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on Econometric Time Series |
| By: | Golosnoy, Vasyl; Gribisch, Bastian; Liesenfeld, Roman |
| Abstract: | We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes a generalized linear autoregressive moving average structure for the scale matrix of the Wishart distribution allowing to accommodate for complex dynamic interdependence between the variances and covariances of assets. In addition, it accounts for symmetry and positive definiteness of covariance matrices without imposing parametric restrictions, and can easily be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a Mixed Data Sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to time series of daily realized variances and covariances for five New York Stock Exchange (NYSE) stocks. -- |
| Keywords: | Component volatility models,Covariance matrix,Mixed data sampling,Observation-driven models,Realized volatility |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cauewp:201007&r=ets |
| By: | J. Isaac Miller (Department of Economics, University of Missouri-Columbia) |
| Abstract: | A test for the rank of a vector error correction model (VECM) or panel VECM based on the well-known trace test is proposed. The proposed test employs instrumental variables (IV's) generated by a class of nonlinear functions of the estimated stochastic trends of the VECM under the null. The test improves the standard trace test by replacing the non-standard critical values with chi-squared critical values. Extending the result to the panel VECM case, the test is robust to cross-sectional correlation of the disturbances. With this test, I extend earlier research using nonlinear IV's for unit root testing. However, the optimal instrument in the univariate case is not admissable in the more general multivariate case. The chi-squared result suggests that IV tests may be used to replace limits of other standard tests with integrated time series that are given by nonstandard stochastic integrals, even without a panel with which to pool tests. |
| Keywords: | VECM, panel VECM, cointegrating rank, trace test, nonlinear instruments |
| JEL: | C12 C32 C33 |
| Date: | 2010–01–30 |
| URL: | http://d.repec.org/n?u=RePEc:umc:wpaper:1001&r=ets |
| By: | Shu-Ping Shi |
| Abstract: | We demonstrate that the constant variance assumption in the Markov-switching Augmented Dickey-Fuller (ADF) test proposed by Hall, Psaradakis and Sola (1999) may result in the misjudgement of bubbles. Upon relaxing this assumption to allow for regime-varying error variances in the Markov-switching ADF test (referred to as the MSADF-RV test), we revisit the integration properties of the money base, consumer price and exchange rate in Argentina from January 1983 to November 1989. Based on the MSADF-RV test, we observe the occurrence of volatility switches in the exchange rate and the consumer price instead of observing bubbles in these two series as in Hall, Psaradakis and Sola (1999) |
| JEL: | C22 |
| Date: | 2010–06 |
| URL: | http://d.repec.org/n?u=RePEc:acb:cbeeco:2010-524&r=ets |
| By: | M. Hashem Pesaran; Andreas Pick; Allan Timmermann |
| Abstract: | This paper conducts a broad-based comparison of iterated and direct multi-period forecasting approaches applied to both univariate and multivariate models in the form of parsimonious factor-augmented vector autoregressions. To account for serial correlation in the residuals of the multi-period direct forecasting models we propose a new SUREbased estimation method and modified Akaike information criteria for model selection. Empirical analysis of the 170 variables studied by Marcellino, Stock and Watson (2006) shows that information in factors helps improve forecasting performance for most types of economic variables although it can also lead to larger biases. It also shows that finitesample modifications to the Akaike information criterion can modestly improve the performance of the direct multi-period forecasts. |
| Keywords: | Multi-period forecasts; direct and iterated methods; factor augmented VARs |
| JEL: | C22 C32 C52 C53 |
| Date: | 2010–06 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:250&r=ets |
| By: | James D. Hamilton |
| Abstract: | This paper surveys efforts to automate the dating of business cycle turning points. Doing this on a real time, out-of-sample basis is a bigger challenge than many academics might presume due to factors such as data revisions and changes in economic relationships over time. The paper stresses the value of both simulated real-time analysis-- looking at what the inference of a proposed model would have been using data as they were actually released at the time-- and actual real-time analysis, in which a researcher stakes his or her reputation on publicly using the model to generate out-of-sample, real-time predictions. The immediate publication capabilities of the internet make the latter a realistic option for researchers today, and many are taking advantage of it. The paper reviews a number of approaches to dating business cycle turning points and emphasizes the fundamental trade-off between parsimony-- trying to keep the model as simple and robust as possible-- and making full use of available information. Different approaches have different advantages, and the paper concludes that there may be gains from combining the best features of several different approaches. |
| JEL: | E32 |
| Date: | 2010–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:16162&r=ets |