We propose a multiplicative autoregressive conditional proportion (ARCP) model for (0,1)-valued time series, in the spirit of GARCH (generalized heteroscedastic) and ACD (autoregressive duration) models. In particular, our underlying process is defined as product independent identically distributed (i.i.d.) sequence inverted mean, which, turn, depends on past reciprocal observations such way th...

this paper investigates the relationship between inflation and growth uncertainty in iran for the period of 1988-2008 by using quarterly data. we employ generalized autoregressive conditional heteroscedasticity in mean (garch-m) model to estimate time-varying conditional residual variance of growth, as a standard measures of growth uncertainty. the empirical evidence shows that growth uncertain...

A panel data regression model with heteroskedastic as well as spatially correlated disturbancesis considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are c...

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle’s Lagrange Multiplier test, clear evidences are found for t...

This article provides a solution to the curse of dimensionality associated to multivariate generalized autoregressive conditionally heteroskedastic (GARCH) estimation. We work with univariate portfolio GARCH models and show how the multivariate dimension of the portfolio allocation problem may be recovered from the univariate approach. The main tool we use is ‘‘variance sensitivity analysis,’’ ...

In this chapter, we examine the use of special forms of correlated random e ects in the generalized linear mixed model (GLMM) setting. A special feature of our GLMM is the inclusion of random residual e ects to account for lack of t due to extra variation, outliers and other unexplained sources of variation. For random e ects, we consider, in particular, the correlation structure and improper p...

The first–order integer–valued autoregressive (INAR(1)) process is investigated, where the autoregressive coefficient is close to one. It is shown that the limiting distribution of the conditional least–squares estimator for this coefficient is normal and, in contrast to the familiar AR(1) process, the rate of convergence is n. Finally, the nearly critical Galton–Watson process with unobservabl...

This paper motivates a reduced form discrete-time series approach that models realized volatility by using its separated components, continuous variation and variation due to jumps. For this purpose, I combine Engle and Russell’s (1998) autoregressive conditional duration (ACD) model applied to the continuous and jump size variation with Hamilton and Jordà’s (2002) autoregressive conditional ha...

Abstract The technique of ANOVA has been widely used in Economics and Finance where the observations are usually time-dependent but the model itself is treated as independent in time. In this paper, we extend an ANOVA model by releasing the assumption of independence in time. We further release the assumption of homoskedasticity in the traditional profile analysis by introducing GARCH innovatio...

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York: New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniqu...