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Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series

Koopman, Siem Jan; Bos, Charles S. - 2003

The linear Gaussian state space model for which the common variance is treated as a stochastic time-varying variable is considered for the modelling of economic time series. The focus of this paper is on the simultaneous estimation of parameters related to the stochastic processes of the mean...

Persistent link: https://www.econbiz.de/10014100716

Forecasting nonlinear aggregates and aggregates with time-varying weights

Lütkepohl, Helmut - 2010

Despite the fact that many aggregates are nonlinear functions and the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the case of linear aggregates with fixed, time-invariant aggregation weights. In this study a...

Persistent link: https://www.econbiz.de/10003966437

Dynamic vs Static Autoregressive Models for Forecasting Time Series

Xie, Chris S. - 2008

I build an innovative Dynamic Autoregressive Model (DAR) in forecasting time series, and make comparison with a Static Autoregressive Model (SAR). DAR model requires re-evaluating optimal orders and coefficients at each period, while SAR models simply treats them as constants. The optimal length...

Persistent link: https://www.econbiz.de/10012723135

Autocorrelate Your Assets or, It's Not All White Noise : A Practical Means for Generating Autocorrelated Time Series Data in Monte Carlo Simulations

Stock, Robert D. - 2020

compromises the quality of the prediction from the data. Simulations that do not take account of autocorrelation will not properly … model reality, as there is significant autocorrelation in many asset returns, for example in T-Bills and hedge fund … satisfy the statistics of any serial autocorrelation, as well as the actual (possibly non-Gaussian) joint probability …

Persistent link: https://www.econbiz.de/10012846361

Dynamic Spatial Network Quantile Autoregression

Xu, Xiu - 2020

, and the associated asymptotic theory for estimation is also provided. Simulation results show that our model performs well …

Persistent link: https://www.econbiz.de/10012823437

Multivariate Modelling of Time Series Count Data : An Autoregressive Conditional Poisson Model

Heinen, Andréas; Rengifo, Erick W. - 2005

This paper introduces a new multivariate model for time series count data. The Multivariate Autoregressive Conditional Poisson model (MACP) makes it possible to deal withissues of discreteness, overdispersion (variance greater thant then mean) and both auto- and crosscorrelation. We model counts...

Persistent link: https://www.econbiz.de/10014067805

Variable Selection, Estimation and Inference for Multi-Period Forecasting Problems

Pesaran, M. Hashem; Pick, Andreas; Timmermann, Allan - 2011

This paper conducts a broad-based comparison of iterated and direct multi-period forecasting approaches applied to both univariate and multivariate models in the form of parsimonious factor-augmented vector autoregressions. To account for serial correlation in the residuals of the multi-period...

Persistent link: https://www.econbiz.de/10014042344

Adaptive rate-optimal detection of small autocorrelation coefficient

Guay, Alain; Guerre, Emmanuel; Lazarová, Štepána - 2009

A new test is proposed for the null of absence of serial correlation. The test uses a data-driven smoothing parameter. The resulting test statistic has a standard limit distribution under the null. The smoothing parameter is calibrated to achieve rate-optimality against several classes of...

Persistent link: https://www.econbiz.de/10003850599

Predicting Bid-Ask Spreads Using Long Memory Autoregressive Conditional Poisson Models

Groß-Klußmann, Axel; Hautsch, Nikolaus - 2011

that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discreteness of …

Persistent link: https://www.econbiz.de/10014180186

Bayesian Model Selection and Forecasting in Noncausal Autoregressive Models

Lanne, Markku; Luoma, Arto; Luoto, Jani - 2009

In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the...

Persistent link: https://www.econbiz.de/10014202739