This paper performs multiple-break unit root tests on the data of China’s national and sectoral output and labour productivity, with finite-sample critical values bootstrapped through Monte Carlo simulations. We find strong evidence against the unit-root hypothesis in favour of the segmentedtrend-stationarity alternative. Based on breaking trend functions, the steady-state and transitional grow...

In this paper we provide empirical findings on the significance of positive feedback trading for the return behavior in the German stock market. Relying on the ShillerSentana-Wadhwani model, we use the link between index return auto-correlation and volatility to obtain a better understanding into the return characteristics generated by traders adhering to positive feedback trading strategies. O...

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...

If returns are not predictable, dividend growth must be predictable, to generate the observed variation in divided yields. I find that the absence of dividend growth predictability gives stronger evidence than does the presence of return predictability. Long-horizon return forecasts give the same strong evidence. These tests exploit the negative correlation of return forecasts with dividend-yie...

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...

This paper examines asymptotic expansions for estimation errors expressed explicitly as functions of underlying random variables. Taylor series expansions are obtained from which "rst and second moment approximations are derived. While the expansions are essentially equivalent to the traditional Nagar type, the terms are expressed in a form which enables moment approximations to be obtained in ...

This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most efficient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood using the EIS strategy (Richard and Zhang, 1998), and detail its implementation for univariate homogene...

We forecast US GDP and inflation over 1-, 4and 8-step horizons using the dataset from Stock and Watson (2009), with factors, variables, both, and neither. Autometrics handles perfect collinearity and more regressors than observations, enabling all principal components and variables to be included for model selection, jointly with using impulse-indicator saturation (IIS) for multiple breaks. Emp...

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...