This work presents a new technique for temporally benchmarking a time series according to the growth rates preservation principle (GRP) by Causey and Trager (1981). A procedure is developed which (i) transforms the original constrained problem into an unconstrained one, and (ii) applies a Newton’s method exploiting the analytic Hessian of the GRP objective function. We show that the proposed te...

This paper considers a general model which allows for both deterministic and stochastic forms of seasonality, including fractional (stationary and nonstationary) orders of integration, and also incorporating endogenously determined structural breaks. Monte Carlo analysis shows that the suggested procedure performs well even in small samples, accurately capturing the seasonal properties of the s...

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...

Consider a linear model setting in which the explanatory variables are specified by time series. To sequentially test for the stability of the regression parameters in time, we introduce a detector which is based on the first excess time of a CUSUM-type statistic over a suitably defined threshold function. The main aim of this paper is to derive the limit distribution of the detector. By provid...

The paper investigates asymptotically efficient inference in general time series likelihood models with time varying parameters. Inference procedures for general loss functions are evaluated by a weighted average risk criterion. The weight function focusses on persistent parameter paths of moderate magnitude, and is proportional to the distribution function of a Gaussian random walk. It is show...

We show how temporal aggregation affects the size and power of the DOLS residualbased KPSS test of the null of cointegration. Size is effectively controlled by setting the minimum number of leads equal to one – as opposed to zero – when selecting the lag/lead order of the DOLS regression, but at a cost to power in finite samples. If highfrequency data for one or more series are available, we sh...

This paper develops an asymptotic theory for a non-linear parametric co-integrating regression model. We establish a general framework for weak consistency that is easy to apply for various non-stationary time series, including partial sum of linear process and Harris recurrent Markov chain. We provide a limit distribution for the nonlinear least square estimator which significantly extends the...

The persistence properties of economic time series has been a primary object of investigation in a variety of guises since the early days of econometrics. This paper suggests investigating the persistence of processes conditioning on their history. In particular we suggest that examining the derivatives of the conditional expectation of a variable with respect to its lags maybe a useful indicat...

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...

This paper develops the methodology and asymptotic theory for the estimation of longrun variance of continuous time process. We analyze the asymptotic bias and variance of the longrun variance estimator in continuous time, and provide the optimal bandwidth balancing them o↵ and minimizing the asymptotic mean squared error. In the paper, we present not only how to consistently estimate the longr...