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...

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method provides a computationally more effic...

This paper considers the estimation of average autoregressive roots-near-unity in panels where the time-series have heterogenous local-to-unity parameters. The pooled estimator is shown to have a potentially severe bias and a robust median based procedure is proposed instead. This median estimator has a small asymptotic bias that can be eliminated almost completely by a bias correction procedur...

We study how total factor productivity (TFP), energy prices and the great moderation are linked. First, we estimate a joint stochastic process for the energy price and TFP and establish that until 1982:II, energy prices negatively affected productivity. This spill-over has since disappeared. Second, we show that within the framework of a Dynamic Stochastic General Equilibrium (DSGE) model, the ...

The paper explores the linkage between equity and commodity markets, focusing in particular on its evolution over time. It documents that a country’s equity market value has significant out-of-sample predictive ability for the future global commodity price index for several primary commodity-exporting countries. The out-of-sample predictive ability of the equity market appears around 2000s. The...

Much time series data are recorded on economic and financial variables. Statistical modelling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of ’memory’, or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are d...

This paper proposes a simple approach to analyzing profit dynamics which allows for time-varying persistence of profits. The time series model is a simple autoregressive process where the dynamics of the persistence parameter follow an autoregressive or random walk process. Using the longest time series available on profits for six US firms (Archer-Daniels-Midland , Avon, Coca Cola, Johnson & J...

A valid asymptotic expansion for the covariance of functions of multivariate normal vectors is applied to approximate autocovariances of time series generated by nonlinear transformation of Gaussian latent variates, and nonlinear functions of these, with special reference to long memory stochastic volatility models, serving to identify the roles played by the underlying Gaussian processes and t...

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...

We consider model selection for non-linear dynamic equations with more candidate variables than observations, based on a general class of non-linear-in-the-variables functions, addressing possible location shifts by impulse-indicator saturation. After an automatic search delivers a simplified congruent terminal model, an encompassing test can be implemented against an investigator’s preferred n...