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on Econometric Time Series |
| By: | Hui, Yongchang; Wong, Wing-Keung; Bai, Zhidong; Zhu, Zhenzhen |
| Abstract: | In this paper, we propose a quick, efficient, and easy method to examine whether a time series Yt possesses any nonlinear feature. The advantage of our proposed nonlinearity test is that it is not required to know the exact nonlinear features and the detailed nonlinear forms of Yt. We find that our proposed test can be used to detect any nonlinearity for the variable being examined and detect GARCH models in the innovations. It can also be used to test whether the hypothesized model, including linear and nonlinear, to the variable being examined is appropriate as long as the residuals of the model being used can be estimated. Our simulation study shows that our proposed test is stable and powerful. We apply our proposed statistic to test whether there is any nonlinear feature in the sunspot data and whether the S&P 500 index follows a random walk model. The conclusion drawn from our proposed test is consistent those from other tests. |
| Keywords: | Nonlinearity, U-statistics, Volterra expansion, sunspots, efficient market |
| JEL: | C01 C12 G10 |
| Date: | 2016–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:75216&r=ets |
| By: | Nalan Basturk (Maastricht University, The Netherlands); Stefano Grassi (University of Kent, United Kingdom); Lennart Hoogerheide (VU University Amsterdam, The Netherlands); Herman K. van Dijk (Erasmus University Rotterdam, The Netherlands) |
| Abstract: | A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and includes stochastic volatility, denoted by FAVAR-SV. Next, a Bayesian strategy combination is introduced in order to deal with a set of strategies. Our approach extends the mixture of the experts analysis by allowing the strategic weights to be dependent between strategies as well as over time and to further allow for strategy incompleteness. Our approach results in a combination of different portfolio strategies: a model-based and a residual momentum strategy. The estimation of this modeling and strategy approach can be done using an extended and modified version of the forecast combination methodology of Casarin, Grassi, Ravazzolo and Van Dijk(2016). Given the complexity of the non-linear and non-Gaussian model used a new and efficient filter is introduced based on the MitISEM approach by Hoogerheide, Opschoor and Van Dijk (2013). Using US industry portfolios between 1926M7 and 2015M6 as data, our empirical results indicate that time-varying combinations of flexible models in the FAVAR-SV class and two momentum strategies lead to better return and risk features than very simple and very complex models. Combinations of two strategies help, in particular, to reduce risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. |
| Keywords: | Nonlinear; non-gaussian state space; filters; density combinations; bayesian modeling; equity momentum |
| JEL: | C11 C15 G11 G17 |
| Date: | 2016–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20160099&r=ets |
| By: | Neil R. Ericsson |
| Abstract: | David Hendry has made major contributions to many areas of economic forecasting. He has developed a taxonomy of forecast errors and a theory of unpredictability that have yielded valuable insights into the nature of forecasting. He has also provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. In addition, David has developed new forecast tools, such as forecast encompassing; and he has improved existing ones, such as nowcasting and robustification to breaks. This interview for the International Journal of Forecasting explores David Hendry’s research on forecasting. |
| Keywords: | Encompassing ; Equilibrium correction models ; Error correction ; Evaluation ; Exogeneity ; Forecasting ; Modeling ; Nowcasting ; Parameter constancy ; Robustification ; Structural breaks |
| JEL: | C53 |
| Date: | 2016–11 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgif:1184&r=ets |
| By: | Roberto Casarin; Claudia Foroni; Massimiliano Marcellino; Francesco Ravazzolo |
| Abstract: | We propose a Bayesian panel model for mixed frequency data whose parameters can change over time according to a Markov process. Our model allows for both structural instability and random effects. We develop a proper Markov Chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters and test its properties in simulation experiments. We use the model to study the effects of macroeconomic uncertainty and financial uncertainty on a set of variables in a multi-country context including the US, several European countries and Japan. We find that for most of the variables financial uncertainty dominates macroeconomic uncertainty. Furthermore, we show that uncertainty coefficients differ if the economy is in a contraction regime or in an expansion regime. JEL codes: C13, C14, C51, C53. Keywords: dynamic panel model, mixed-frequency, Markov switching, Bayesian inference, MCMC. |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:igi:igierp:585&r=ets |