Model Reduction Methods for Vector Autoregressive Processes

Model Reduction Methods for Vector Autoregressive Processes

Author: Ralf Brüggemann

Publisher: Springer Science & Business Media

Published: 2012-09-25

Total Pages: 226

ISBN-13: 3642170293

DOWNLOAD EBOOK

1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo sitions, have been developed over the years. The econometrics of VAR models and related quantities is now well established and has found its way into various textbooks including inter alia Llitkepohl (1991), Hamilton (1994), Enders (1995), Hendry (1995) and Greene (2002). The unrestricted VAR model provides a general and very flexible framework that proved to be useful to summarize the data characteristics of economic time series. Unfortunately, the flexibility of these models causes severe problems: In an unrestricted VAR model, each variable is expressed as a linear function of lagged values of itself and all other variables in the system.


Optimal Multi-Step VAR Forecasting Averaging

Optimal Multi-Step VAR Forecasting Averaging

Author: Jen-Che Liao

Publisher:

Published: 2018

Total Pages: 54

ISBN-13:

DOWNLOAD EBOOK

This paper proposes frequentist multiple-equation least squares averaging approaches for multi-step forecasting with vector autoregressive (VAR) models. The proposed VAR forecasting averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative and direct multi-step forecasting averaging, respectively. Under the framework of stationary VAR processes of infinite order, we provide theoretical justifications by establishing asymptotic unbiasedness and asymptotic optimality of the proposed forecasting averaging approaches. Specifically, MMMA exhibits asymptotic optimality for one-step ahead forecast averaging, whereas for direct multi-step forecasting averaging the asymptotically optimal combination weights are determined separately for each forecast horizon based on the MCVAh procedure. The finite-sample behaviour of the proposed averaging procedures under misspecification is investigated via simulation experiments. An empirical application to a three-variable monetary VAR, based on the U.S. data, is also provided to present our methodology.


MULTIVARIATE TIME SERIES ANALYSIS with MATLAB. VAR and VARMAX MODELS

MULTIVARIATE TIME SERIES ANALYSIS with MATLAB. VAR and VARMAX MODELS

Author: Perez M.

Publisher: Createspace Independent Publishing Platform

Published: 2016-06-24

Total Pages: 176

ISBN-13: 9781534868076

DOWNLOAD EBOOK

This book focuses on Multivariate Time Series Models. The most important issues are the following: Vector Autoregressive Models Introduction to Vector Autoregressive (VAR) Models Data Structures Model Specification Structures VAR Model Estimation VAR Model Forecasting, Simulation, and Analysis VAR Model Case Study Cointegration and Error Correction Introduction to Cointegration Analysis Identifying Single Cointegrating Relations Identifying Multiple Cointegrating Relations Testing Cointegrating Vectors and Adjustment Speeds