nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒10‒15
twelve papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Large mixed-frequency VARs with a parsimonious time-varying parameter structure By Götz, Thomas B.; Hauzenberger, Klemens
  2. Parameter Estimation of Heavy-Tailed AR Model with Missing Data via Stochastic EM By Junyan Liu; Sandeep Kumar; Daniel P. Palomar
  3. Prediction Regions for Interval-valued Time Series By Gloria Gonzalez-Rivera; Yun Luo; Esther Ruiz
  4. Multivariate Stochastic Volatility with Co-Heteroscedasticity By Joshua Chan; Arnaud Doucet; Roberto Leon-Gonzalez; Rodney W. Strachan
  5. Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models By Licht, Adrian; Escribano Sáez, Álvaro; Blazsek, Szabolcs Istvan
  6. Efficient generation of time series with diverse and controllable characteristics By Yanfei Kang; Rob J Hyndman; Feng Li
  7. Ergodicity conditions for a double mixed Poisson autoregression By Aknouche, Abdelhakim; Demouche, Nacer
  8. Bootstrap Assisted Tests of Symmetry for Dependent Data By Zacharias Psaradakis; Marian Vavra
  9. On normalization and algorithm selection for unsupervised outlier detection By Sevvandi Kandanaarachchi; Mario A Munoz; Rob J Hyndman; Kate Smith-Miles
  10. Dynamic price jumps: The performance of high frequency tests and measures, and the robustness of inference By Worapree Maneesoonthorn; Gael M Martin; Catherine S Forbes
  11. Focused econometric estimation for noisy and small datasets: A Bayesian Minimum Expected Loss estimator approach By Andres Ramirez-Hassan; Manuel Correa-Giraldo
  12. Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada By Licht, Adrian; Escribano Sáez, Álvaro; Blazsek, Szabolcs Istvan

  1. By: Götz, Thomas B.; Hauzenberger, Klemens
    Abstract: To simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural changes, we introduce a time-varying parameter mixed-frequency VAR. To keep our approach from becoming too complex, we implement time variation parsimoniously: only the intercepts and a common factor in the error variances vary over time. We can therefore estimate moderately large systems in a reasonable amount of time, which makes our modifications appealing for practical use. For eleven U.S. variables, we examine the performance of our model and compare the results to the time-constant MF-VAR of Schorfheide and Song (2015). Our results demonstrate the feasibility and usefulness of our method.
    Keywords: Mixed Frequencies,Time-Varying Intercepts,Common Stochastic Volatility,Bayesian VAR,Forecasting
    JEL: C32 C51 C53
    Date: 2018
  2. By: Junyan Liu; Sandeep Kumar; Daniel P. Palomar
    Abstract: The autoregressive (AR) model is a widely used model to understand time series data. Traditionally, the innovation noise of the AR is modeled as Gaussian. However, many time series applications, for example, financial time series data are non-Gaussian, therefore, the AR model with more general heavy-tailed innovations are preferred. Another issue that frequently occurs in time series is missing values, due to the system data record failure or unexpected data loss. Although there are numerous works about Gaussian AR time series with missing values, as far as we know, there does not exist any work addressing the issue of missing data for the heavy-tailed AR model. In this paper, we consider this issue for the first time, and propose an efficient framework for the parameter estimation from incomplete heavy-tailed time series based on the stochastic approximation expectation maximization (SAEM) coupled with a Markov Chain Monte Carlo (MCMC) procedure. The proposed algorithm is computationally cheap and easy to implement. The convergence of the proposed algorithm to a stationary point of the observed data likelihood is rigorously proved. Extensive simulations on synthetic and real datasets demonstrate the efficacy of the proposed framework.
    Date: 2018–09
  3. By: Gloria Gonzalez-Rivera (Department of Economics, University of California Riverside); Yun Luo (University of California, Riverside); Esther Ruiz (Universidad Carlos III de Madrid)
    Abstract: We approximate probabilistic forecasts for interval-valued time series by offering alternative approaches to construct bivariate prediction regions of the interval center and range (or lower/upper bounds). We estimate a bivariate system of the center/log-range, which may not be normally distributed. Implementing analytical or bootstrap methods, we directly transform prediction regions for center/log-range into those for center/range and upper/lower bounds systems. We propose new metrics to evaluate the regions performance. Monte Carlo simulations show bootstrap methods being preferred even in Gaussian systems. For daily SP500 low/high return intervals, we build joint conditional prediction regions of the return level and return volatility.
