nep-ets New Economics Papers
on Econometric Time Series
Issue of 2020‒11‒23
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Sparse time-varying parameter VECMs with an application to modeling electricity prices By Niko Hauzenberger; Michael Pfarrhofer; Luca Rossini
  2. An Alternative Bootstrap for Proxy Vector Autoregressions By Martin Bruns; Helmut Luetkepohl
  3. Instrumental Variable Identification of Dynamic Variance Decompositions By Mikkel Plagborg-M{\o}ller; Christian K. Wolf
  4. Learning from Forecast Errors: A New Approach to Forecast Combinations By Tae-Hwy Lee; Ekaterina Seregina
  5. Using mixed-frequency and realized measures in quantile regression By Vincenzo Candila; Giampiero M. Gallo; Lea Petrella
  6. Developments on the Bayesian Structural Time Series Model: Trending Growth By David Kohns; Arnab Bhattacharjee

  1. By: Niko Hauzenberger; Michael Pfarrhofer; Luca Rossini
    Abstract: In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroscedastic disturbances. We combine a set of econometric techniques for dynamic model specification in an automatic fashion. We employ continuous global-local shrinkage priors for pushing the parameter space towards sparsity. In a second step, we post-process the cointegration relationships, the autoregressive coefficients and the covariance matrix via minimizing Lasso-type loss functions to obtain truly sparse estimates. This two-step approach alleviates overfitting concerns and reduces parameter estimation uncertainty, while providing estimates for the number of cointegrating relationships that varies over time. Our proposed econometric framework is applied to modeling European electricity prices and shows gains in forecast performance against a set of established benchmark models.
    Date: 2020–11
  2. By: Martin Bruns (University of East Anglia); Helmut Luetkepohl (DIW Berlin and Freie Universitaet Berlin)
    Abstract: We propose a new bootstrap for inference for impulse responses in structural vector autoregressive models identi ed with an external proxy variable. Simulations show that the new bootstrap provides confidence intervals for impulse responses which often have more precise coverage than and similar length as the competing moving-block bootstrap intervals. An empirical example shows how the new bootstrap can be applied in the context of identifying monetary policy shocks.
    Keywords: Bootstrap inference, structural vector autoregression, impulse responses, instrumental variable
    JEL: C32
    Date: 2020–11–11
  3. By: Mikkel Plagborg-M{\o}ller; Christian K. Wolf
    Abstract: Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various additional restrictions guarantee point identification of both variance and historical decompositions. Unlike SVAR analysis, our methods do not require invertibility. Applied to U.S. data, they give a tight upper bound on the importance of monetary shocks for inflation dynamics.
    Date: 2020–11
  4. By: Tae-Hwy Lee; Ekaterina Seregina
    Abstract: This paper studies forecast combination (as an expert system) using the precision matrix estimation of forecast errors when the latter admit the approximate factor model. This approach incorporates the facts that experts often use common sets of information and hence they tend to make common mistakes. This premise is evidenced in many empirical results. For example, the European Central Bank's Survey of Professional Forecasters on Euro-area real GDP growth demonstrates that the professional forecasters tend to jointly understate or overstate GDP growth. Motivated by this stylized fact, we develop a novel framework which exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic components of the forecast errors. The proposed algorithm is called Factor Graphical Model (FGM). Our approach overcomes the challenge of obtaining the forecasts that contain unique information, which was shown to be necessary to achieve a "winning" forecast combination. In simulation, we demonstrate the merits of the FGM in comparison with the equal-weighted forecasts and the standard graphical methods in the literature. An empirical application to forecasting macroeconomic time series in big data environment highlights the advantage of the FGM approach in comparison with the existing methods of forecast combination.
    Date: 2020–11
  5. By: Vincenzo Candila; Giampiero M. Gallo; Lea Petrella
    Abstract: Quantile regression is an efficient tool when it comes to estimate popular measures of tail risk such as the conditional quantile Value at Risk. In this paper we exploit the availability of data at mixed frequency to build a volatility model for daily returns with low-- (for macro--variables) and high--frequency (which may include an \virg{--X} term related to realized volatility measures) components. The quality of the suggested quantile regression model, labeled MF--Q--ARCH--X, is assessed in a number of directions: we derive weak stationarity properties, we investigate its finite sample properties by means of a Monte Carlo exercise and we apply it on financial real data. VaR forecast performances are evaluated by backtesting and Model Confidence Set inclusion among competitors, showing that the MF--Q--ARCH--X has a consistently accurate forecasting capability.
    Date: 2020–11
  6. By: David Kohns; Arnab Bhattacharjee
    Abstract: This paper investigates the added benefit of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of the mixed frequency augmented Bayesian Structural Time Series model (BSTS) of Scott and Varian (2014). We show that a large dimensional set of search terms are able to improve nowcasts before other macro data becomes available early on the quarter. Search terms with high inclusion probability have negative correlation with GDP growth, which we reason to stem from them signalling special attention likely due to expected large troughs. We further offer several improvements on the priors: we allow to shrink state variances to zero to avoid overfitting states, extend the SSVS prior to the more flexible normal-inverse-gamma prior of Ishwaran et al. (2005) which stays agnostic about the underlying model size, as well as adapt the horseshoe prior of Carvalho et al. (2010) to the BSTS. The application to nowcasting GDP growth as well as a simulation study show that the horseshoe prior BSTS improves markedly over the SSVS and the original BSTS model, with largest gains to be expected in dense data-generating-processes.
    Date: 2020–11

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