nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒06‒24
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Forecasting Commodity Prices: GARCH, Jumps, and Mean Reversion By Jean-Thomas Bernard; Lynda Khalaf; Maral Kichian; Sebastien McMahon
  2. Forecasting Canadian Time Series with the New Keynesian Model By Ali Dib; Mohamed Gammoudi; Kevin Moran
  3. Reexamining the linkages between inflation and output growth: A bivariate ARFIMA-FIGARCH approach By Mustafa Caglayan and Feng Jiang
  4. Problems in Applying Dynamic Panel Data Models: Theoretical and Empirical Findings By Felicitas Nowak-Lehmann D.; Dierk Herzer; Sebastian Vollmer; Inmaculada Martínez-Zarzoso
  5. Identification of Technology Shocks in Structural VARs By FÈVE, Patrick; GUAY, Alain
  6. Time Series Analysis By Francis X. Diebold; Lutz Kilian; Marc Nerlove
  7. A New Approach Based on Cumulants for Estimating Financial Regression Models with Errors in the Variables: the Fama and French Model Revisited By Alain Coen; Francois-Éric Racicot; Raymond Théoret

  1. By: Jean-Thomas Bernard; Lynda Khalaf; Maral Kichian; Sebastien McMahon
    Abstract: Fluctuations in the prices of various natural resource products are of concern in both policy and business circles; hence, it is important to develop accurate price forecasts. Structural models provide valuable insights into the causes of price movements, but they are not necessarily the best suited for forecasting given the multiplicity of known and unknown factors that affect supply and demand conditions in these markets. Parsimonious representations of price processes often prove more useful for forecasting purposes. Central questions in such stochastic models often revolve around the time-varying trend, the stochastic convenience yield and volatility, and mean reversion. The authors seek to assess and compare alternative approaches to modelling these effects, focusing on forecast performance. Three econometric specifications are considered that cover the most up-to-date models in the recent literature on commodity prices: (i) random-walk models with autoregressive conditional heteroscedasticity (ARCH) or generalized ARCH (GARCH) effects, and with normal or student-t innovations, (ii) Poisson-based jump-diffusion models with ARCH or GARCH effects, and with normal or student-t innovations, and (iii) meanreverting models that allow for uncertainty in equilibrium price.
    Keywords: Econometric and statistical methods
    JEL: C52 C53 E37
    Date: 2006
  2. By: Ali Dib; Mohamed Gammoudi; Kevin Moran
    Abstract: The authors document the out-of-sample forecasting accuracy of the New Keynesian model for Canada. They estimate their variant of the model on a series of rolling subsamples, computing out-of-sample forecasts one to eight quarters ahead at each step. They compare these forecasts with those arising from simple vector autoregression (VAR) models, using econometric tests of forecasting accuracy. Their results show that the forecasting accuracy of the New Keynesian model compares favourably with that of the benchmarks, particularly as the forecasting horizon increases. These results suggest that the model could become a useful forecasting tool for Canadian time series. The authors invoke the principle of parsimony to explain their findings.
    Keywords: Business fluctuations and cycles; Economic models; Econometric and statistical methods
    JEL: E32 E37 C12
    Date: 2006
  3. By: Mustafa Caglayan and Feng Jiang
    Abstract: In this paper, given recent theoretical developments that inflation can exhibit long memory properties due to the output growth process, we propose a new class of bivariate processes to simultaneously investigate the dual long memory properties in the mean and the conditional variance of inflation and output growth series. We estimate the model using monthly UK data and document the presence of dual long memory properties in both series. Then, using the conditional variances generated from our bivariate model, we employ Granger causality tests to scrutinize the linkages between the means and the volatilities of inflation and output growth.
    JEL: C32 E31
  4. By: Felicitas Nowak-Lehmann D. (Universität Göttingen, Ibero-Amerika Institut); Dierk Herzer (Universität Frankfurt / Universität Göttingen); Sebastian Vollmer (Universität Göttingen, Ibero-Amerika Institut); Inmaculada Martínez-Zarzoso (Universität Göttingen, Ibero-Amerika Institut)
    Abstract: The objective of this paper is twofold: First, the applicability of a widely used dynamic model, the autoregressive distributed lag model (ARDL), is scrutinized in a panel data setting. Second, Chile’s development of market shares in the EU market in the period of 1988 to 2002 is then analyzed in this dynamic framework, testing for the impact of price competitiveness on market shares and searching for estimation methods that allow dealing with the problem of inter-temporal and cross-section correlation of the disturbances. To estimate the coefficients of the ARDL model, FGLS is utilized within the Three Stage Feasible Generalized Least Squares (3SFGLS) and the system Generalized Method of Moments (system GMM) methods. A computation of errors is added to highlight the susceptibility of the model to problems related to underlying model assumptions.
    Keywords: Dynamic panel data model, autoregressive distributed lag model; pooled 3Stage Feasible
    JEL: F14 F17 C23
    Date: 2006–06–06
  5. By: FÈVE, Patrick; GUAY, Alain
    JEL: C32 E32
    Date: 2006–02
  6. By: Francis X. Diebold (Department of Economics, University of Pennsylvania); Lutz Kilian (Department of Economics, University of Michigan); Marc Nerlove (Department of Agricultural and Resource Economics, University of Maryland)
    Abstract: We provide a concise overview of time series analysis in the time and frequency domains, with lots of references for further reading.
    Keywords: time series analysis, time domain, frequency domain
    JEL: C22
    Date: 2006–05–01
  7. By: Alain Coen (Département de stratégie des affaires, Université du Québec (Montréal)); Francois-Éric Racicot (Département des sciences administratives, Université du Québec (Outaouais) et LRSP); Raymond Théoret (Département de stratégie des affaires, Université du Québec (Montréal))
    Abstract: This paper proposes to revisit both the CAPM and the three-factor model of Fama and French (1993) in presence of errors in the variables. To reduce the bias induced by measurement and specification errors, we transpose to the cost of equity an estimator based on cumulants of order three and four initially developed by Dagenais and Dagenais (1997) and lated generalized to financial models by Racicot (2003). Our results show that our technique has great and significant consequences on the measure of the cost of equity. We obtain ipso facto a new estimator of the Jensen alpha.
    Keywords: Errors in the variables, cumulants, higher moments, instrumental variables, cost of equity, Jensen alpha.
    JEL: C13 C49 G12 G31
    Date: 2006–05–01

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