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on Econometric Time Series |
| By: | Paresh Kumar Narayan (School of Accounting, Economics and Finance, Deakin University); Stephan Popp (University of Duisburg-Essen, Germany) |
| Abstract: | In this paper we propose a new ADF-type test for unit roots which accounts for two structural breaks. We consider two different specifications: (a) two breaks in the level of a trending series; and (b) two breaks in the level and slope of trending data. The breaks whose time of occurance is assumed to be unknown are modelled as innovational outliers and thus take effect gradually. Using Monte Carlo simulations, we show that our proposed test has correct size, stable power, and identifies the structural breaks accurately. |
| Date: | 2009–06–24 |
| URL: | http://d.repec.org/n?u=RePEc:dkn:econwp:eco_2009_11&r=ets |
| By: | Franses, Ph.H.B.F. (Erasmus Econometric Institute) |
| Abstract: | Econometric models for economic time series may include harmonic regressors to describe cyclical patterns in the data. This paper focuses on the possibility that the cycle periods in these regressors change over time. To this end, a smooth regime-switching harmonic regression is proposed, and a diagnostic test for changing cycle periods is proposed. An application to annual GDP growth in the Netherlands (for 1969-2007) shows that around 1975 the business cycle period shifted from about 3 years to about 11 years. |
| Keywords: | harmonic regressors;smooth regime-switching model |
| Date: | 2009–07–13 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureir:1765016216&r=ets |
| By: | Chen, Pu |
| Abstract: | In this note the author discusses the problem of updating forecasts in a time-discrete forecasting model when information arrives between the current period and the next period. To use the information that arrives between two periods, he assumes that the process between two periods can be approximated by a linear interpolation of the timediscrete forecasting model. Based on this assumption the author drives the optimal updating rule for the forecast of the next period when new information arrives between the current period and the next period. He demonstrates by theoretical arguments and empirical examples that this updating rule is simple, intuitively appealing, defendable and useful. |
| Keywords: | Forecast |
| JEL: | C32 |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwedp:7586&r=ets |
| By: | Bortoluzzo, Adriana B.; Morettin, Pedro A.; Toloi, Clelia M. C. |
| Date: | 2008–10 |
| URL: | http://d.repec.org/n?u=RePEc:ibm:ibmecp:wpe_172&r=ets |
| By: | Mark J. Jensen (Federal Reserve Bank of Atlanta); John M. Maheu (University of Toronto and RCEA) |
| Abstract: | This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility modelsClassification-JEL: |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:wp23_09&r=ets |
| By: | Andros Kourtellos (Department of Economics, University of Cyprus); Thanasis Stengos (Department of Economics, University of Guelph); Chih Ming Tan (Department of Economics, Tufts University) |
| Abstract: | This paper extends the simple threshold regression framework of Hansen (2000) and Caner and Hansen (2004) to allow for endogeneity of the threshold variable. We develop a concentrated least squares estimator of the threshold parameter based on an inverse Mills ratio bias correction. We show that our estimator is consistent and investigate its performance using a Monte Carlo simulation that indicates the applicability of the method in nite samples Classification-JEL: C13, C51 |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:wp22_09&r=ets |
| By: | Thomas Lux; Leonardo Morales-Arias |
| Abstract: | We examine the performance of volatility models that incorporate features such as long (short) memory, regime-switching and multifractality along with two competing distributional assumptions of the error component, i.e. Normal vs Student-t. Our precise contribution is twofold. First, we introduce a new model to the family of Markov-Switching Multifractal models of asset returns (MSM), namely, the Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t). Second, we perform a comprehensive panel forecasting analysis of the MSM models as well as other competing volatility models of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) legacy. Our cross-sections consist of all-share equity indices, bond indices and real estate security indices at the country level. Furthermore, we investigate complementarities between models via combined forecasts. We find that: (i) Maximum Likelihood (ML) and Generalized Method of Moments (GMM) estimation are both suitable for MSM-t models, (ii) empirical panel forecasts of MSM-t models show an improvement over the alternative volatility models in terms of mean absolute forecast errors and that (iii) forecast combinations obtained from the different MSM and (FI)GARCH models considered appear to provide some improvement upon forecasts from single models |
| Keywords: | Multiplicative volatility models, long memory, Student-t innovations, international volatility forecasting |
| JEL: | C20 G12 |
| Date: | 2009–07 |
| URL: | http://d.repec.org/n?u=RePEc:kie:kieliw:1532&r=ets |
| By: | Chudik, A.; Pesaran, M.H.; Tosetti, E. |
| Abstract: | This paper introduces the concepts of time-specific weak and strong cross section dependence. A double-indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain 'granularity' conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects. |
| Keywords: | Panels, Strong and Weak Cross Section Dependence, Weak and Strong Factors |
| JEL: | C10 C31 C33 |
| Date: | 2009–06–09 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:0924&r=ets |