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...

abstract the purpose of this paper is to test the hypothesis first proposed by romer (1993); suggesting that inflation is lower in more open economies. according to this hypothesis, central banks have a lower incentive to engineer surprise inflations in more-open economies because the phillips curve is steeper. furthermore, comparing with other empirical studies, this paper has used the new kof...

Existing estimates of the Federal Reserve’s implicit inflation target typically rely on the assumption that it is constant for the duration of the period of analysis. This paper relaxes this assumption and estimates the implicit inflation target using a time-varying parameter model and the Kalman filter. In applying this method to the Volcker–Greenspan period, it finds significant time variatio...

We model elasticity of volatility as a stochastic process with an eye to merge popular constant elasticity of variance (CEV) and stochastic volatility (SV) models in order to understand when it is appropriate to use absolute or relative changes or some intermediate transformation as well as to compare with more traditional autoregressive exponential stochastic volatility formulations. We descri...

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...

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...

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...