nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒11‒04
nine papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Truncated priors for tempered hierarchical Dirichlet process vector autoregression By Sergei Seleznev
  2. Center-Outward R-Estimation for Semiparametric VARMA Models By Marc Hallin; Davide La Vecchia; H Liu
  3. Estimating a Large Covariance Matrix in Time-varying Factor Models By Jaeheon Jung
  4. Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area By Gergely Ganics; Florens Odendahl
  5. Bootstrap Aggregating and Random Forest By Tae-Hwy Lee; Aman Ullah; Ran Wang
  6. High-Frequency Volatility Forecasting of US Housing Markets By Mawuli Segnon; Rangan Gupta; Keagile Lesame; Mark E. Wohar
  7. Oil Shocks and Stock Market Volatility of the BRICS: A GARCH-MIDAS Approach By Afees A. Salisu; Rangan Gupta
  8. CO2 Emissions and GDP: Evidence from China By Guglielmo Maria Caporale; Gloria Claudio-Quiroga; Luis A. Gil-Alana
  9. Analyzing China's Consumer Price Index Comparatively with that of United States By Zhenzhong Wang; Yundong Tu; Song Xi Chen

  1. By: Sergei Seleznev (Bank of Russia, Russian Federation)
    Abstract: We construct priors for the tempered hierarchical Dirichlet process vector autoregression model (tHDP-VAR) that in practice do not lead to explosive forecasting dynamics. Additionally, we show that tHDP-VAR and its variational Bayesian approximation with heuristics demonstrate competitive or even better forecasting performance on US and Russian datasets.
    Keywords: Bayesian nonparametrics, forecasting, hierarchical Dirichlet process, infinite hidden Markov model.
    JEL: C11 C32 C53 E37
    Date: 2019–10
  2. By: Marc Hallin; Davide La Vecchia; H Liu
    Abstract: We propose a new class of estimators for semiparametric VARMA models with the innovation density playing the role of nuisance parameter. Our estimators are R-estimators based on the multivariate concepts of center-outward ranks and signs recently proposed by Hallin~(2017). We show how these concepts, combined with Le Cam's asymptotic theory of statistical experiments, yield a robust yet flexible and powerful class of estimation procedures for multivariate time series. We develop the relevant asymptotic theory of our R-estimators, establishing their root-n consistency and asymptotic normality under a broad class of innovation densities including, e.g. multimodal mixtures of Gaussians or and multivariate skew-t distributions. An implementation algorithm is provided in the supplementary material, available online. A Monte Carlo study compares our R-estimators with the routinely-applied Gaussian quasi-likelihood ones; the latter appear to be quite significantly outperformed away from elliptical innovations. Numerical results also provide evidence of considerable robustness gains. Two real data examples conclude the paper.
    Keywords: Multivariate ranks, Distribution-freeness, Local asymptotic normality, Measure transportation, Quasi likelihood estimation, Skew innovation density
    Date: 2019–10
  3. By: Jaeheon Jung
    Abstract: This paper deals with the time-varying high dimensional covariance matrix estimation. We propose two covariance matrix estimators corresponding with a time-varying approximate factor model and a time-varying approximate characteristic-based factor model, respectively. The models allow the factor loadings, factor covariance matrix, and error covariance matrix to change smoothly over time. We study the rate of convergence of each estimator. Our simulation and empirical study indicate that time-varying covariance matrix estimators generally perform better than time-invariant covariance matrix estimators. Also, if characteristics are available that genuinely explain true loadings, the characteristics can be used to estimate loadings more precisely in finite samples; their helpfulness increases when loadings rapidly change.
    Date: 2019–10
  4. By: Gergely Ganics; Florens Odendahl
    Abstract: We incorporate external information extracted from the European Central Bank's Survey of Professional Forecasters into the predictions of a Bayesian VAR, using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve the plain BVAR point and density forecasts. Importantly, we do not restrict the forecasts at a specific quarterly horizon, but their possible paths over several horizons jointly, as the survey information comes in the form of one- and two-year-ahead expectations. Besides improving the accuracy of the variable that we target, the spillover effects to ``other-than-targeted'' variables are relevant in size and statistically significant. We document that the baseline BVAR exhibits an upward bias for GDP growth after the financial crisis and our results provide evidence that survey forecasts can help mitigate the effects of structural breaks on the forecasting performance of a popular macroeconometric model.