    Keywords: Bootstrap, Constrained Regression, Coverage Rates, Logarithmic Transformation, QML estimation
    JEL: C01 C22 C53
    Date: 2018–10
  4. By: Joshua Chan (Purdue University); Arnaud Doucet (University of Oxford); Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies, Tokyo, Japan); Rodney W. Strachan (University of Queensland)
    Abstract: This paper develops a new methodology that decomposes shocks into homoscedastic and heteroscedastic components. This specification implies there exist linear combinations of heteroscedastic variables that eliminate heteroscedasticity. That is, these linear combinations are homoscedastic; a property we call co-heteroscedasticity. The heteroscedastic part of the model uses a multivariate stochastic volatility inverse Wishart process. The resulting model is invariant to the ordering of the variables, which we show is important for impulse response analysis but is generally important for, e.g., volatility estimation and variance decompositions. The specification allows estimation in moderately high-dimensions. The computational strategy uses a novel particle lter algorithm, a reparameterization that substantially improves algorithmic convergence and an alternating-order particle Gibbs that reduces the amount of particles needed for accurate estimation. We provide two empirical applications; one to exchange rate data and another to a large Vector Autoregression (VAR) of US macroeconomic variables. We find strong evidence for co-heteroscedasticity and, in the second application, estimate the impact of monetary policy on the homoscedastic and heteroscedastic components of macroeconomic variables.
    Date: 2018–10
  5. By: Licht, Adrian; Escribano Sáez, Álvaro; Blazsek, Szabolcs Istvan
    Abstract: We suggest a new mechanism to detect stochastic seasonality of multivariate macroeconomic variables, by using an extension of the score-driven first-order multivariate t-distribution model. We name the new model as the quasi-vector autoregressive (QVAR) model. QVAR is a nonlinear extension of Gaussian VARMA (VAR moving average). The location of dependent variables for QVAR is updated by the score function, thus QVAR is robust to extreme observations. For QVAR, we present the econometric formulation, computation of the impulse response function (IRF), maximum likelihood (ML) estimation, and conditions of the asymptotic properties of ML that include invertibility. We use quarterly macroeconomic data for the period of 1987:Q1 to 2013:Q2 inclusive, which include extreme observations from three I(0) variables: percentage change in crude oil real price, United States (US) inflation rate, and US real gross domestic product (GDP) growth. The sample size of these data is relatively small, which occurs frequently in macroeconomic analyses. The statistical performance of QVAR is superior to that of VARMA and VAR. Annual seasonality effects are identified for QVAR, whereas those effects are not identified for VARMA and VAR. Our results suggest that QVAR may be used as a practical tool for seasonality detection in small macroeconomic datasets.
    JEL: C52 C32
    Date: 2018–09–12
  6. By: Yanfei Kang; Rob J Hyndman; Feng Li
    Abstract: The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires a diverse collection of time series data to enable reliable comparisons against alternative approaches. We propose the use of mixture autoregressive (MAR) models to generate collections of time series with diverse features. We simulate sets of time series using MAR models and investigate the diversity and coverage of the simulated time series in a feature space. An efficient method is also proposed for generating new time series with controllable features by tuning the parameters of the MAR models. The simulated data based on our method can be used as evaluation tool for tasks such as time series classification and forecasting.
    Keywords: time series features, time series generation, mixture autoregressive models
    Date: 2018
  7. By: Aknouche, Abdelhakim; Demouche, Nacer
    Abstract: We propose a double mixed Poisson autoregression in which the intensity, scaled by a unit mean independent and identically distributed (iid) mixing process, has different regime specifications according to the state of a finite unobserved iid chain. Under some contraction in mean conditions, we show that the proposed model is strictly stationary and ergodic with a finite mean. Applications to various count time series models are given.
