno &. Keywords: Solving dynamic models, Second-order approximation, Third-order approximation, Second-order matrix chain rule, Third-order matrix chain rule, Generalised Sylvester equations 1.

Introduction Solving higher order approximations of DSGE models can. a set of MATLAB programs designed to compute the coeﬃcients of the second-order approximation. The validity and applicability of the proposed method is illustrated by solving the dynamics of a number of model economies.

JEL Classiﬁcation: E0, C Key words: Solving Dynamic General Equilibrium Models, Second-Order Approximation, Matlab code. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," by Stephanie Schmitt-Grohe and Martin Uribe (JEDC, vol.

28, Januarypp. ) Matlab code. First-order approximation gx_hx.m. Second-order approximation gxx_hxx.m gss_hss.m. Obtaining the derivatives of f (requires Matlab's Symbolic. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper provides first and second-order approximation methods for the solution of nonlinear dynamic stochastic models in which the exogenous state variables follow conditionally-linear stochastic processes displaying time-varying risk.

The first-order approximation is consistent with a conditionally-linear model in. risk in our models. Rather than focusing on the time-varying probably of disaster risk as in Gourio (), we explore the interaction between stochastic volatility and risk aversion.

We conclude that the endogenous responses of the economy due to volatility shocks are ampli ed when agents display higher level of risk Size: KB. This letter is intended to circumvent this limitation. It proposes a closed-form approximation for the steady-state performance of a Kalman filter based on a second-order dynamic model, while at the same time providing a novel closed-form upper bound for the convergence by: 1.

Order Accurate Solutions of Discrete Time Dynamic Equilibrium Models," Journal of Eco-nomic Dynamics and Cont { Schmitt-Groh e, S. and M. Uribe (), \Solving Dynamic General Equilibrium Models Us-ing a Second-Order Approximation to the Policy Function," Journal of Economic Dynamics and Cont { Smith, Jr.

Carhart 4-factors model. The results suggest that the estimated risks are time-varying and not stable overtime which confirms the risk instability anomaly. The results is more pronounced in Carhart’s 4-factors model.

Introduction The main problem in the dynamic asset pricing model literature is that the set of conditioning. Dynamic Speech Models Theory, Algorithms, and Applications i. Articulatory Dynamic Model Functional Nonlinear Model for Articulatory-to- In a broad sense, speech dynamics are time-varying or temporal characteristics in all stages of the human speech communication process.

This process, sometimes referred to as speechAuthor: Li Deng. Some risk systems do this through approximation techniques, such as a Taylor series based on the previous night’s closing risks, but that approach is extremely fragile in extreme market conditions.

For example, in latea lot of exotics desks were using Taylor series approximations from the previous night’s risk calculations to manage.

Working Paper File Downloads Abstract Views; Last month: 3 months: 12 months: Total: Last month: 3 months: 12 months: Total: A Central Bank Theory of Price Level Determination.

Second-Order Approximation of Dynamic Models with Time-Varying Risk G Benigno, P Benigno, S Nisticò Journal of Economic Dynamics and Control 37 (7), –, 2 A Model with Time-Varying Risk.

We now describe a model—first generally and then in detail—that can generate the observations just discussed. It is a general equilibrium monetary model with segmented markets that generates time-varying risk by: These approximations have grown in popularity, mainly because they allow researchers to quickly and accurately solve DSGE models with many state variables and inherent non-linearities to analyse uncertainty shocks or time-varying risk premia (see Fernández-Villaverde et al.

and Rudebusch and Swanson (), among others).Cited by: The assumption of time-varying exogenous uncertainty entails nontrivial issues in the solution of the model. To this end, we apply a new method that we have recently developed for general dynamic stochastic models with time-varying uncertainty (Benigno, Benigno, and Nisticò ).

The main result of our previous work is that a second-order Cited by: proof of the global validity of our approximation, the source code for our Perturbation-AIM software routine, and the third-order approximate solution to all the variables of the stochastic growth model as a benchmark.

The Algorithm Model Setup and Notation We consider a system of dynamic, discrete-time rational expectations equations of. Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies Herman K.

van Dijk EUR and Norges Bank (joint with Nalan Ba˘sturk, Agnieszka Borowska, Stefano Grassi, Lennart Hoogerheide) Workshop: Big data, Machine Learning and The. "The Sample Average Approximation Method for Stochastic Programs with Integer Recourse", Published electronically in: Optimization Online.

Shapiro, A. and Yomdin, Y., " On functions representable as a difference of two convex functions, and necessary conditions in a constrainted optimization", preprint, Parts of Books.

The life safety model uses results of flood water depth and velocity from two-dimensional hydraulic models such as Telemac 2D over the course of the event, to represent the flood hazard.

Unlike many other models and methods of this kind the life safety model includes a dynamic interaction between the receptors and the flood by: 8. In the synthetic aperture radar (SAR) imaging of ship-induced wakes, it is difficult to obtain the Doppler velocity of a Kelvin wake due to the lack of time-varying wake models and suitable radar equipment.

The conventional Kelvin wake investigation based on the static Kelvin wake model failed to reflect time-varying characteristics, which are significant in the application of the Kelvin wake Author: Jie Niu, Xingdong Liang, Xin Zhang. We develop a dynamic factor model with time-varying factor loadings and stochastic volatility in both the latent factors and idiosyncratic components.

We employ this new measurement tool to study the evolution of international business cycles in the post-Bretton Woods period, using a panel of output growth rates for nineteen by: function of the model, is in general difﬁcult to evaluate.

Particle ﬁltering is a powerful route (although not the only one) to accomplish this goal. We structure this section as follows. First, we present the state-space representation of a dynamic macroeconomic model.

Second, we deﬁne the likelihood function of a dynamic model.Dynamic Term Structure Models with Score-Driven Time-Varying Parameters: Estimation and Forecasting Koopman, Lucas, and Zamojski () I model time-varying relative risk aversion in the consumption capital asset pricing model with the standard constant relative risk .