    Keywords: : Survey of Professional Forecasters, Density forecasts, Entropic tilting, Soft conditioning.
    JEL: C53 C32 E37
    Date: 2019
  5. By: Tae-Hwy Lee (Department of Economics, University of California Riverside); Aman Ullah (University of California, Riverside); Ran Wang (University of California, Riverside)
    Abstract: Bootstrap Aggregating (Bagging) is an ensemble technique for improving the robustness of forecasts. Random Forest is a successful method based on Bagging and Decision Trees. In this chapter, we explore Bagging, Random Forest, and their variants in various aspects of theory and practice. We also discuss applications based on these methods in economic forecasting and inference.
    Keywords: bagging, decision trees, random forests, forecasting
    JEL: C2 C3 C4 C5
    Date: 2019–07
  6. By: Mawuli Segnon (Department of Economics, Institute for Econometric and Economic Statistics, University of Münster, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Keagile Lesame (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Mark E. Wohar (Department of Economics, University of NE-Omaha, USA and School of Business and Economics, Loughborough University, UK)
    Abstract: We propose a logistic smooth transition autoregressive fractionally integrated [STARFI(p,d)] process for modeling and forecasting US housing price volatility. We discuss the statistical properties of the model and investigate its forecasting performance by assuming various specifications for the dynamics underlying the variance process in the model. Using a unique database of daily data on price indices from ten major US cities, and the corresponding daily Composite 10 Housing Price Index, and also a housing futures price index, we find that using the Markov-switching multifractal (MSM) and FIGARCH frameworks for modeling the variance process helps improving the gains in forecast accuracy.
    Keywords: US housing prices, GARCH processes, MSM processes, Model confidence set
    JEL: C22 C53 C58
    Date: 2019–10
  7. By: Afees A. Salisu (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam and Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Vietnam); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)
    Abstract: In this study, we employ the GARCH-MIDAS model to investigate the response of stock market volatility of the BRICS to oil shocks. We utilize the recent datasets of Baumeister & Hamilton (2019) where oil shocks are decomposed into four variants - oil supply shocks, economic activity shocks, oil consumption shocks, and oil inventory shocks. We further decomposed each of these shocks into positive and negative shocks, and our findings show heterogeneous response of stock market volatility of the BRICS countries to the alternative oil shocks including the positive and negative shocks. The differing responses across the BRICS countries could be attributed to the difference in the economic size, oil production and consumption profile, market share distribution across firms, as well as financial system and regulation efficiency.
    Keywords: Oil shocks, Stock market volatility, BRICS, GARCH-MIDAS
    JEL: C32 G12 G15 Q02
    Date: 2019–10
  8. By: Guglielmo Maria Caporale; Gloria Claudio-Quiroga; Luis A. Gil-Alana
    Abstract: This paper examines the relationship between the logarithms of CO2 emissions and real GDP in China by applying fractional integration and cointegration methods. The univariate results indicate that the two series are highly persistent, their orders of integration being around 2, whilst the cointegration tests (using both standard and fractional techniques) imply that there exists a long-run equilibrium relationship between the two variables in first differences, i.e. their growth rates are linked together in the long run. This suggests the need for environmental policies aimed at reducing emissions during periods of economic growth.
    Keywords: CO2 emissions, GDP, China, persistence, fractional integration, fractional cointegration
    JEL: C22 C32 Q56
    Date: 2019
  9. By: Zhenzhong Wang; Yundong Tu; Song Xi Chen
    Abstract: This paper provides a thorough analysis on the dynamic structures and predictability of China's Consumer Price Index (CPI-CN), with a comparison to those of the United States. Despite the differences in the two leading economies, both series can be well modeled by a class of Seasonal Autoregressive Integrated Moving Average Model with Covariates (S-ARIMAX). The CPI-CN series possess regular patterns of dynamics with stable annual cycles and strong Spring Festival effects, with fitting and forecasting errors largely comparable to their US counterparts. Finally, for the CPI-CN, the diffusion index (DI) approach offers improved predictions than the S-ARIMAX models.
    Date: 2019–10

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