nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒05‒08
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Density forecasting comparison of volatility models By Leopoldo Catania; Nima Nonejad
  2. Unit Root Testing in ARMA Models: A Likelihood Ratio Approach By Hernández Juan R.
  3. A Time Series Model of Interest Rates With the Effective Lower Bound By Johannsen, Benjamin K.; Mertens, Elmar
  4. Robustness of Forecast Combination in Unstable Environment: A Monte Carlo Study of Advanced Algorithms By Yongchen Zhao
  5. Dynamic Factor model with infinite dimensional factor space: forecasting By Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi
  6. Decomposing Duration Dependence in a Stopping Time Model By Fernando E. Alvarez; Katarína Borovičková; Robert Shimer
  7. Tractable Likelihood-Based Estimation of Non-Linear DSGE Models Using Higher-Order Approximations By Kollmann, Robert
  8. Do data revisions matter for DSGE estimation? By Givens, Gregory
  9. Inference for Impulse Response Coefficients From Multivariate Fractionally Integrated Processes By Richard T. Baillie; George Kapetanios; Fotis Papailias
  10. Is Robust Inference with OLS Sensible in Time Series Regressions? Investigating Bias and MSE Trade-offs with Feasible GLS and VAR Approaches By Richard T. Baillie; Kun Ho Kim
  11. On the use of high frequency measures of volatility in MIDAS regressions By Elena Andreou

  1. By: Leopoldo Catania; Nima Nonejad
    Abstract: We compare the predictive ability of several volatility models for a long series of weekly log-returns of the Dow Jones Industrial Average Index from 1902 to 2016. Our focus is particularly on predicting one and multi-step ahead conditional and aggregated conditional densities. Our set of competing models includes: Well-known GARCH specifications, Markov switching GARCH, sempiparametric GARCH, Generalised Autoregressive Score (GAS), the plain stochastic volatility (SV) as well as its more flexible extensions such as SV with leverage, in-mean effects and Student-t distributed errors. We find that: (i) SV models generally outperform the GARCH specifications, (ii): The SV model with leverage effect provides very strong out-of-sample performance in terms of one and multi-steps ahead density prediction, (iii) Differences in terms of Value-at-Risk (VaR) predictions accuracy are less evident. Thus, our results have an important implication: the best performing model depends on the evaluation criterion
    Date: 2016–05
  2. By: Hernández Juan R.
    Abstract: In this paper I propose a Likelihood Ratio test for a unit root (LR) with a local-to-unity Autoregressive parameter embedded in ARMA(1,1) models. By dealing explicitly with dependence in a time series through the Moving Average, as opposed to the long Autorregresive lag approximation, the test shows gains in power and has good small-sample properties. The asymptotic distribution of the test is shown to be independent of the short-run parameters. The Monte Carlo experiments show that the LR test has higher power than the Augmented Dickey Fuller test for several sample sizes and true values of the Moving Average parameter. The exception is the case when this parameter is very close to -1 with a considerably small sample size.
    Keywords: Likelihood ratio test; ARMA model; Unit root test.
    JEL: C22
    Date: 2016–04
  3. By: Johannsen, Benjamin K.; Mertens, Elmar
    Abstract: Modeling interest rates over samples that include the Great Recession requires taking stock of the effective lower bound (ELB) on nominal interest rates. We propose a flexible time– series approach which includes a “shadow rate”—a notional rate that is less than the ELB during the period in which the bound is binding—without imposing no–arbitrage assumptions. The approach allows us to estimate the behavior of trend real rates as well as expected future interest rates in recent years.
    Keywords: Bayesian Econometrics ; Effective Lower Bound ; Shadow Rate ; State-Space Model ; Term Structure of Interest Rates
    JEL: C32 C34 C53 E43 E47
    Date: 2016–04–04
  4. By: Yongchen Zhao (Towson University)
    Abstract: Based on a set of carefully designed Monte Carlo exercises, this paper document the behavior and performance of several newly developed advanced forecast combination algorithms in unstable environments, where performance of candidate forecasts are cross-sectionally heterogeneous and dynamically evolving over time. Results from these exercises provide guidelines regarding the selection of forecast combination method based on the nature, frequency, and magnitude of instabilities in forecasts as well as the target variable. Following these guidelines, a simple forecast combination exercise using the U.S. Survey of Professional Forecasters, where combined forecasters are shown to have superior performance that is not only statistically significant but also of practical importance.
    Keywords: Forecast combination; exponential re-weighting; shrinkage; estimation error; performance stability; real-time data
    JEL: C53 C22 C15
    Date: 2015–12
  5. By: Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi
    Abstract: The paper compares the pseudo real-time forecasting performance of three Dynamic Factor Models: (i) The standard principal-component model, Stock and Watson (2002a), (ii) The model based on generalized principal components, Forni et al. (2005), (iii) The model recently proposed in Forni et al. (2015) and Forni et al. (2016). We employ a large monthly dataset of macroeconomic and financial time series for the US economy, which includes the Great Moderation, the Great Recession and the subsequent recovery. Using a rolling window for estimation and prediction, we find that (iii) neatly outperforms (i) and (ii) in the Great Moderation period for both Industrial Production and Inflation, and for Inflation over the full sample. However, (iii) is outperformed by (i) and (ii) over the full sample for Industrial Production.