    Keywords: Double mixed Poisson autoregression, negative binomial mixture INGARCH model, ergodicity, weak dependence, contraction in mean
    JEL: C40 C46 C50
    Date: 2018–03–03
  8. By: Zacharias Psaradakis (University of London); Marian Vavra (National Bank of Slovakia)
    Abstract: TThe paper considers the problem of testing for symmetry (about an unknown centre) of the marginal distribution of a strictly stationary and weakly dependent stochastic process. The possibility of using the autoregressive sieve bootstrap and stationary bootstrap procedures to obtain critical values and P-values for symmetry tests is explored. Bootstrap-assisted tests for symmetry are straightforward to implement and require no prior estimation of asymptotic variances. The small-sample properties of a wide variety of tests are investigated using Monte Carlo experiments. A bootstrap-assisted version of the triples test is found to have the best overall performance.
    Keywords: Autoregressive sieve bootstrap; Stationary bootstrap; Symmetry; Weak dependence
    JEL: C12 C15 C22
    Date: 2018–10
  9. By: Sevvandi Kandanaarachchi; Mario A Munoz; Rob J Hyndman; Kate Smith-Miles
    Abstract: This paper demonstrates that the performance of various outlier detection methods depends sensitively on both the data normalization schemes employed, as well as characteristics of the datasets. Recasting the challenge of understanding these dependencies as an algorithm selection problem, we perform the first instance space analysis of outlier detection methods. Such analysis enables the strengths and weaknesses of unsupervised outlier detection methods to be visualized and insights gained into which method and normalization scheme should be selected to obtain the most likely best performance for a given dataset.
    Date: 2018
  10. By: Worapree Maneesoonthorn; Gael M Martin; Catherine S Forbes
    Abstract: This paper provides an extensive evaluation of high frequency jump tests and measures, in the context of using such tests and measures in the estimation of dynamic models for asset price jumps. Specifically, we investigate: i) the power of alternative tests to detect individual price jumps, most notably in the presence of volatility jumps; ii) the frequency with which sequences of dynamic jumps are correctly identified; iii) the accuracy with which the magnitude and sign of a sequence of jumps, including small clusters of consecutive jumps, are estimated; and iv) the robustness of inference about dynamic jumps to test and measure design. Substantial differences are discerned in the performance of alternative methods in certain dimensions, with inference being sensitive to these differences in some cases. Accounting for measurement error when using measures constructed from high frequency data to conduct inference on dynamic jump models is also shown to have an impact. The sensitivity of inference to test and measurement construction is documented using both artificially generated data and empirical data on both the S&P500 stock index and the IBM stock price. The paper concludes by providing guidelines for empirical researchers who wish to exploit high frequency data when drawing conclusions regarding dynamic jump processes.
    Keywords: price jump tests, nonparametric jump measures, Hawkes process, discretized jump diffusion model, volatility jumps, Bayesian Markov chain Monte Carlo
    JEL: C12 C22 C58
    Date: 2018
  11. By: Andres Ramirez-Hassan; Manuel Correa-Giraldo
    Abstract: Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug-in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug-in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterized by small sample sizes and noisy models. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when datasets are not very informative.
    Date: 2018–09
  12. By: Licht, Adrian; Escribano Sáez, Álvaro; Blazsek, Szabolcs Istvan
    Abstract: We introduce the Seasonal-QVAR (quasi-vector autoregressive) model that we apply to study the relationship between oil production and economic activity. Seasonal-QVAR is a score-driven nonlinear model for the multivariate t distribution. It is an alternative to the basic structural model that disentangles local level and stochastic seasonality. Seasonal-QVAR is robust to extreme observations and it is an extension of Seasonal-VARMA (VAR moving average). We use monthly data from world crude oil production growth, global real economic activity growth and the industrial production growths of the United States and Canada. We address an important economic question about the influence of world crude oil production on the industrial productions of the United States and Canada. We find that the effects of industrial production growth of the United States on world crude oil production growth are about six times higher for the basic structural model and Seasonal-VARMA than for Seasonal-QVAR. We also find that the effects of world crude oil production growth on the industrial production growth of Canada are positive for Seasonal-QVAR, but those effects are negative for Seasonal-VARMA. Likelihood-based performance metrics and transitivity arguments support the estimates of Seasonal- QVAR, as opposed to the basic structural model and Seasonal-VARMA.
    Keywords: Vector autoregressive moving average (VARMA) model; Basic structural model; Nonlinear multivariate dynamic location models; Score-driven stochastic seasonality; Dynamic conditional score (DCS)
    Date: 2018–09–12

This nep-ets issue is ©2018 by Jaqueson K. Galimberti. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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