nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒02‒26
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Long-Run Covariability By Ulrich K. Müller; Mark W. Watson
  2. Modeling time series with zero observations By Andrew Harvey; Ryoko Ito
  3. Structural Change in (Economic) Time Series By Christian Kleiber
  4. Testing for volatility co-movement in bivariate stochastic volatility models By Jinghui Chen; Masahito Kobayashi; Michael McAleer
  5. Joint Forecast Combination of Macroeconomic Aggregates and Their Components By Cobb, Marcus P A
  6. Aggregate Density Forecasting from Disaggregate Components Using Large VARs By Cobb, Marcus P A

  1. By: Ulrich K. Müller; Mark W. Watson
    Abstract: We develop inference methods about long-run comovement of two time series. The parameters of interest are defined in terms of population second-moments of lowfrequency trends computed from the data. These trends are similar to low-pass filtered data and are designed to extract variability corresponding to periods longer than the span of the sample divided by q/2, where q is a small number, such as 12. We numerically determine confidence sets that control coverage over a wide range of potential bivariate persistence patterns, which include arbitrary linear combinations of I(0), I(1), near unit roots and fractionally integrated processes. In an application to U.S. economic data, we quantify the long-run covariability of a variety of series, such as those giving rise to the “great ratios”, nominal exchange rates and relative nominal prices, unemployment rate and inflation, money growth and inflation, earnings and stock prices, etc.
    JEL: C22 C53 E17
    Date: 2017–02
  2. By: Andrew Harvey (Faculty of Economics, Cambridge University); Ryoko Ito (Dept of Economics and Nuffield College, Oxford University)
    Abstract: We consider situations in which a signi?cant proportion of observations in a time series are zero, but the remaining observations are positive and measured on a continuous scale. We propose a new dynamic model in which the conditional distribution of the observations is constructed by shifting a distribution for non-zero observations to the left and censoring negative values. The key to generalizing the censoring approach to the dynamic case is to have (the logarithm of) the location/scale parameter driven by a ?lter that depends on the score of the conditional distribution. An exponential link function means that seasonal effects can be incorporated into the model and this is done by means of a cubic spline (which can potentially be time-varying). The model is ?tted to daily rainfall in northern Australia and compared with a dynamic zero-augmented model.
    Keywords: Censored distributions; dynamic conditional score model; generalized beta distribution; rainfall; seasonality, zero aug- mented model.
    JEL: C22
    Date: 2017–02–21
  3. By: Christian Kleiber
    Abstract: Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. Several recent methods for dating structural changes are compared using a time series of oil prices spanning more than 60 years. The methods broadly agree for the first part of the series up to the mid-1980s, for which changes are associated with major historical events, but provide somewhat different solutions thereafter, reflecting a gradual increase in oil prices that is not well described by a step function. As a further illustration, 1990s data on the volatility of the Hang Seng stock market index are reanalyzed.
    Date: 2017–02
  4. By: Jinghui Chen (Graduate School of International Social Sciences Yokohama National University.); Masahito Kobayashi (Department of Economics Yokohama National University.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan And Econometric Institute, Erasmus School of Economics Erasmus University Rotterdam And Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)
    Abstract: The paper considers the problem of volatility co-movement, namely as to whether two financial returns have perfectly correlated common volatility process, in the framework of multivariate stochastic volatility models and proposes a test which checks the volatility co-movement. The proposed test is a stochastic volatility version of the co-movement test proposed by Engle and Susmel (1993), who investigated whether international equity markets have volatility co-movement using the framework of the ARCH model. In empirical analysis we found that volatility co-movement exists among closelylinked stock markets and that volatility co-movement of the exchange rate markets tends to be found when the overall volatility level is low, which is contrasting to the often-cited finding in the financial contagion literature that financial returns have co-movement in the level during the financial crisis.
    Keywords: Lagrange multiplier test, Volatility co-movement, Stock markets, Exchange rate Markets, Financial crisis.
    JEL: C12 C58 G01 G11
    Date: 2017–02
  5. By: Cobb, Marcus P A
    Abstract: This paper presents a framework that extends forecast combination to include an aggregate and its components in the same process. This is done with the objective of increasing aggregate forecasting accuracy by using relevant disaggregate information and increasing disaggregate forecasting accuracy by providing a binding context for the component’s forecasts. The method relies on acknowledging that underlying a composite index is a well defined structure and its outcome is a fully consistent forecasting scenario. This is particularly relevant for people that are interested in certain components or that have to provide support for a particular aggregate assessment. In an empirical application with GDP data from France, Germany and the United Kingdom we find that the outcome of the combination method shows equal aggregate accuracy to that of equivalent traditional combination methods and a disaggregate accuracy similar or better to that of the best single models.
    Keywords: Bottom-up forecasting; Forecast combination; Hierarchical forecasting; Reconciling forecasts
    JEL: C53 E27 E37
    Date: 2017–02
  6. By: Cobb, Marcus P A
    Abstract: When it comes to point forecasting there is a considerable amount of literature that deals with ways of using disaggregate information to improve aggregate accuracy. This includes examining whether producing aggregate forecasts as the sum of the component’s forecasts is better than alternative direct methods. On the contrary, the scope for producing density forecasts based on disaggregate components remains relatively unexplored. This research extends the bottom-up approach to density forecasting by using the methodology of large Bayesian VARs to estimate the multivariate process and produce the aggregate forecasts. Different specifications including both fixed and time-varying parameter VARs and allowing for stochastic volatility are considered. The empirical application with GDP and CPI data for Germany, France and UK shows that VARs can produce well calibrated aggregate forecasts that are similar or more accurate than the aggregate univariate benchmarks.
    Keywords: Density Forecasting; Bottom-up forecasting; Hierarchical forecasting; Bayesian VAR; Forecast calibration
    JEL: C32 C53 E37
    Date: 2017–02

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