nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒02‒20
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Forecasting economic activity with higher frequency targeted predictors By Guido Bulligan; Massimiliano Marcellino; Fabrizio Venditti
  2. Particle Filters for Markov Switching Stochastic Volatility Models By Yun Bao; Carl Chiarella
  3. Stochastic Correlation and Risk Premia in Term Structure Models By Carl Chiarella; Chih-Ying Hsiao; Thuy-Duong To
  4. Evaluating the calibration of multi-step-ahead density forecasts using raw moments By Knüppel, Malte
  5. Almost periodically correlated time series in business fluctuations analysis By Łukasz Lenart; Mateusz Pipień
  6. Parametric estimation of hidden stochastic model by contrast minimization and deconvolution: application to the Stochastic Volatility Model By Salima El Kolei
  7. Identifying speculative bubbles with an in finite hidden Markov model By Song, Yong; Shi, Shuping
  8. Forecasting multivariate volatility in larger dimensions: some practical issues By Adam E Clements; Ayesha Scott; Annastiina Silvennoinen
  9. Bootstrap determination of the co-integration rank in VAR models By Giuseppe Cavaliere; Anders Rahbek; Taylor A.M.Robert
  10. On tests for linearity against STAR models with deterministic trends By Kaufmann, Hendrik; Kruse, Robinson; Sibbertsen, Philipp

  1. By: Guido Bulligan (Bank of Italy); Massimiliano Marcellino (European University Institute, Bocconi University); Fabrizio Venditti (Bank of Italy)
    Abstract: In this paper we explore the performance of bridge and factor models in forecasting quarterly aggregates in the very short-term subject to a pre-selection of monthly indicators. Starting from a large information set, we select a subset of targeted predictors using data reduction techniques as in Bai and Ng (2008). We then compare a Diffusion Index forecasting model as in Stock and Watson (2002), with a Bridge model specified with an automated General-To-Specific routine. We apply these techniques to forecasting Italian GDP growth and its main components from the demand side and find that Bridge models outperform naive forecasts and compare favorably against factor models. Results for France, Germany, Spain and the euro area confirm these findings.
    Keywords: short-term GDP forecast, factor models, bridge models, General To Specific
    JEL: C52 C53 E37
    Date: 2012–01
  2. By: Yun Bao (Toyota Financial Services Australia); Carl Chiarella (Finance Discipline Group, UTS Business School, University of Technology, Sydney; Finance Discipline Group, UTS Business School, University of Technology, Sydney)
    Abstract: This paper proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed by a first-order Markov chain. We proposes an ongoing updated Dirichlet distribution to estimate the transition probabilities of the Markov chain in the auxiliary particle filter. A simulation-based algorithm is presented for the method which demonstrated that we are able to estimate a class of models in which the probability that the system state transits from one regime to a different regime is relatively high. The methodology is implemented to analyze a real time series: the foreign exchange rate of Australian dollars vs South Korean won.
    Keywords: Particle filters; Markov switching stochastic volatility models; Sequential Monte Carlo simulation
    JEL: C61 D11
    Date: 2012–01–01
  3. By: Carl Chiarella (Finance Discipline Group, UTS Business School, University of Technology, Sydney); Chih-Ying Hsiao (Commonwealth Bank of Australia); Thuy-Duong To (School of Banking and Finance, University of NSW)
    Abstract: This paper proposes and analyses a term structure model that allows for both stochastic correlation between underlying factors and an extended market price of risk specification. The issues of invariant transformation and different normalization are then considered so that a comparison between different restrictions can be made. We show that signi?cant improvement in bond fitting is obtained by both allowing the market price of risk to have an extended affine form, and allowing the correlation between underlying factors to be stochastic as well as of variable sign. The overall model fit is more negatively impacted by the restriction on the market price of risk than the restriction of correlated factors. However, the stochastic correlation is priced significantly by market participants, though its impact on the risk premia reduces gradually as time to maturity increases. In addition, stochastic correlation is vital in obtaining good hedged portfolio positions. Certainly, the best hedged portfolio is the one that is built based on the model that takes into account both stochastic correlation and extended market price of risk.
    Keywords: Term structure; Stochastic correlation, Risk premium; Wishart; Af?ne; Extended af?ne; Multidimensional CIR
    JEL: E43 C51
    Date: 2011–12–01
  4. By: Knüppel, Malte
    Abstract: The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found which take this problem into account. However, these approaches can be computationally burdensome, ignore important information and therefore lack power, or suffer from size distortions even asymptotically. In this work, a fourth testing approach based on raw moments is proposed. It is easy to implement, uses standard critical values, can include all moments regarded as important, and has correct asymptotic size. It is found to have good size and power properties if it is based directly on the (standardized) probability integral transforms. --
    Keywords: density forecast evaluation,normality tests
    JEL: C12 C52 C53
    Date: 2011
  5. By: Łukasz Lenart (Economic Institute in National Bank of Poland, Department of Mathematics in Cracow University of Economics); Mateusz Pipień (Economic Institute in National Bank of Poland, Department of Econometrics and Operations Research in Cracow University of Economics)
    Abstract: We propose a non-standard subsampling procedure to make formal statistical inference about the business cycle, one of the most important unobserved feature characterising fluctuations of economic growth. We show that some characteristics of business cycle can be modelled in a non-parametric way by discrete spectrum of the Almost Periodically Correlated (APC) time series. On the basis of estimated characteristics of this spectrum business cycle is extracted by filtering. As an illustration we characterise the man properties of business cycles in industrial production index for Polish economy.
