nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒09‒24
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Permanent and transitory components of GDP and stock prices: further analysis By Jesus Gonzalo; Tae-Hwy Lee; Weiping Yang
  2. Wald Tests of I(1) against I(d) alternatives : some new properties and an extension to processes with trending components By Juan Jose Dolado; Jesús Gonzalo; Laura Mayoral
  3. Forecasting with small macroeconomic VARs in the presence of instabilities By Todd E. Clark; Michael W. McCracken
  4. Averaging forecasts from VARs with uncertain instabilities By Todd E. Clark; Michael W. McCracken
  5. Nonlinear stock prices adjustment in the G7 countries By Georges Prat; Fredj Jawadi
  6. Aggregation of regional economic time series with different spatial correlation structures By Giuseppe Arbia; Marco Bee; Giuseppe Espa
  7. Volatility Proxies for Discrete Time Models By de Vilder, Robin G.; Visser, Marcel P.

  1. By: Jesus Gonzalo; Tae-Hwy Lee; Weiping Yang
    Abstract: Using the conventional VAR identification approach, Cochrane (1994) finds that substantial amounts of variation in GDP growth and stock returns are due to transitory shocks. Following the common trend decomposition of King, et al. (1991), we show that Cochrane's results depend on the assumption of weak exogeneity of one of the variables with respect to the cointegration vector. When this assumption holds both approaches coincide. If not, the shocks Cochrane called transitory are not totally transitory. In this case, the conventional VAR approach with the assumption of the weak exogeneity may overstate the magnitude of transitory shocks and understate that of permanent shocks. We find that the permanent components of GDP and stock prices are much larger than those estimates of Cochrane, although substantial (but much smaller than in Cochrane (1994)) variations in GDP growth and stock returns are attributed to transitory shocks.
    Date: 2007–05
  2. By: Juan Jose Dolado; Jesús Gonzalo; Laura Mayoral
    Abstract: This paper analyses the power properties, under fixed alternatives, of a Wald-type test, i.e., the (Efficient) Fractional Dickey-Fuller (EFDF) test of I(1) against I(d), d<1, relative to LM tests. Further, it extends the implementation of the EFDF test to the presence of deterministic trending components in the DGP. Tests of these hypotheses are important in many macroeconomic applications where it is crucial to distinguish between permanent and transitory shocks because shocks die out in I(d) processes with d<1. We show how simple is the implementation of the EFDF in these situations and argue that, under fixed alternatives, it has better power properties than LM tests. Finally, an empirical application is provided where the EFDF approach allowing for deterministic components is used to test for long-memory in the GDP p.c. of several OECD countries, an issue that has important consequences to discriminate between alternative growth theories.
    Date: 2007–06
  3. By: Todd E. Clark; Michael W. McCracken
    Abstract: Small-scale VARs are widely used in macroeconomics for forecasting U.S. output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters and the Federal Reserve Board's Greenbook as benchmarks.
    Date: 2007
  4. By: Todd E. Clark; Michael W. McCracken
    Abstract: A body of recent work suggests commonly-used VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, different observation windows for estimation, (over-) differencing, intercept correction, stochastically time-varying parameters, break dating, discounted least squares, Bayesian shrinkage, and detrending of inflation and interest rates. Although each individual method could be useful, the uncertainty inherent in any single representation of instability could mean that combining forecasts from the entire range of VAR estimates will further improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combination in improving VAR forecasts made with real-time data. The combinations include simple averages, medians, trimmed means, and a number of weighted combinations, based on: Bates-Granger regressions, factor model estimates, regressions involving forecast quartiles, Bayesian model averaging, and predictive least squares-based weighting. Our goal is to identify those approaches that, in real time, yield the most accurate forecasts of these variables. We use forecasts from simple univariate time series models as benchmarks.
    Date: 2007
  5. By: Georges Prat (EconomiX - [CNRS : UMR7166] - [Université de Paris X - Nanterre]); Fredj Jawadi
    Abstract: This paper aims to modeling stock prices adjustment dynamics toward their fundamentals. We used the class of Switching Transition Error Correction Models (STECM) and we showed that stock prices deviations toward fundamentals could be characterized by nonlinear adjustment process with mean reversion. First, according to Anderson (1997), De Grauwe and Grimaldi (2005) and Boswijk et al.(2006), we justify these nonlinearities by the presence of heterogeneous transaction costs, behavioural heterogeneity and the interaction between shareholders expectations. After, we present STECM specification. We apply this model to describe the G7 indexes adjustment dynamics toward their fundamentals. We showed that the G7 stock indexes adjustment is smooth and nonlinearly mean-reverting and that the convergence speeds vary according to the disequilibrium extent. Finally, using two indicators proposed by Peel and Taylor (2000), we determine phases of under- and overvaluation of stock prices and measure intensity of stock prices adjustment strengths.
    Keywords: Stock Prices, Heterogeneous Transaction Costs, Nonlinear Adjustment
    Date: 2007–09–18
  6. By: Giuseppe Arbia; Marco Bee; Giuseppe Espa
    Abstract: In this paper we compare the relative efficiency of different forecasting methods of space-time series when variables are spatially and temporally correlated. We consider the case of a space-time series aggregated into a single time series and the more general instance of a space-time series aggregated into a coarser spatial partition. We extend in various directions the outcomes found in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. The outcomes obtained provide operational suggestions on how to choose between alternative forecasting methods in empirical circumstances.
    Keywords: Spatial correlation, Aggregation, Forecast efficiency, Space–time models, Edge effects, Negative spatial correlation.
    JEL: C15 C21 C43 C53
    Date: 2007
  7. By: de Vilder, Robin G.; Visser, Marcel P.
    Abstract: Discrete time volatility models typically employ a latent scale factor to represent volatility. High frequency data may be used to construct proxies for these scale factors. Examples are the intraday high-low range and the realized volatility. This paper develops a method for ranking and optimizing volatility proxies. It is possible to outperform the quadratic variation as a proxy for the discrete time scale factor. For the S&P 500 index data over the years 1988-2006 this is achieved by a proxy which puts, among other things, more weight on the highs than on the lows over intraday intervals.
    Keywords: volatility proxy; realized volatility; quadratic variation; scale factor; arch/garch/stochastic volatility; intraday seasonality
    JEL: C65 C52 C22
    Date: 2007–09–14

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