Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks
نویسنده
چکیده
In the paper a general framework for large scale modeling of macroeconomic and nancial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Address for correspondence: Claudio Morana, Università di Milano Bicocca, Facoltà di Economia, Dipartimento di Economia Politica, Piazza dellAteneo Nuovo 1, 20126, Milano, Italy. E-mail: [email protected]. yA previous version of the paper was presented at the Nineteenth and Twenty rst Annual Symposium of the Society for Non Linear Dynamics and Econometrics, the Fourth Annual Conference of the Society for Financial Econometrics, the 65th European Meeting of the Econometric Society (ESEM), the 2011 NBER-NSF Time Series Conference, and the 5th CSDA International Conference on Computational and Financial Econometrics. The author is grateful to conference participants, N. Cassola, F.C. Bagliano, and R.T. Baillie for constructive comments. zAs Mitsuo Aida wrote in one of his poems, somewhere in life/ there is a path/ that must be taken regardless of how hard we try to avoid it/ at that time, all one can do is remain silent and walk the path/ neither complaining nor whining/ saying nothing and walking on/ just saying nothing and showing no tears/ it is then,/ as human beings,/ that the roots of our souls grow deeper. This paper is dedicated to the loving memory of A.
منابع مشابه
Efficient Tests for General Persistent Time Variation in Regression Coefficients∗
There are a large number of tests for instability or breaks in coefficients in regression models designed for different possible departures from the stable model. We make two contributions to this literature. First, we consider a large class of persistent breaking processes that lead to asymptotically equivalent efficient tests. Our class allows for many or relatively few breaks, clustered brea...
متن کاملIdentification of Structural Vector Autoregressions by Stochastic Volatility
In Structural Vector Autoregressive (SVAR) models, heteroskedasticity can be exploited to identify structural parameters statistically. In this paper, we propose to capture time variation in the second moment of structural shocks by a stochastic volatility (SV) model, assuming that their log variances follow latent AR(1) processes. Estimation is performed by Gaussian Maximum Likelihood and an e...
متن کاملModified Maximum Likelihood Estimation in First-Order Autoregressive Moving Average Models with some Non-Normal Residuals
When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...
متن کاملForecasting Long Memory Processes Subject to Structural Breaks
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC o...
متن کاملA Dynamic Semiparametric Proportional Hazard Model
This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors. In order to capture persistent serial dependence in the duration process, we extend the model by an o...
متن کامل