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on Econometric Time Series |
| By: | Tschernig, Rolf; Weber, Enzo; Weigand, Roland |
| Abstract: | We state that long-run restrictions that identify structural shocks in VAR models with unit roots lose their original interpretation if the fractional integration order of the affected variable is below one. For such fractionally integrated models we consider a medium-run approach that employs restrictions on variance contributions over finite horizons. We show for alternative identification schemes that letting the horizon tend to infinity is equivalent to imposing the restriction of Blanchard and Quah (1989) introduced for the unit-root case. |
| Keywords: | Structural vector autoregression; long-run restriction; finite-horizon identification; fractional integration; impulse response function |
| JEL: | C32 C50 |
| Date: | 2013–12 |
| URL: | http://d.repec.org/n?u=RePEc:bay:rdwiwi:29162&r=ets |
| By: | Osmani Teixeira de Carvalho Guillén; Alain Hecq; João Victor Issler; Diogo Saraiva |
| Abstract: | This paper has two original contributions. First, we show that PV relationships entail a weak-form SCCF restriction, as in Hecq et al. (2006) and in Athanasopoulos et al. (2011), and implies a polynomial serial correlation common feature relationship (Cubadda and Hecq, 2001). These represent short-run restrictions on the dynamic multivariate systems, something that has not been discussed before. Our second contribution relates to forecasting multivariate time series that are subject to PVM restrictions, which has a wide application in macroeconomics and finance. We benefit from previous work showing the benefits for forecasting when the short-run dynamics of the system is constrained. The reason why appropriate common-cycle restrictions improve forecasting is because it finds linear combinations of the first differences of the data that cannot be forecast by past information. This embeds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to PV restrictions: the online series maintained and updated by Robert J. Shiller at http://www.econ.yale.edu/~shiller/data.htm. We focus on three different data sets. The first includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of six different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Specifically, cointegration restrictions, and cointegration and weak-form SCCF rank restrictions, as well as all the set of theoretical restrictions embedded in the PVM |
| Date: | 2013–10 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:330&r=ets |
| By: | Heinemann A. (GSBE) |
| Abstract: | This paper studies an alternative approach to construct confidence intervals for parameter estimates of the Lee-Carter model. First, the procedure of obtaining confidence intervals using regular nonparametric i.i.d. bootstrap is specified. Empirical evidence seems to invalidate this approach as it indicates strong autocorrelation and cross correlation in the residuals. A more general approach is introduced, relying on the Sieve bootstrap method, that includes the nonparametric i.i.d. method as a special case. Secondly, this paper examines the performance of the nonparametric i.i.d. and the Sieve bootstrap approach. In an application to a Dutch data set, the Sieve bootstrap method returns much wider confidence intervals compared to the nonparametric i.i.d. approach. Neglecting the residuals dependency structure, the nonparametric i.i.d. bootstrap method seems to construct confidence intervals that are too narrow. Third, the paper discusses an intuitive explanation for the source of autocorrelation and cross correlation within stochastic mortality models. |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umagsb:2013069&r=ets |
| By: | Rotta, Pedro Nielsen; Pereira, Pedro Luiz Valls |
| Abstract: | Over the last decades, the analysis of the transmissions of international nancial events has become the subject of many academic studies focused onmultivariate volatility models volatility. The goal of this study is to evaluatethe nancial contagion between stock market returns. The econometricapproach employed was originally presented by Pelletier (2006), namedRegime Switching Dynamic Correlation (RSDC). This methodology involvesthe combination of Constant Conditional Correlation Model (CCC) proposedby Bollerslev (1990) with Markov Regime Switching Model suggested by Hamiltonand Susmel (1994). A modi cation was made in the original RSDC model,the introduction of the GJR-GARCH model formulated in Glosten, Jagannathane Runkle (1993), on the equation of the conditional univariate variancesto allow asymmetric e ects in volatility be captured. The database wasbuilt with the series of daily closing stock market indices in the United States(SP500), United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea(KOSPI) for the period from 02/01/2003 to 09/20/2012. Throughout the workthe methodology was compared with others most widespread in the literature,and the model RSDC with two regimes was de ned as the most appropriatefor the selected sample. The set of results provide evidence for the existenceof nancial contagion between markets of the four countries considering thede nition of nancial contagion from the World Bank called very restrictive.Such a conclusion should be evaluated carefully considering the wide diversityof de nitions of contagion in the literature. |
| Date: | 2013–12–06 |
| URL: | http://d.repec.org/n?u=RePEc:fgv:eesptd:340&r=ets |
| By: | Guillaume Chevillon (ESSEC Business School - ESSEC Business School) |
