|
on Econometric Time Series |
| By: | Wenger, Kai; Leschinski, Christian |
| Abstract: | We propose a family of self-normalized CUSUM tests for structural change under long memory. The test statistics apply non-parametric kernel-based fixed-b and fixed-m long-run variance estimators and have well-defined limiting distributions that only depend on the long-memory parameter. A Monte Carlo simulation shows that these tests provide finite sample size control while outperforming competing procedures in terms of power. |
| Keywords: | Fixed-bandwidth asymptotics; Fractional Integration; Long Memory; Structural Breaks |
| JEL: | C12 C22 |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-647&r=ets |
| By: | Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, Hamburg, Germany) |
| Abstract: | We study the incremental in- and out-of-sample predictive value of time-varying risk aversion for realized volatility of gold-price returns via extended heterogeneous autoregressive realized volatility (HAR-RV) models. Our findings suggest that time varying risk aversion possesses predictive value for gold volatility both in- and out-of-sample. The predictive power of risk aversion is robust to the inclusion of realized higher-moments, jumps, gold returns, leverage effect as well as the aggregate market volatility in the forecasting model. Interestingly, risk aversion is found to absorb in sample the predictive power of stock-market volatility at a short forecasting horizon, while out-of-sample results show that risk aversion adds to predictive value at a medium and long forecast horizon. Additional tests suggest that the short-run (long-run) out-of-sample predictive value of risk aversion is beneficial for investors who are more concerned about over-predicting (under-predicting) gold market volatility. Overall, our findings show that time-varying risk aversion captures information useful for predicting (bad, good) realized volatility not already contained in the other predictors, and allows more accurate out-of-sample forecasts to be computed at a medium and long forecast horizon. |
| Keywords: | Gold-price returns, Realized volatility, Forecasting |
| Date: | 2018–12 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201881&r=ets |
| By: | Gary Koop; Stuart McIntyre; James Mitchell; Aubrey Poon |
| Abstract: | Output growth estimates for the regions of the UK are currently published at the annual frequency only and are released with a long delay. Regional economists and policymakers would benefit from having higher frequency and more timely estimates. In this paper we develop a mixed frequency Vector Autoregressive (MF-VAR) model and use it to produce estimates of quarterly regional output growth. Temporal and cross-sectional restrictions are imposed in the model to ensure that our quarterly regional estimates are consistent with the annual regional observations and the observed quarterly UK totals. We use a machine learning method based on the hierarchical Dirichlet-Laplace prior to ensure optimal shrinkage and parsimony in our over-parameterised MF-VAR. Thus,this paper presents a new, regional quarterly database of nominal and real Gross Value Added dating back to 1970. We describe how we update and evaluate these estimates on an ongoing, quarterly basis to publish online (at www.escoe.ac.uk/regionalnowcasting) more timely estimates of regional economic growth. We illustrate how the new quarterly data can be used to contribute to our historical understanding of business cycle dynamics and connectedness between regions. |
| Keywords: | Regional data, Mixed frequency, Nowcasting, Bayesian methods, Realtime data, Vector autoregressions |
| JEL: | C32 C53 E37 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:escoed:escoe-dp-2018-14&r=ets |
| By: | Jaime Martinez-Martin (OECD); Elena Rusticelli (OECD) |
| Abstract: | This paper builds an innovative composite world trade cycle index (WTI) by means of a dynamic factor model to monitor and perform short-term forecasts in real time of world trade growth of both goods and (usually neglected) services. The selection of trade indicator series is made using a multidimensional approach, including Bayesian model averaging techniques, dynamic correlations and Granger non-causality tests in a linear VAR framework. To overcome real-time forecasting challenges, the dynamic factor model is extended to account for mixed frequencies, to deal with asynchronous data publication and to include hard and survey data along with leading indicators. Nonlinearities are addressed with a Markov switching model. Simulations analysis in pseudo real-time suggests that: i) the global trade index is a useful tool to track and forecast world trade in real time; ii) the model is able to infer global trade cycles precisely and better than the few competing alternatives; and iii) global trade finance conditions seem to lead the trade cycle, in line with the theoretical literature. |
| Keywords: | bayesian model averaging, cycles, dynamic factor models, goods trade, granger non-causality, leading indicators, markov switching models, Real-time forecasting, services trade, VAR models, world trade |
| JEL: | C2 E27 E32 |
| Date: | 2018–12–13 |
| URL: | http://d.repec.org/n?u=RePEc:oec:ecoaaa:1524-en&r=ets |