My remarks about this paper are organised within four points. The …rst of these notes the close connection between the authors’ random coe¢ cient model and some earlier models that were not formulated in terms of "random coe¢ cients". The second comments on the model’s identi…cation. The third point queries the transition from the authors’basic model (1) to their quantile version (2). Finally w...

The vector autoregression (VAR), has long proven to be an effective method for modeling the joint dynamics of macroeconomic time series as well as forecasting. One of the major disadvantages of the VAR that has hindered its applicability is its heavy parameterization; the parameter space grows quadratically with the number of series included, quickly exhausting the available degrees of freedom....

In this paper, we present a new method for modeling timeevolving correlation networks, using a Mean Reversion Autoregressive Model, and apply this to stock market data. The work is motivated by the assumption that the price and return of a stock eventually regresses back towards their mean or average. This allows us to model the stock correlation time-series as an autoregressive process with a ...

1 1 1. Introduction Autoregressive models form an important class of processes in time series analysis. A nonparametric version of these models was introduced by Jones (1978). To allow for heteroscedastic modelling of the innovations, people often consider the model where the " t are assumed to be i.i.d. with mean 0 and variance 1. Several authors dealt with the interesting statistical problem ...

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A nonparametric regression estimator is introduced which adapts to the smoothness of the unknown function being estimated. This property allows the new estimator to automatically achieve minimal bias over a large class of locally smooth functions without changing the rate at which the variance converges. Optimal convergence rates are shown to hold for both i.i.d. data and autoregressive process...

An overview of model building with periodic autoregression (PAR) models is given emphasizing the three stages of model development: identification, estimation and diagnostic checking. New results on the distribution of residual autocorrelations and suitable diagnostic checks are derived. The validity of these checks is demonstrated by simulation. The methodology discussed is illustrated with an...

Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, such models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecast...