This paper derives the asymptotic normality of the nonlinear quantile regression estimator with dependent errors. The required assumptions are weak, and it is neither assumed that the error process is stationary nor that it is mixing. In fact, the notion of weak dependence introduced in this paper, can be considered as a quantile specific local variant of known concepts. The connection of the d...

This paper estimates a time-varying AR-GARCH model of inflation producing measures of inflation uncertainty for the euro area, and investigates the linkages between them in a VAR framework, also allowing for the possible impact of the policy regime change associated with the start of EMU in 1999. The main findings are as follows. Steady-state inflation and inflation uncertainty have declined st...

In this paper, we propose a class of ACD-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and sign and size asymmetries in financial durations. In particular, our functional coefficient autoregressive conditional duration (FC-ACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a unive...

This paper provides a simple unified framework for assessing the empirical linkages between returns and realized and implied volatilities. First, we show that whereas the volatility feedback effect as measured by the sign of the correlation between contemporaneous return and realized volatility depends importantly on the underlying structural model parameters, the correlation between return and...

Econometric modelling using automatic algorithms such as PcGets (Hendry and Krolzig, 2001) and Autometrics (Doornik, 2007) has recently become popular. This paper considers automatic model selection when there is non-linearity inherent in the process. The strategy uses a new test for nonlinearity, specifies the general model using polynomials if linearity is rejected, and undertakes a general-t...

This paper investigates if component GARCH models introduced by Engle and Lee (1999) and Ding and Granger (1996) can capture the long-range dependence observed in measures of time-series volatility. Long-range dependence is assessed through the sample autocorrelations, two popular semiparametric estimators of the long-memory parameter, and the parametric fractionally integrated GARCH (FIGARCH) ...

In this paper we use new statistical methods to examine the connection between economic expectations and support for the government in the U.S.A. For this purpose, we analyse the order of integration for Congressional Approval and Economic Expectations from a fractional point of view. The results show that though both individual series can be specified in terms of fractional processes, the unit...

This paper proposes an empirical Bayes approach for Markov switching autoregressions that can constrain some of the state-dependent parameters (regression coefficients and error variances) to be approximately equal across regimes. By flexibly reducing the dimension of the parameter space, this can help to ensure regime separation and to detect the Markov switching nature of the data. The permut...

This paper demonstrates how parsimonious models of sinusoidal functions can be used to fit spatially variant time series in which there is considerable variation of a periodic type. A typical shortcoming of such tools relates to the difficulty in capturing idiosyncratic variation in periodic models. The strategy developed here addresses this deficiency. While previous work has sought to overcom...

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previous...