| Abstract: | Standard tests for the rank of cointegration of a vector autoregressive process present distributions that are affected by the presence of deterministic trends. We consider the recent approach of Demetrescu et al. (2009) who recommend testing a composite null. We assess this methodology in the presence of trends (linear or broken) whose magnitude is small enough not to be detectable at conventional significance levels. We model them using local asymptotics and derive the properties of the test statistics. We show that whether the trend is orthogonal to the cointegrating vector has a major impact on the distributions but that the test combination approach remains valid. We apply of the methodology to the study of cointegration properties between global temperatures and the radiative forcing of human gas emissions. We find new evidence of Granger Causality. |
| Keywords: | Cointegration ; Deterministic Trend ; Global Warming ; Likelihood Ratio ; Local Trends |
| Date: | 2013–11 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-00914830&r=ets |
| By: | Felix Chan (School of Economics and Finance, Curtin University) |
| Abstract: | Since the seminal work of Engle and Granger (1987) and Johansen (1988), testing for cointegration has become standard practice in analysing economic and financial time series data. Many of the techniques in cointegration analysis require the assumption of normality, which may not always hold. Although there is evidence that these techniques are robust to non-normality, most existing techniques do not seek additional information from non-normality. This is important in at least two cases. Firstly, the number of observations is typically small for macroeconomic time series data, the fact that the underlying distribution may not be normal provides important information that can potentially be useful in testing for cointegrating relationships. Secondly, high frequency financial time series data often shows evidence of non-normal random variables with time-varying second moments and it is unclear how these characteristics affect the standard test of cointegration, such as Johansen's trace and max tests. This paper proposes a new framework derived from Independent Component Analysis (ICA) to test for cointegration. The framework explicitly exploits processes with non-normal distributions and their independence. Monte Carlo simulation shows that the new test is comparable to the Johansen's trace and max tests when the number of observations is large and has a slight advantage over Johansen's tests if the number of observations is limited. Moreover, the computational requirement for this method is relatively mild, which makes this method practical for empirical research. |
| Keywords: | Blind Source Separation, Independent Component Analysis, Cointegration Rank |
| JEL: | C13 C32 C53 |
| Date: | 2013–07 |
| URL: | http://d.repec.org/n?u=RePEc:ozl:bcecwp:wp1306&r=ets |
| By: | Bayraci, Selcuk; Demiralay, Sercan |
| Abstract: | : We examine the volatility spillovers among major Eurozone countries employing the Diebold and Yilmaz (2012) model with time-varying conditional ranges generated from conditional autoregressive range (CARR) model of Chou (2005). The empirical findings, based on a data set covering a fifteen year period (1998-2013), suggest a total volatility spillover index in a very high degree. 74.9% of total volatility in the Eurozone markets is attributed to spillover effects from other markets. Moreover, rolling window analysis shows that volatility spillover index is relatively higher during the turmoil periods. |
| Keywords: | CARR, financial crisis, volatility spillover index, Eurozone |
| JEL: | C32 G01 G10 |
| Date: | 2013–11–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:51909&r=ets |
| By: | Brandon J. Bates; Mikkel Plagborg-Møller; James H. Stock; Mark W. Watson |
| Abstract: | This paper considers the estimation of approximate dynamic factor models when there is temporal instability in the factor loadings. We characterize the type and magnitude of instabilities under which the principal components estimator of the factors is consistent and find that these instabilities can be larger than earlier theoretical calculations suggest. We also discuss implications of our results for the robustness of regressions based on the estimated factors and of estimates of the number of factors in the presence of parameter instability. Simulations calibrated to an empirical application indicate that instability in the factor loadings has a limited impact on estimation of the factor space and diffusion index forecasting, whereas estimation of the number of factors is more substantially affected. |
| URL: | http://d.repec.org/n?u=RePEc:qsh:wpaper:84631&r=ets |
| By: | Barbara Rossi; Atsushi Inoue |
| Abstract: | This paper proposes new methodologies for evaluating out-of-sample forecasting performance that are robust to the choice of the estimation window size. The methodologies involve evaluating the predictive ability of forecasting models over a wide range of window sizes. We show that the tests proposed in the literature may lack the power to detect predictive ability and might be subject to data snooping across different window sizes if used repeatedly. An empirical application shows the usefulness of the methodologies for evaluating exchange rate models' forecasting ability. |
| Keywords: | Predictive Ability Testing, Forecast Evaluation, Estimation Window. |
| JEL: | C22 C52 C53 |
| Date: | 2012–04 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1404&r=ets |