nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒12‒18
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Forecasting the equity risk premium with frequency-decomposed predictors By Gonçalo Faria; Fabio Verona
  2. Adaptive models and heavy tails with an application to inflation forecasting By Davide Delle Monache; Ivan Petrella
  3. Estimation of the global regularity of a multifractional Brownian motion By Joachim Lebovits; Mark Podolskij
  4. IV and GMM Estimation and Testing of Multivariate Stochastic Unit Root Models By Offer Lieberman; Peter C.B. Phillips
  5. Variance targeting estimation of the BEKK-X model By Thieu, Le Quyen
  6. Equation by equation estimation of the semi-diagonal BEKK model with covariates By Thieu, Le Quyen
  7. When is Nonfundamentalness in SVARs A Real Problem? By Beaudry, Paul; Fève, Patrick; Guay, Alain; Portier, Franck
  8. Testing for Symmetry in Weakly Dependent Time Series By Luke Hartigan

  1. By: Gonçalo Faria (Católica Porto Business School and CEGE, Universidade Católica Portuguesa); Fabio Verona (Bank of Finland and CEF.UP)
    Abstract: We show that the out-of-sample forecast of the equity risk premium can be significantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fifteen predictors from the existing literature, for the out-of-sample forecasting period from January 1990 to December 2014. The best result achieved for individual predictors is a monthly out-of-sample R2 of 2.98 % and utility gains of 549 basis points per year for a mean-variance investor. This performance is improved even further when the individual forecasts from the frequency- decomposed predictors are combined. These results are robust for different subsamples, including the Great Moderation period, the Great Financial Crisis period and, more generically, periods of bad, normal and good economic growth. The strong and robust performance of this method comes from its ability to disentangle the information aggregated in the original time series of each variable, which allows to isolate the frequencies of the predictors with the highest predictive power from the noisy parts.
    Keywords: predictability, equity risk premium, frequency domain, discrete wavelets
    JEL: C58 G11 G12 G17
    Date: 2016–12
  2. By: Davide Delle Monache (Bank of Italy); Ivan Petrella (WBS; CEPR)
    Abstract: This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, the resulting model is observation-driven and is estimated by classical methods. In particular, we consider time variation in both coefficients and volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters so as to attain local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics with the following results: allowing for heavy tails leads to significant improvements in terms of fit and forecast, and the adoption of the Student-t distribution proves to be crucial in order to obtain well calibrated density forecasts. These results are obtained using the US CPI inflation rate and are confirmed by other inflation indicators, as well as for CPI inflation of the other G7 countries.
    Keywords: adaptive algorithms, inflation, score-driven models, student-t, time-varying parameters.
    JEL: C22 C51 C53 E31
    Date: 2016–11
  3. By: Joachim Lebovits (University Paris 13); Mark Podolskij (Aarhus University and CREATES)
    Abstract: This paper presents a new estimator of the global regularity index of a multifractional Brownian motion. Our estimation method is based upon a ratio statistic, which compares the realized global quadratic variation of a multifractional Brownian motion at two different frequencies. We show that a logarithmic transformation of this statistic converges in probability to the minimum of the Hurst functional parameter, which is, under weak assumptions, identical to the global regularity index of the path.
    Keywords: consistency, Hurst parameter, multifractional Brownian motion, power variation
    JEL: C10 C13 C14
    Date: 2016–12–06
  4. By: Offer Lieberman (Bar-Ilan University); Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: Lieberman and Phillips (2016; Journal of Econometrics; LP) introduced a multivariate stochastic unit root (STUR) model, which allows for random, time varying local departures from a unit root (UR) model, where nonlinear least squares (NLLS) may be used for estimation and inference on the STUR coefficient. In a structural version of this model where the driver variables of the STUR coefficient are endogenous, the NLLS estimate of the STUR parameter is inconsistent, as are the corresponding estimates of the associated covariance parameters. This paper develops a nonlinear instrumental variable (NLIV) as well as GMM estimators of the STUR parameter which conveniently addresses endogeneity. We derive the asymptotic distributions of the NLIV and GMM estimators and establish consistency under similar orthogonality and relevance conditions to those used in the linear model. An overidentification test and its asymptotic distribution are also developed. The results enable inference about structural STUR models and a mechanism for testing the local STUR model against a simple UR null, which complements usual UR tests. Simulations reveal that the asymptotic distributions of the the NLIV and GMM estimators of the STUR parameter as well as the test for overidentifying restrictions perform well in small samples and that the distribution of the NLIV estimator is heavily leptokurtic with a limit theory which has Cauchy-like tails. Comparisons of STUR coefficient and a standard UR coefficient test show that the one-sided UR test performs poorly against the one-sided STUR coefficient test both as the sample size and departures from the null rise.
    Keywords: Autoregression, Diffusion; Similarity, Stochastic unit root, Time-varying coefficients
    JEL: C22
    Date: 2016–06
  5. By: Thieu, Le Quyen
    Abstract: This paper studies the BEKK model with exogenous variables (BEKK-X), which intends to take into account the influence of explanatory variables on the conditional covariance of the asset returns. Strong consistency and asymptotic normality of a variance targeting estimator (VTE) is proved. Monte Carlo experiments and an application to financial series illustrate the asymptotic results.
    Keywords: BEKK model augmented with exogenous variables, BEKK-X model, Variance targeting estimation (VTE)
    JEL: C13
    Date: 2016–08–01
  6. By: Thieu, Le Quyen
    Abstract: This paper provide the asymptotic normality of the Equation by Equation estimator for the semi-diagonal BEKK models augmented by the exogenous variables. The results are obtained without assuming that the innovations are independent, which allows investigate different additional explanatory variables into the information set.
    Keywords: BEKK-X, Equation by equation estimation, exogenous variables, covariates, semi-diagonal BEKK-X
    JEL: C10
    Date: 2016–09–01
  7. By: Beaudry, Paul; Fève, Patrick; Guay, Alain; Portier, Franck
    Abstract: Identification of structural shocks can be subject to nonfundamentalness, as the econometrician may have an information set smaller than the economic agents´i one. How serious is that problem from a quantitative point of view? In this work we propose a simple diagnosis statistics for the quantitative importance of nonfundamentalness in structural VARs. The diagnosis is of interest as nonfundamentalness is not an either/or question, but is a quantitative issue which can be more or less severe. Using our preferred strategy for identifying news shocks, we find that nonfundamentalness is quantitatively unimportant and that news shocks continue to generate significant business cycle type fluctuations when adjust the estimating procedure to take into account the potential nonfundamentalness issue.
    Keywords: Non-Fundamentalness, Business Cycles, SVARs, News.
    JEL: C32 E32
    Date: 2016–11
  8. By: Luke Hartigan (School of Economics, UNSW Business School, UNSW)
    Abstract: I propose a test of symmetry for a stationary time series based on the difference between the dispersion above the central tendency of the series with that below it. The test has many attractive features: it is applicable to dependent processes, it has a familiar form, it can be implemented using regression, and it has a standard Gaussian limiting distribution under the null of symmetry. The finite sample properties of the test are examined via Monte Carlo simulation and suggest that it is more powerful than competing tests in the literature for the DGPs considered. I apply the test to investigate business cycle asymmetry in sectoral data and confirm previous findings that asymmetry is more often detected in goods-producing sectors than service-related sectors.
    Keywords: Symmetry; Weak dependence; Hypothesis testing; Monte Carlo simulation; Business cycle asymmetry
    JEL: C12 C15 C22 C52 E32
    Date: 2016–11

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