    Keywords: business cycle, industrial production index, almost periodically correlated time series, subsampling procedure
    JEL: C01 C02 C14
    Date: 2012
  6. By: Salima El Kolei
    Abstract: We study a new parametric approach for particular hidden stochastic models such as the Stochastic Volatility model. This method is based on contrast minimization and deconvolution. After proving consistency and asymptotic normality of the estimation leading to asymptotic confidence intervals, we provide a thorough numerical study, which compares most of the classical methods that are used in practice (Quasi Maximum Likelihood estimator, Simulated Expectation Maximization Likelihood estimator and Bayesian estimators). We prove that our estimator clearly outperforms the Maximum Likelihood Estimator in term of computing time, but also most of the other methods. We also show that this contrast method is the most robust with respect to non Gaussianity of the error and also does not need any tuning parameter.
    Date: 2012–02
  7. By: Song, Yong; Shi, Shuping
    Abstract: This paper proposes an infinite hidden Markov model (iHMM) to detect, date stamp,and estimate speculative bubbles. Three features make this new approach attractive to practitioners. First, the iHMM is capable of capturing the nonlinear dynamics of different types of bubble behaviors as it allows an infinite number of regimes. Second, the implementation of this procedure is straightforward as the detection, dating, and estimation of bubbles are done simultaneously in a coherent Bayesian framework. Third, the iHMM, by assuming hierarchical structures, is parsimonious and superior in out-of-sample forecast. Two empirical applications are presented: one to the Argentinian money base, exchange rate, and consumer price from January 1983 to November 1989; and the other to the U.S. oil price from April 1983 to December 2010. We find prominent results, which have not been discovered by the existing finite hidden Markov model. Model comparison shows that the iHMM is strongly supported by the predictive likelihood.
    Keywords: speculative bubbles; in nite hidden Markov model; Dirichlet process
    JEL: C14 C15 C11
    Date: 2012–02–06
  8. By: Adam E Clements (QUT); Ayesha Scott (QUT); Annastiina Silvennoinen (QUT)
    Abstract: The importance of covariance modelling has long been recognised in the field of portfolio management and large dimensional multivariate problems are increasingly becoming the focus of research. This paper provides a straightforward and commonsense approach toward investigating whether simpler moving average based correlation forecasting methods have equal predictive accuracy as their more complex multivariate GARCH counterparts for large dimensional problems. We find simpler forecasting techniques do provide equal (and often superior) predictive accuracy in a minimum variance sense. A portfolio allocation problem is used to compare forecasting methods. The global minimum variance portfolio and Model Confidence Set (Hansen, Lunde, and Nason (2003)) are used to compare methods, whilst portfolio weight stability and computational time are also considered.
    Keywords: Volatility, multivariate GARCH, portfolio allocation
    JEL: C22 G11 G17
    Date: 2012–02–06
  9. By: Giuseppe Cavaliere (Università di Bologna); Anders Rahbek; Taylor A.M.Robert
    Abstract: This paper discusses a consistent bootstrap implementation of the likelihood ratio [LR] co-integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates of the underlying VAR model which obtain under the reduced rank null hypothesis. A full asymptotic theory is provided which shows that, unlike the bootstrap procedure in Swensen (2006) where a combination of unrestricted and restricted estimates from the VAR model is used, the resulting bootstrap data are I(1) and satisfy the null co-integration rank, regardless of the true rank. This ensures that the bootstrap LR test is asymptotically correctly sized and that the probability that the bootstrap sequential procedure selects a rank smaller than the true rank converges to zero. Monte Carlo evidence suggests that our bootstrap procedures work very well in practice.
    Keywords: Bootstrap; Co-integration; Trace statistic; Rank determination Cointegrazione; Statistica “traccia”; determinazione del rango
    Date: 2011
  10. By: Kaufmann, Hendrik; Kruse, Robinson; Sibbertsen, Philipp
    Abstract: Linearity testing against smooth transition autoregressive (STAR) models when deterministic trends are potentially present in the data is considered in this paper. As opposed to recently reported results in Zhang (2012), we show that linearity tests against STAR models lead to useful results in this setting.
    Keywords: Nonlinearity, Smooth transition, Deterministic trend
    JEL: C12 C22
    Date: 2012–02

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