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on Econometric Time Series |
| By: | Raffaella Giacomini; Barbara Rossi |
| Abstract: | This review provides an overview of forecasting methods that can help researchers forecast in the presence of non-stationarities caused by instabilities. The emphasis of the review is both theoretical and applied, and provides several examples of interest to economists. We show that modeling instabilities can help, but it depends on how they are modeled. We also show how to robustify a model against instabilities. |
| Keywords: | Forecasting, instabilities, structural breaks. |
| Date: | 2014–12 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1476&r=ets |
| By: | Xi-Yuan Qian (ECUST); Ya-Min Liu (ECUST); Zhi-Qiang Jiang (ECUST); Boris Podobnik (BU and ZSEM); Wei-Xing Zhou (ECUST); H. Eugene Stanley (BU) |
| Abstract: | We propose a new method, detrended partial cross-correlation analysis (DPXA), to uncover the intrinsic power-law cross-correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis by taking into account the partial correlation analysis. We illustrate the performance of the method using bivariate fractional Brownian motions and multifractal binomial measures with analytical expressions and apply it to extract the intrinsic cross-correlation between crude oil and gold futures by considering the impact of the US dollar index. |
| Date: | 2015–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1504.02435&r=ets |
| By: | Kurennoy, Alexey (Russian Presidential Academy of National Economy and Public Administration (RANEPA)) |
| Abstract: | This preprint describes a number of statistical tests for (unselective) assess the quality of forecasting. For each of these assumptions are presented and discussed to be executed for the corresponding test can be used. In addition, preprint extends the scope of applicability of the Giacomini and White tests, spreading them in the event of forecasts prepared according to the recursive scheme, but almost entirely dependent on the short-term observations. |
| Keywords: | forecasting, forecasting quality, Giacomini test, White test |
| Date: | 2015–03 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:ppaper:mak7&r=ets |
| By: | Skrobotov, Anton (Russian Presidential Academy of National Economy and Public Administration (RANEPA)) |
| Abstract: | Recent approaches to testing the hypothesis of a unit root that take into account the effect of the initial value, trend and changes in the data using pre-tested of the initial value, trend and shifts, and on the basis of this use the strategy of combining of rejections of several tests. This allows the use of more powerful tests if there is some uncertainty about the model parameters. In this paper we propose a generalization of the approach Harvey et al. (2012b) in the event of uncertainty about the initial value. It is shown that this approach has a low power at high initial value, because it includes tests based on the GLS-detrending. Therefore, we investigate the effectiveness of some tests for unit root ADF-type taking into account the shift at different values of the initial value, and proposes a decision rule based on the additional pre-testing the value of the initial value and the simultaneous use of tests based on the GLS, and OLS-detrending. In addition, modification of the proposed algorithm are discussed: the use of coefficient pre-test of the trend, the possible presence of multiple structural breaks in the trend and the presence of partial information about the location of the shift. The asymptotic behavior of all the tests is analyzed with local representation and autoregressive parameter and parameters in trend and shifts. The proposed modification are showing good properties asymptotically and in finite samples at different values of interfering parameters. |
| Keywords: | Dickey-Fuller test, the local trend, local shift in the trend, the asymptotic local power, uniting of the rejections, pre-testing, several changes in the trend |
| JEL: | C12 C22 |
| Date: | 2015–03 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:ppaper:mak6&r=ets |
| By: | Skrobotov, Anton (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Turuntseva, Marina (Russian Presidential Academy of National Economy and Public Administration (RANEPA)) |
| Abstract: | In this paper an overview of methods for the analysis of structural VAR models is provided. The fundamental properties of SVAR models, the estimated parameters, as well as various methods of identifying shocks and pritsnipe construct confidence intervals for impulse responses, are discussed. The paper also discusses the problems associated with non-stationary variables. |
| Keywords: | structural VAR models (SVAR), structural VECM (SVECM), impulse responses, decomposition of the forecast error variances, the identification of shocks |
| Date: | 2015–03 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:ppaper:mak8&r=ets |
| By: | Markku Lanne (University of Helsinki and CREATES); Mika Meitz (University of Helsinki); Pentti Saikkonen (University of Helsinki) |
| Abstract: | Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are typically imposed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non-Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions. |
| Keywords: | Structural vector autoregressive model, identification, impulse responses, non-Gaussianity |
| JEL: | C13 C32 C53 |
| Date: | 2015–03–30 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2015-16&r=ets |