nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒11‒18
fourteen papers chosen by
Yong Yin
SUNY at Buffalo

  1. Accurate Value-at-Risk Forecast with the (good old) Normal-GARCH Model By Christoph Hartz; Stefan Mittnik; Marc S. Paolella
  2. Testing for the Cointegrating Rank of a VAR Process with Level Shift and Trend Break By Carsten Trenkler; Pentti Saikkonen; Helmut Luetkepohl
  3. Testing Models of Low-Frequency Variability By Ulrich Mueller; Mark W. Watson
  4. International Macroeconomic Dynamics: A Factor Vector Autoregressive Approach By Fabio C. Bagliano; Claudio Morana
  5. A Closer Look at Serial Growth Rate Correlation By Alex Coad
  6. Testing for unit roots in three-dimensional heterogeneous panels in the presence of cross-sectional dependence By Giulietti, Monica; Otero, Jesús; Smith, Jeremy
  7. Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation. By Clements, Michael P; Galvão, Ana Beatriz
  8. Forecast Encompassing Tests and Probability Forecasts By Clements, Michael P; Harvey, David I
  9. Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? By De Mol, Christine; Giannone, Domenico; Reichlin, Lucrezia
  10. Real-time forecasting of GDP based on a large factor model with monthly and quarterly data By Schumacher, Christian; Breitung, Jörg
  11. Regime Switching and Artificial Neural Network Forecasting By Eleni Constantinou; Robert Georgiades; Avo Kazandjian; George Kouretas
  12. Beyond Optimal Forecasting By Richard A. Ashley.
  13. A New Bispectral Test for Nonlinear Serial Dependence By Elena Rusticelli; Richard A. Ashley; Estela Bee Dagum; Douglas M. Patterson
  14. Frequency Dependence in Regression Model Coefficients: An Alternative Approach for Modeling Nonlinear Dynamic Relationships in Time Series By Richard A. Ashley.; Randall J. Verbrugge

