nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒05‒25
nine papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Modeling High-Dimensional Unit-Root Time Series By Zhaoxing Gao; Ruey S. Tsay
  2. Investing in VIX futures based on rolling GARCH models forecasts By Oleh Bilyk; Paweł Sakowski; Robert Ślepaczuk
  3. Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus By Christian GOURIEROUX; Joann JASIAK
  4. Fast and Accurate Variational Inference for Models with Many Latent Variables By Rub\'en Loaiza-Maya; Michael Stanley Smith; David J. Nott; Peter J. Danaher
  5. Dynamic shrinkage in time-varying parameter stochastic volatility in mean models By Florian Huber; Michael Pfarrhofer
  6. Energy Markets and Global Economic Conditions By Christiane Baumeister; Dimitris Korobilis; Thomas K. Lee
  7. Nowcasting Finnish GDP growth using financial variables: a MIDAS approach By Laine, Olli-Matti; Lindblad, Annika
  8. Bayesian dynamic variable selection in high dimensions By Gary Koop; Dimitris Korobilis
  9. Nowcasting Economic Activity in Times of COVID-19 : An Approximation from the Google Community Mobility Report By Sampi Bravo,James Robert Ezequiel; Jooste,Charl

  1. By: Zhaoxing Gao; Ruey S. Tsay
    Abstract: In this paper, we propose a new procedure to build a structural-factor model for a vector unit-root time series. For a $p$-dimensional unit-root process, we assume that each component consists of a set of common factors, which may be unit-root non-stationary, and a set of stationary components, which contain the cointegrations among the unit-root processes. To further reduce the dimensionality, we also postulate that the stationary part of the series is a nonsingular linear transformation of certain common factors and idiosyncratic white noise components as in Gao and Tsay (2019a, b). The estimation of linear loading spaces of the unit-root factors and the stationary components is achieved by an eigenanalysis of some nonnegative definite matrix, and the separation between the stationary factors and the white noises is based on an eigenanalysis and a projected principal component analysis. Asymptotic properties of the proposed method are established for both fixed $p$ and diverging $p$ as the sample size $n$ tends to infinity. Both simulated and real examples are used to demonstrate the performance of the proposed method in finite samples.
    Date: 2020–05
  2. By: Oleh Bilyk; Paweł Sakowski (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw)
    Abstract: The aim of this work is to compare the performance of VIX futures trading strategies built across different GARCH model volatility forecasting techniques. Long and short signals for VIX futures are produced by comparing one-day ahead volatility forecasts with current historical volatility. We found out that using the daily data over the seven-year period (2013-2019), strategy based on the fGARCH-TGARCH and GJR-GARCH specifications outperformed those of the GARCH and EGARCH models, and performed slightly below the “buy-and-hold” S&P 500 strategy. For the base GARCH(1,1) model, the training window size and the type gave stable results, whereas the performance across refit frequency, conditional distribution of returns, and historical volatility estimators varies significantly. Despite non-robustness of some investment strategies and some space for improvements, the presented strategies show their potential in competing with the equity and volatility benchmarks.
    Keywords: GARCH, VIX index, volatility futures, rolling forecasting, volatility, investment strategies, volatility exposure
    JEL: C4 C45 C61 C15 G14 G17
    Date: 2020
  3. By: Christian GOURIEROUX (University of Toronto, Toulouse School of Economics and CREST); Joann JASIAK (York University, Canada)
    Abstract: A major difficulty in the analysis of propagation of the coronavirus is that many infected individuals show no symptoms of Covid-19. This implies a lack of information on the total counts of infected individuals and of recovered and immunized individuals. In this paper, we consider parametric time varying Markov processes of Coronavirus propagation and show how to estimate the model parameters and approximate the unobserved counts from daily numbers of infected and detectedi ndividuals and total daily death counts. This model-based approach is illustrated in an application to French data.
    Keywords: Markov Process; Partial Observability; Information Recovery; Estimating Equations; SIR Model; Coronavirus; Infection Rate.
