nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒03‒14
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Testing Conditional Factor Models By Dennis Kristensen; Andrew Ang
  2. The Factor-Spline-GARCH Model for High and Low Frequency Correlations By Jose Gonzalo Rangel; Robert F. Engle
  3. A Note on the Dynamics of Persistence in US Inflation By Noriega Antonio E.; Ramos Francia Manuel
  4. Forecasting Exchange Rate Volatility: The Superior Performance of Conditional Combinations of Time Series and Option Implied Forecasts By Guillermo Benavides; Carlos Capistrán
  5. The Theta Model in the Presence of a Unit Root Some new results on “optimal” theta forecasts By Dimitrios Thomakos; Konstantinos Nikolopoulos
  6. Theta Model Forecasts for Financial Time Series: A Case Study in the S&P500 By Konstantinos Nikolopoulos; Dimitrios Thomakos; Fotios Petropoulos; Vassilis Assimakopoulos
  7. ESTAR model with multiple fixed points. Testing and Estimation By David Peel; Ivan Paya; Ioannis A. Venetis
  8. Forecasting Large Datasets with Conditionally Heteroskedastic Dynamic Common Factors By Lucia Alessi; Matteo Barigozzi; Marco Capasso
  9. Likelihood-Based Confidence Sets for the Timing of Structural Breaks By Eo, Yunjong; Morley, James C.
  10. Bayesian Analysis of DSGE Models with Regime Switching By Eo, Yunjong

  1. By: Dennis Kristensen (Columbia University and CREATES); Andrew Ang (Columbia University and NBER)
    Abstract: We develop a new methodology for estimating time-varying factor loadings and conditional alphas based on nonparametric techniques. We test whether long-run alphas, or averages of conditional alphas over the sample, are equal to zero and derive test statistics for the constancy of factor loadings. The tests can be performed for a single asset or jointly across portfolios. The traditional Gibbons, Ross and Shanken (1989) test arises as a special case when there is no time variation in the factor loadings. As applications of the methodology, we estimate conditional CAPM and Fama and French (1993) models on book-to-market and momentum decile portfolios. We reject the null that long-run alphas are equal to zero even though there is substantial variation in the conditional factor loadings of these portfolios.
    Keywords: factor models, time-varying loadings, nonparametric estimation, kernel methods, testing
    JEL: C12 C13 C14 C32 G11
    Date: 2009–03–04
  2. By: Jose Gonzalo Rangel; Robert F. Engle
    Abstract: We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns. High frequency correlations mean revert to slowly varying functions that characterize long-term correlation patterns. We associate such term behavior with low frequency economic variables, including determinants of market and idiosyncratic volatilities. Flexibility in the time varying level of mean reversion improves the empirical fit of equity correlations in the US and correlation forecasts at long horizons.
    Keywords: Yield curve, forecasting, economic activity
    JEL: C22 C32 C51 C53 G11 G12 G32
    Date: 2009–02
  3. By: Noriega Antonio E.; Ramos Francia Manuel
    Abstract: Empirical research on the degree and stability of inflation persistence in the US has produced mixed results: some suggest high and unchanged persistence during the last few decades, while others argue in favor of a decline in persistence since the early 1980s. We show that post-WWII US inflation (monthly and quarterly) became highly persistent during the´Great Inflation´ period, and then switched back to a low persistence process during 1984, and has remained stationary until the present day.
    Keywords: Inflation, Multiple change in persistence, Stationarity, Great inflation.
    JEL: C12 C22 E31 E52
    Date: 2008–08
  4. By: Guillermo Benavides; Carlos Capistrán
    Abstract: This paper provides empirical evidence that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, unconditional combinations, and hybrid forecasts. Superior forecasting performance is achieved by both, taking into account the conditional expected performance of each model given current information, and combining individual forecasts. The method used in this paper to produce conditional combinations extends the application of conditional predictive ability tests to select forecast combinations. The application is for volatility forecasts of the Mexican Peso-US Dollar exchange rate, where realized volatility calculated using intra-day data is used as a proxy for the (latent) daily volatility.
    Keywords: Composite Forecasts, Forecast Evaluation, GARCH, Implied volatility, Mexican Peso-U.S. Dollar Exchange Rate, Regime-Switching
    JEL: C22 C52 C53 G10
    Date: 2009–01
  5. By: Dimitrios Thomakos; Konstantinos Nikolopoulos
    Abstract: We significantly extend earlier work by Assimakopoulos and Nikopoloulos (2000) and Hyndman and Billah (2003) on the properties and performance of the Theta model, and potentially explain its very good performance in the M3 forecasting competition. We derive a number of new theoretical results for theta forecasts when the data generating process contains both deterministic and stochastic trends. In particular (a) we show that using the standard theta forecasts coincides with the minimum mean-squared error forecast when the innovations are uncorrelated; (b) we provide, for the first time, an optimal value for the theta parameter, which coincides with the first order autocorrelation of the innovations, and thus provide a single optimal theta line; (c) we show that the optimal linear combination of two standard theta lines coincides with the single optimal theta line of (b). Under (b) and (c) we show that the optimal theta forecast function is identical with that of an ARIMA(1,1,0) model. Furthermore, we illustrate how the Theta model can be generalized to include local behavior in two different ways.