    Date: 2016–04
  6. By: Fernando E. Alvarez; Katarína Borovičková; Robert Shimer
    Abstract: We develop a dynamic model of transitions in and out of employment. A worker finds a job at an optimal stopping time, when a Brownian motion with drift hits a barrier. This implies that the duration of each worker's jobless spells has an inverse Gaussian distribution. We allow for arbitrary heterogeneity across workers in the parameters of this distribution and prove that the distribution of these parameters is identified from the duration of two spells. We use social security data for Austrian workers to estimate the model. We conclude that dynamic selection is a critical source of duration dependence.
    JEL: E24 J64
    Date: 2016–04
  7. By: Kollmann, Robert
    Abstract: This paper discusses a tractable approach for computing the likelihood function of non-linear Dynamic Stochastic General Equilibrium (DSGE) models that are solved using second- and third order accurate approximations. By contrast to particle filters, no stochastic simulations are needed for the method here. The method here is, hence, much faster and it is thus suitable for the estimation of medium-scale models. The method assumes that the number of exogenous innovations equals the number of observables. Given an assumed vector of initial states, the exogenous innovations can thus recursively be inferred from the observables. This easily allows to compute the likelihood function. Initial states and model parameters are estimated by maximizing the likelihood function. Numerical examples suggest that the method provides reliable estimates of model parameters and of latent state variables, even for highly non-linear economies with big shocks.
    Keywords: Likelihood-based estimation of non-linear DSGE models, higher-order approximations, pruning, latent state variables
    JEL: C6 E3
    Date: 2016
  8. By: Givens, Gregory
    Abstract: This paper checks whether the coefficient estimates of a famous DSGE model are robust to macroeconomic data revisions. The effects of revisions are captured by rerunning the estimation on a real-time data set compiled using the latest time series available each quarter from 1997 through 2015. Results show that point estimates of the structural parameters are generally robust to changes in the data that have occurred over the past twenty years. By comparison, estimates of the standard errors are relatively more sensitive to revisions. The latter implies that judgements about the statistical significance of certain parameters depend on which data vintage is used for estimation.
    Keywords: Data Revisions, Real-Time Data, DSGE Estimation
    JEL: C32 C82 E32 E52
    Date: 2016–04–22
  9. By: Richard T. Baillie (Department of Economics, Michigan State University, USA; School of Economics and Finance, Queen Mary University of London, UK; The Rimini Centre for Economic Analysis, Italy); George Kapetanios (School of Economics and Finance, Queen Mary University of London, UK); Fotis Papailias (Queen's University Management School, Queen's University Belfast, UK; quantf research,
    Abstract: This paper considers a multivariate system of fractionally integrated time series and investigates the most appropriate way for estimating Impulse Response (IR) coefficients and their associated confidence intervals. The paper extends the univariate analysis recently provided by Baillie and Kapetanios (2013), and uses a semi parametric, time domain estimator, based on a vector autoregression (VAR) approximation. Results are also derived for the orthogonalized estimated IRs which are generally more practically relevant. Simulation evidence strongly indicates the desirability of applying the Kilian small sample bias correction, which is found to improve the coverage accuracy of confidence intervals for IRs. The most appropriate order of the VAR turns out to be relevant for the lag length of the IR being estimated.
    Date: 2015–12
  10. By: Richard T. Baillie (Department of Economics, Michigan State University, USA; School of Economics and Finance, Queen Mary University of London, UK; The Rimini Centre for Economic Analysis, Italy); Kun Ho Kim (Department of Economics, Hanyang University, Republic of Korea)
    Abstract: It has become commonplace in applied time series econometric work to estimate regressions with consistent, but asymptotically inefficient OLS and to base inference of conditional mean parameters on robust standard errors. This approach seems mainly to have occurred due to concern at the possible violation of strict exogeneity conditions from applying GLS. We first show that even in the case of the violation of contemporaneous exogeneity, that the asymptotic bias associated with GLS will generally be less than that of OLS. This result extends to Feasible GLS where the error process is approximated by a sieve autoregression. The paper also examines the trade-offs between asymptotic bias and efficiency related to OLS, feasible GLS and inference based on full system VAR. We also provide simulation evidence and several examples including tests of efficient markets, orange juice futures and weather and a control engineering application of furnace data. The evidence and general conclusion is that the widespread use of OLS with robust standard errors is generally not a good research strategy. Conversely, there is much to recommend FGLS and VAR system based estimation.
    Date: 2016–03
  11. By: Elena Andreou
    Abstract: Many empirical studies link mixed data frequency variables such as low frequency macroeconomic or financial variables with high frequency financial indicators’ volatilities, especially within a predictive regression model context. The objective of this paper is threefold: First, we relate the standard Least Squares (LS) regression model with high frequency volatility predictors, with the corresponding Mixed Data Sampling Nonlinear LS (MIDAS-NLS) regression model (Ghysels et al., 2005, 2006), and evaluate the properties of the regression estimators of these models. We also consider alternative high frequency volatility measures as well as various continuous time models using their corresponding relevant higher-order moments to further analyze the properties of these estimators. Second, we derive the relative MSE efficiency of the slope estimator in the standard LS and MIDAS regressions, we provide conditions for relative efficiency and present the numerical results for different continuous time models. Third, we extend the analysis of the bias of the slope estimator in standard LS regressions with alternative realized measures of risk such as the Realized Covariance, Realized Beta and the Realized Skewness when the true DGP is a MIDAS model.
    Keywords: MIDAS regression model, high-frequency volatility estimators, bias, efficiency.
    JEL: C22 C53 G22
    Date: 2016–04

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