    Date: 2020–03–31
  4. By: Rub\'en Loaiza-Maya; Michael Stanley Smith; David J. Nott; Peter J. Danaher
    Abstract: Models with a large number of latent variables are often used to fully utilize the information in big or complex data. However, they can be difficult to estimate using standard approaches, and variational inference methods are a popular alternative. Key to the success of these is the selection of an approximation to the target density that is accurate, tractable and fast to calibrate using optimization methods. Mean field or structured Gaussian approximations are common, but these can be inaccurate and slow to calibrate when there are many latent variables. Instead, we propose a family of tractable variational approximations that are more accurate and faster to calibrate for this case. The approximation is a parsimonious copula model for the parameter posterior, combined with the exact conditional posterior of the latent variables. We derive a simplified expression for the re-parameterization gradient of the variational lower bound, which is the main ingredient of efficient optimization algorithms used to implement variational estimation. We illustrate using two substantive econometric examples. The first is a nonlinear state space model for U.S. inflation. The second is a random coefficients tobit model applied to a rich marketing dataset with one million sales observations from a panel of 10,000 individuals. In both cases, we show that our approximating family is faster to calibrate than either mean field or structured Gaussian approximations, and that the gains in posterior estimation accuracy are considerable.
    Date: 2020–05
  5. By: Florian Huber; Michael Pfarrhofer
    Abstract: Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state-of-the-art shrinkage techniques that allow for time-variation in the degree of shrinkage. Using a real-time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters slightly improves forecast performance for the United States (US), the United Kingdom (UK) and the Euro Area (EA). Comparing in-sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
    Date: 2020–05
  6. By: Christiane Baumeister; Dimitris Korobilis; Thomas K. Lee
    Abstract: This paper evaluates alternative indicators of global economic activity and other market fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of the most useful indicators that has been proposed in the literature. However, by combining measures from a number of different sources we can do even better. Our analysis results in a new index of global economic conditions and new measures for assessing future tightness of energy demand and expected oil price pressures.
    Keywords: Energy demand, forecasting, stochastic volatility, oil price pressures, petroleum consumption, state of the world economy
    JEL: C11 C32 C52 Q41 Q47
    Date: 2020–02
  7. By: Laine, Olli-Matti; Lindblad, Annika
    Abstract: We analyse the performance of financial market variables in nowcasting Finnish quarterly GDP growth. Especially, we assess if prediction accuracy is affected by the sampling frequency of the financial variables. Therefore, we apply MIDAS models that allow us to forecast quarterly GDP growth using monthly or daily data without temporal aggregation in a parsimonious way. Our results show that financial market data nowcasts Finnish GDP growth relatively well. When it comes to individual variables, ratios like average price-to-earnings, average price-to-book or average dividend yield track GDP growth well. Our results suggest that the sampling frequency of financial market variables is not crucial: the forecasting accuracy of daily, monthly and quarterly data is similar.
    Keywords: MIDAS,Nowcasting,Financial markets,GDP
    JEL: E44 G00 E37
    Date: 2020
  8. By: Gary Koop; Dimitris Korobilis
    Abstract: This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large number of predictors. This strategy allows for assessing in individual time periods which predictors are relevant (or not) for forecasting the dependent variable. The new algorithm is evaluated numerically using synthetic data and its computational advantages are established. Using macroeconomic data for the US we find that regression models that combine time-varying parameters with the information in many predictors have the potential to improve forecasts of price inflation over a number of alternative forecasting models.
    Keywords: dynamic linear model; approximate posterior inference; dynamic variable selection; forecasting
    JEL: C11 C13 C52 C53 C61
    Date: 2020–05
  9. By: Sampi Bravo,James Robert Ezequiel; Jooste,Charl
    Abstract: This paper proposes a leading indicator, the"Google Mobility Index,"for nowcasting monthly industrial production growth rates in selected economies in Latin America and the Caribbean. The index is constructed using the Google COVID-19 Community Mobility Report database via a Kalman filter. The Google database is publicly available starting from February 15, 2020. The paper uses a backcasting methodology to increase the historical number of observations and then augments a lag of one week in the mobility data with other high-frequency data (air quality) over January 1, 2019 to April 30, 2020. Finally, mixed data sampling regression is implemented for nowcasting industrial production growth rates. The Google Mobility Index is a good predictor of industrial production. The results suggest a significant decline in output of between 5 and 7 percent for March and April, respectively, while indicating a trough in output in mid-April.
    Keywords: International Trade and Trade Rules,Health Care Services Industry,Pharmaceuticals Industry,ICT Policy and Strategies,ICT Legal and Regulatory Framework,Economic Conditions and Volatility
    Date: 2020–05–14

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