    Keywords: forecasting, theta model, unit roots.
    Date: 2009
  6. By: Konstantinos Nikolopoulos; Dimitrios Thomakos; Fotios Petropoulos; Vassilis Assimakopoulos
    Abstract: The Theta model created a lot of interest in academic circles due to its surprisingly good performance in the M3 forecasting competition. However, this interest was not followed up by other studies, with the exception of Hyndman and Billah in 2003. In addition, the Theta model performance has not been tested on a large dataset of non-demand forecasting series, nor its properties have been examined analytically for time series that are found in finance and economics. The present study presents some empirical results on the application of the Theta model for forecasting the evolution of the S&P500 index, both as an examination of its relative performance against the standard benchmarks, and as a motivation for further theoretical work. We use weekly data over a long period of 20 years and the Theta model is used alongside the benchmark models and rolling-origin forecasts are generated. The results are interesting since they show that the Theta model has performance that is either on par or better than the benchmarks.
    Keywords: financial time series, forecasting, S&P500, Theta model.
    Date: 2009
  7. By: David Peel; Ivan Paya; Ioannis A. Venetis
    Abstract: In this paper we propose a globally stationary augmentation of the Exponential Smooth Transition Autoregressive (ESTAR) model that allows for multiple fixed points in the transition function. An F-type test statistic for the null of nonstationarity against such globally stationary nonlinear alternative is developed. The test statistic is based on the standard approximation of the nonlinear function under the null hypothesis by a Taylor series expansion. The model is applied to the U.S real interest rate data for which we find evidence of the new ESTAR process.
    Keywords: ESTAR, unit toot, real interest rates
    Date: 2009
  8. By: Lucia Alessi; Matteo Barigozzi; Marco Capasso
    Abstract: We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation results and carry out two empirical applications on financial asset returns and macroeconomic series, with a particular focus on different measures of inflation. Our proposed model outperforms the benchmarks in forecasting the conditional volatility of returns and the inflation level. Moreover, it allows to predict conditional covariances of all the time series in the panel.
    Keywords: Dynamic factors, multivariate GARCH, covolatility forecasting, inflation forecasting
    JEL: C52 C53
    Date: 2009
  9. By: Eo, Yunjong; Morley, James C.
    Abstract: In this paper, we propose a new approach to constructing confidence sets for the timing of structural breaks. This approach involves using Markov-chain Monte Carlo methods to simulate marginal “fiducial” distributions of break dates from the likelihood function. We compare our proposed approach to asymptotic and bootstrap confidence sets and find that it performs best in terms of producing short confidence sets with accurate coverage rates. Our approach also has the advantages of i) being broadly applicable to different patterns of structural breaks, ii) being computationally efficient, and iii) requiring only the ability to evaluate the likelihood function over parameter values, thus allowing for many possible distributional assumptions for the data. In our application, we investigate the nature and timing of structural breaks in postwar U.S. Real GDP. Based on marginal fiducial distributions, we find much tighter 95% confidence sets for the timing of the so-called “Great Moderation” than has been reported in previous studies.
    Keywords: Fiducial Inference; Bootstrap Methods; Structural Breaks; Confidence Intervals and Sets; Coverage Accuracy and Expected Length; Markov-chain Monte Carlo;
    JEL: C15 C22
    Date: 2008–09–05
  10. By: Eo, Yunjong
    Abstract: I estimate DSGE models with recurring regime changes in monetary policy (inflation target and reaction coefficients), technology (growth rate and volatility), and/or nominal price rigidities. In the models, agents are assumed to know deep parameter values but make probabilistic inference about prevailing and future regimes based on Bayes’ rule. I develop an estimation method that takes these probabilistic inferences into account when relating state variables to observed data. In an application to postwar U.S. data, I find stronger support for regime switching in monetary policy than in technology or nominal rigidities. In addition, a model with regime switching policy that conforms to the long-run Taylor principle given in Davig and Leeper (2007) is preferred to a determinacy-indeterminacy model motivated by Lubik and Schorfheide (2004). These empirical results indicate that, even though a passive policy regime produced more volatility in the economy from the early 1970s to the mid-1980s, the economy can be explained by determinacy over the entire postwar period, implying no role for sunspot shocks in explaining the changes in volatility.
    Keywords: New Keynesian DSGE; Markov-switching; Monetary Policy; Indeterminacy; Long-run Taylor Principle; Bayesian Analysis;
    JEL: C51 C32 E32 C52 E52 C11
    Date: 2008–08

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