nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒11‒02
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Predicting trend reversals using market instantaneous state By Thomas Bury
  2. Testing for Panel Unit Roots under General Cross-Sectional Dependence By Holgersson, Thomas; Månsson, Kristofer; Shukur, Ghazi
  3. Model Selection in the Presence of Incidental Parameters By Yoonseok Lee; Peter C.B. Phillips
  4. Finite-Sample Resampling-Based Combined Hypothesis Tests, with Applications to Serial Correlation and Predictability By Jean-Marie DUFOUR; Lynda KHALAF; Marcel VOIA
  5. Bayesian Variable Selection for Nowcasting Economic Time Series By Steven L. Scott; Hal R. Varian
  6. Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview By Jennifer Castle; David Hendry
  7. Detecting Big Structural Breaks in Large Factor Models By Liang Chen; Juan Dolado; Jesus Gonzalo

  1. By: Thomas Bury
    Abstract: Collective behaviors taking place in financial markets reveal strongly correlated states especially during a crisis period. A natural hypothesis is that trend reversals are also driven by mutual influences between the different stock exchanges. Using a maximum entropy approach, we find coordinated behavior during trend reversals dominated by the pairwise component. In particular, these events are predicted with high significant accuracy by the ensemble's instantaneous state.
    Date: 2013–10
  2. By: Holgersson, Thomas (Jönköping International Business School, and Linnaeus University); Månsson, Kristofer (Jönköping International Business School); Shukur, Ghazi (Jönköping International Business School, and Linnaeus University)
    Abstract: In this paper we generalize four tests of multivariate linear hypothesis to panel data unit root testing. The test statistics are invariant to certain linear transformations of data and therefore simulated critical values may conveniently be used. It is demonstrated that all four tests remains well behaved in cases of where there are heterogeneous alternatives and cross-correlations between marginal variables. A Monte Carlo simulation is included to compare and contrast the tests with two well-established ones.
    Keywords: panel data; unit roots; linear hypothesis; invariance
    JEL: C32 C52
    Date: 2013–10–21
  3. By: Yoonseok Lee (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Peter C.B. Phillips (Yale University, New Haven, CT 06520)
    Abstract: This paper considers model selection in nonlinear panel data models where incidental parameters or large-dimensional nuisance parameters are present. Primary interest typically centers on selecting a model that best approximates the underlying structure involving parameters that are common within the panel after concentrating out the incidental parameters. It is well known that conventional model selection procedures are often inconsistent in panel models and this can be so even without nuisance parameters (Han et al, 2012). Modifications are then needed to achieve consistency. New model selection information criteria are developed here that use either the Kullback-Leibler information criterion based on the profile likelihood or the Bayes factor based on the integrated likelihood with the robust prior of Arellano and Bonhomme (2009). These model selection criteria impose heavier penalties than those associated with standard information criteria such as AIC and BIC. The additional penalty, which is datadependent, properly reflects the model complexity arising from the presence of incidental parameters. A particular example is studied in detail involving lag order selection in dynamic panel models with fixed individual effects. The new criteria are shown to control for over/underselection probabilities in these models and lead to consistent order selection criteria.
    Keywords: (Adaptive) model selection, incidental parameters, profile likelihood, Kullback- Leibler information, Bayes factor, integrated likelihood, robust prior, model complexity, fixed effects, lag order
    JEL: C23 C52
    Date: 2013–10
  4. By: Jean-Marie DUFOUR; Lynda KHALAF; Marcel VOIA
    Abstract: This paper suggests Monte Carlo multiple test procedures which are provably valid in finite samples. These include combination methods originally proposed for independent statistics and further improvements which formalize statistical practice. We also adapt the Monte Carlo test method to non-continuous combined statistics. The methods suggested are applied to test serial dependence and predictability. In particular, we introduce and analyze new procedures that account for endogenous lag selection. A simulation study illustrates the properties of the proposed methods. Results show that concrete and non-spurious power gains (over standard combination methods) can be achieved through the combined Monte Carlo test approach, and confirm arguments in favour of variance-ratio type criteria.
    Keywords: Monte Carlo test, induced test, test combination, simultaneous inference, Variance ratio
    Date: 2013
  5. By: Steven L. Scott; Hal R. Varian
    Abstract: We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.
    JEL: C11 C53
    Date: 2013–10
  6. By: Jennifer Castle; David Hendry
    Abstract: We consider the reasons for nowcasting, how nowcasts can be achieved, and the use and timing of information.� The existence of contemporaneous data such as surveys is a major difference from forecasting, but many of the recent lessons about forecasting remain relevant.� Given the extensive disaggregation over variables underlying flash estimates of aggregates, we show that automatic model selection can play a valuable role, especially when location shifts would otherwise induce nowcast failure.� Thus, we address nowcasting when location shifts occur, probably with measurement error.� We describe impulse-indicator saturation as a potential solution to such shifts, noting its relation to intercept corrections and to robust methods to avoid systematic nowcast failure.� We propose a nowcasting strategy, building models of all disaggregate series by automatic methods, forecasting all variables before the end of each period, testing for shifts as available measures arrive, and adjusting forecasts of cognate missing series if substantive discrepancies are found.� An alternative is switching to robust forecasts when breaks are detected.� We apply a variant of this strategy to nowcast UK GDP growth, seeking pseudo real-time data availability.
    Keywords: Nowcasting, Location shifts, Forecasting, Contemporaneous information, Autometrics, Impulse-indicator saturation
    JEL: C52 C51
    Date: 2013–09–27
  7. By: Liang Chen; Juan Dolado; Jesus Gonzalo
    Abstract: Time invariance of factor loadings is a standard assumption in the analysis of large factor models.� Yet, this assumption may be restrictive unless parameter shifts are mild (i.e., local to zero).� In this paper we develop a new testing procedure to detect big breaks in these loadings at either known or unknown dates.� It relies upon testing for parameter breaks in a regression of one of the factors estimated by Principal Components analysis on the remaining estimated factors, where the number of factors is chosen according to Bai and Ng's (2002) information criteria.� The test fares well in terms of power relative to other recently proposed tests on this issue, and can be easily implemented to avoid forecasting failures in standard factor-augmented (FAR, FAVAR) models where the number of factors is a priori imposed on the basis of theoretical considerations.
    Keywords: Structural break, large factor model, factor loadings, principal components
    JEL: C12 C33
    Date: 2013–10–14

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