A least squares estimation approach for the estimation of a GARCH (1,1) modelis developed. The asymptotic properties of the estimator are studied given mild regularity conditions, which require only that the error term has a conditionalmomen t of some order. We establish the consistency,...

Long memory processes have been extensively studied over the past decades. When dealing with the financial and economic data, seasonality and time-varying long-range dependence can often be observed and thus some kind of non-stationarity can exist inside financial data sets. To take into account...

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a...

This paper contributes to the literature by comparing predictive accuracy of one-period real-time simple seasonal ARIMA forecasts of Latvia's Gross Domestic Product (GDP) as well as by comparing a direct forecast of Latvia's GDP versus three kinds of indirect forecasts. Four main results are as...

This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these...

Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML) methods. The paper suggests and studies the performance of generalized method of moments (GMM) and indirect estimators for the autoregressive asymmetric moving average model. Both...

In the context of time series regression, we extend the standard Tobitmodel to allow for the possibility of conditional heteroskedastic error processes of the GARCH type.We discuss the likelihood function of the Tobit model in the presence of conditionally heteroskedastic errors.Expressing the...

Signal waveforms are very fast dampening oscillatory time series composed of exponential functions. The regular least squares fitting techniques are often unstable when used to fit exponential functions to such signal waveforms since such functions are highly correlated. Of late, some attempts...

This paper studies the sequential sampling scheme as a solution to the problem of aliasing, where the sampling interval is restricted to a minimum allowable value. Sequential sampling is analyzed and it is proved that when the sampling ratio is an integral number, the associated spectral...