nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒09‒30
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Real-time Prediction with UK Monetary Aggregates in the Presence of Model Uncertainty By Anthony Garratt; Gary Koop; Emi Mise; Shaun P Vahey
  2. Cyclical Trends in Continuous Time Models By Joanne S. Ercolani
  3. Enhanced routines for instrumental variables/GMM estimation and testing By Christopher F Baum; Mark E. Schaffer; Steven Stillman
  4. Assessing forecast uncertainties in a VECX* model for Switzerland: an exercise in forecast combination across models and observation windows By Pesaran, M.H.; Assenmacher-Wesche, K.
  5. A Note onTesting for Unit Roots in the Unobservable Trend Component of a Structural Model By Fabio Nieto; Eliana González
  6. Theory and inference for a Markov switching GARCH model By Luc, BAUWENS; Arie, PREMINGER; Jeroen, ROMBOUTS
  7. GLS Bias Correction for Low Order ARMA models By Patrick Richard

  1. By: Anthony Garratt (School of Economics, Mathematics & Statistics, Birkbeck); Gary Koop; Emi Mise; Shaun P Vahey
    Abstract: A popular account for the demise of the UK monetary targeting regime in the 1980s blames the weak predictive relationships between broad money and inflation and real output. In this paper, we investigate these relationships using a variety of monetary aggregates which were used as intermediate UK policy targets. We use both real-time and final vintage data and consider a large set of recursively estimated Vector Autoregressive (VAR) and Vector Error Correction models (VECM). These models differ in terms of lag length and the number of cointegrating relationships. Faced with this model uncertainty, we utilize Bayesian model averaging (BMA) and contrast it with a strategy of selecting a single best model. Using the real-time data available to UK policymakers at the time, we demonstrate that the in-sample predictive content of broad money fluctuates throughout the 1980s for both strategies. However, the strategy of choosing a single best model amplifies these fluctuations. Out-of-sample predictive evaluations rarely suggest that money matters for either inflation or real output, regardless of whether we select a single model or do BMA. Overall, we conclude that the money was a weak (and unreliable) predictor for these key macroeconomic variables. But the view that the predictive content of UK broad money diminished during the 1980s receives little support using either the real-time or final vintage data.
    Keywords: Money, Vector Error Correction Models, Model Uncertainty, Bayesian Model Averaging, Real Time Data
    JEL: C11 C32 C53 E51 E52
    Date: 2007–09
  2. By: Joanne S. Ercolani
    Abstract: It is undoubtedly desirable that econometric models capture the dynamic behaviour,like trends and cycles, observed in many economic processes. Building models with such capabilities has been an important objective in the continuous time econometrics literature, see for instance the cyclical growth models of Bergstrom (1966), the complete economy-wide macroeconometric models of, for example, Bergstrom and Wymer (1976), unobserved stochastic trends of Harvey and Stock (1988 and 1993) and Bergstrom (1997), and differential-difference equations of Chambers and McGarry (2002). This paper’s contribution is to examine cyclical trends formulated in continuous time, which complement the trend-plus-cycle models that are frequently used in the unobserved components literature.
    Keywords: Cyclical Trends, continuous time models, stochastic differential equations, differential-difference equations
    JEL: C22
    Date: 2007–09
  3. By: Christopher F Baum (Boston College); Mark E. Schaffer (Heriot-Watt University); Steven Stillman (Motu Economic and Public Policy Research)
    Abstract: We extend our 2003 paper on instrumental variables (IV) and GMM estimation and testing and describe enhanced routines that address HAC standard errors, weak instruments, LIML and k-class estimation, tests for endogeneity and RESET and autocorrelation tests for IV estimates.
    Keywords: instrumental variables, generalized method of moments, endogeneity, heteroskedasticity, autocorrelation, weak instruments, overidentifying restrictions
    JEL: C20 C22 C23 C12 C13 C87
    Date: 2007–05–09
  4. By: Pesaran, M.H.; Assenmacher-Wesche, K.
    Abstract: We investigate the effect of forecast uncertainty in a cointegrating vector error correction model for Switzerland. Forecast uncertainty is evaluated in three different dimensions. First, we investigate the effect on forecasting performance of averaging over forecasts from different models. Second, we look at different estimation windows. We find that averaging over estimation windows is at least as e¤ective as averaging over different models and both complement each other. Third, we explore whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the e¤ect of the weighting scheme on forecast accuracy is small in our application.
    Keywords: Bayesian model averaging, choice of observation window, longrun structural vector autoregression.
    JEL: C53 C32
    Date: 2007–09
  5. By: Fabio Nieto; Eliana González
    Abstract: Testing for unit roots is a common practice in observable stochastic processes and there is abundant literature on this topic. However, sometimes, one is faced with the same problem but in the case where the processes of interest are latent or unobservable. In this paper, empirical distributions of the usual unit-root test statistics are obtained for the trend component of some particular structural models, which are based on optimal predictions (as the observed data) of the trend stochastic process. It is found that these statistical tests tend to be most powerful than the usual Dickey-Fuller tests
    Date: 2007–09–22
  6. By: Luc, BAUWENS (UNIVERSITE CATHOLIQUE DE LOUVAIN, Department of Economics); Arie, PREMINGER (University of Haifa, Israel, Department of Economics); Jeroen, ROMBOUTS (Institute of Applied Economics at HEC Montreal, CIRPEE, CREF, CORE (UniversitŽ catholique de Louvain), CREF)
    Abstract: We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existene of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on SP500 daily returns.
    Keywords: GARCH, Markov-switching, Bayesian inference
    JEL: C11 C22 C52
    Date: 2007–09–18
  7. By: Patrick Richard (GREDI, Département d'économique, Université de Sherbrooke)
    Abstract: We study the problems of bias correction in the estimation of low order ARMA(p, q) time series models. We introduce a new method to estimate the bias of the parameters of ARMA(p, q) process based on the analytical form of the GLS transformation matrix of Galbraith and Zinde-Walsh (1992). We show that the resulting bias corrected estimator is consistent and asymptotically normal. We also argue that, in the case of an MA(q) model, our method may be considered as an iteration of the analytical indirect inference technique of Galbraith and Zinde-Walsh (1994). The potential of our method is illustrated through a series of Monte Carlo experiments.
    Keywords: ARMA; bias correction; GLS
    JEL: C13 C22
    Date: 2007

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