  1. By: Christoph Hartz (University of Munich); Stefan Mittnik (University of Munich, Center for Financial Studies and ifo); Marc S. Paolella (University of Zurich)
    Abstract: A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L.
    Keywords: Bootstrap, GARCH, Value-at-Risk
    JEL: C22 C53 C63 G12
    Date: 2006–11–03
  2. By: Carsten Trenkler; Pentti Saikkonen; Helmut Luetkepohl
    Abstract: A test for the cointegrating rank of a vector autoregressive (VAR) process with a possible shift and broken linear trend is proposed. The break point is assumed to be known. The setup is a VAR process for cointegrated variables. The tests are not likelihood ratio tests but the deterministic terms including the broken trends are removed first by a GLS procedure and a likelihood ratio type test is applied to the adjusted series. The asymptotic null distribution of the test is derived and it is shown by a Monte Carlo experiment that the test has better small sample properties in many cases than a corresponding Gaussian likelihood ratio test for the cointegrating rank.
    Keywords: Cointegration, structural break, vector autoregressive process, error correction model
    JEL: C32
    Date: 2006
  3. By: Ulrich Mueller; Mark W. Watson
    Abstract: We develop a framework to assess how successfully standard times eries models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series. The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models. We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle.
    JEL: C22 E32
    Date: 2006–11
  4. By: Fabio C. Bagliano; Claudio Morana
    Abstract: In this paper international comovements among a set of key real and nominal macroeconomic variables for the G-7 countries have been investigated for the 1980- 2005 period, using a Factor Vector Autoregressive approach. We present evidence that comovements in macroeconomic variables do not concern only real activity, but are an important feature also of stock market returns, in‡ation rates, interest rates and, to a smaller extent, monetary aggregates. Both common sources of shocks and similar transmission mechanisms explain international comovements, with the only exception of Japan, where the idiosyncratic features seem to dominate. Finally, concerning the origin of global shocks, evidence of both global supply-side and demand-side disturbances is found.
    Keywords: G7, international business cycle, factor vector autoregressive models, common factors.
    JEL: C22 E31 E32
    Date: 2006
  5. By: Alex Coad
    Abstract: Serial correlation in annual growth rates carries a lot of information on growth pro-cesses – it allows us to directly observe firm performance as well as to test theories. Using a 7-year balanced panel of 10 000 French manufacturing firms, we observe that small firms typically are subject to negative correlation of annual growth rates, whereas larger firms display positive correlation. Furthermore, we find that those small firms that experience extreme positive or negative growth in any one year are unlikely to repeat this performance in the following year
    Keywords: Serial correlation, firm growth, quantile regression, French manufacturing, fast-growth firms
    Date: 2006–11–03
  6. By: Giulietti, Monica (Aston Business School, University of Aston); Otero, Jesús (Facultad de Economía, Universidad del Rosario,); Smith, Jeremy (Department of Economics, University of Warwick)
    Abstract: This paper extends the cross-sectionally augmented IPS (CIPS) test of Pesaran (2006) to a three-dimensional (3D) panel. This 3D-CIPS test is correctly sized in the presence of cross-sectional dependency. Comparing its power performance to that of a bootstrapped IPS (BIPS) test, we find that the BIPS test invariably dominates, although for high levels of cross-sectional dependency the 3D-CIPS test can out-perform the BIPS test.
    Keywords: Heterogeneous dynamic panels ; Monte Carlo ; unit roots ; cross-sectional dependence
    JEL: C12 C15 C22 C23
    Date: 2006
  7. By: Clements, Michael P (Department of Economics, University of Warwick); Galvão, Ana Beatriz (Bank of Portugal)
    Abstract: Although many macroeconomic series such as US real output growth are sampled quarterly, many potentially useful predictors are observed at a higher frequency. We look at whether a recently developed mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth and inflation. We carry out a number of related real-time forecast comparisons using various indicators as explanatory variables. We find that MIDAS model forecasts of output growth are more accurate at horizons less than one quarter using coincident indicators ; that MIDAS models are an effective way of combining information from multiple indicators ; and that the forecast accuracy of the unemployment-rate Phillips curve for inflation is enhanced using the MIDAS approach.
    Keywords: Data frequency ; multiple predictors ; combination ; real-time forecasting
    JEL: C51 C53
    Date: 2006
  8. By: Clements, Michael P (Department of Economics, University of Warwick); Harvey, David I (School of Economics, University of Nottingham)
    Abstract: We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecasting models’ parameters on these distributions. The small-sample performance of the various statistics is investigated, both in terms of small numbers of forecasts and model estimation sample sizes. Two empirical applications show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables, and for evaluating survey-based probability forecasts. Probability forecasts ; encompassing tests ; recession probabilities
    JEL: C12 C15 C53
    Date: 2006
  9. By: De Mol, Christine; Giannone, Domenico; Reichlin, Lucrezia
    Abstract: This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section.
    Keywords: Bayesian VAR, ridge regression, Lasso regression, principal components, large cross-sections
    JEL: C11 C13 C33 C53
    Date: 2006
  10. By: Schumacher, Christian; Breitung, Jörg
    Abstract: This paper discusses a factor model for estimating monthly GDP using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm combined with a principal components estimator. We discuss the in-sample properties of the estimator in real-time environments and methods for out-of-sample forecasting. As an empirical application, we estimate monthly German GDP in real-time, discuss the nowcast and forecast accuracy of the model and the role of revisions. Furthermore, we assess the contribution of timely monthly data to the forecast performance.
    Keywords: monthly GDP, EM algorithm, principal components, factor models
    JEL: C53 E37
    Date: 2006
  11. By: Eleni Constantinou (Department of Accounting and Finance, The Philips College, 4-6 Lamias Street, CY-2100, Nicosia,); Robert Georgiades (Department of Accounting and Finance, The Philips College, 4-6 Lamias Street, CY-2100, Nicosia,); Avo Kazandjian (Department of Business Studies, The Philips College, 4-6 Lamias Street, CY-2100, Nicosia, Cyprus.); George Kouretas (Department of Economics, University of Crete, Greece)
    Abstract: This paper provides an analysis of regime switching in volatility and out-of-sample forecasting of the Cyprus Stock Exchange using daily data for the period 1996-2002. We first model volatility regime switching within a univariate Markov-Switching framework. Modelling stock returns within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. The results show that linearity is rejected in favour of a MS specification, which forms statistically an adequate representation of the data. Two regimes are implied by the model; the high volatility regime and the low volatility one and they provide quite accurately the state of volatility associated with the presence of a rational bubble in the capital market of Cyprus. Another implication is that there is evidence of regime clustering. We then provide out-of-sample forecasts of the CSE daily returns using two competing non-linear models, the univariate Markov Switching model and the Artificial Neural Network Model. The comparison of the out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano (1995) test and forecast encompassing, using the Clements and Hendry (1998) test. The results suggest that both non-linear models equivalent in forecasting accuracy and forecasting encompassing and therefore on forecasting performance.
    Keywords: Regime switching, artificial neural networks, stock returns, forecast
    JEL: G
    Date: 2005–01
  12. By: Richard A. Ashley.
    Keywords: forecasting,forecast loss functions,stochastic dominance.
    Date: 2006
  13. By: Elena Rusticelli; Richard A. Ashley; Estela Bee Dagum; Douglas M. Patterson
    Keywords: Bispectrum, nonlinearity, time series analysis
    Date: 2006
  14. By: Richard A. Ashley.; Randall J. Verbrugge
    Keywords: Phillips Curve, spectral regression, time series analysis
    Date: 2006

This nep-ets issue is ©2006 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.