nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒10‒02
seven papers chosen by
Jaqueson K. Galimberti, Asian Development Bank

  1. Precision-based sampling for state space models that have no measurement error By Mertens, Elmar
  2. Stochastic Variational Inference for GARCH Models By Hanwen Xuan; Luca Maestrini; Feng Chen; Clara Grazian
  3. Towards seasonal adjustment of infra-monthly time series with JDemetra+ By Webel, Karsten; Smyk, Anna
  4. Econometrics of Machine Learning Methods in Economic Forecasting By Andrii Babii; Eric Ghysels; Jonas Striaukas
  5. Impulse Response Functions for Self-Exciting Nonlinear Models By Neville Francis; Michael T. Owyang; Daniel Soques
  6. Recurrent Neural Networks with more flexible memory: better predictions than rough volatility By Damien Challet; Vincent Ragel
  7. Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations By Camilo Granados; Daniel Parra-Amado

  1. By: Mertens, Elmar
    Abstract: This article presents a computationally efficient approach to sample from Gaussian state space models. The method is an instance of precision-based sampling methods that operate on the inverse variance-covariance matrix of the states (also known as precision). The novelty is to handle cases where the observables are modeled as a linear combination of the states without measurement error. In this case, the posterior variance of the states is singular and precision is ill-defined. As in other instances of precision-based sampling, computational gains are considerable. Relevant applications include trend-cycle decompositions, (mixed-frequency) VARs with missing variables and DSGE models.
    Keywords: State space models, signal extraction, Kalman filter and smoother, precision-based sampling, band matrix
    JEL: C11 C32 C51
    Date: 2023
  2. By: Hanwen Xuan; Luca Maestrini; Feng Chen; Clara Grazian
    Abstract: Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skew-t response GARCH models and fit these using Gaussian variational approximating densities. We implement efficient stochastic gradient ascent procedures based on the use of control variates or the reparameterization trick and demonstrate that the proposed implementations provide a fast and accurate alternative to Markov chain Monte Carlo sampling. Additionally, we present sequential updating versions of our variational algorithms, which are suitable for efficient portfolio construction and dynamic asset allocation.
    Date: 2023–08
  3. By: Webel, Karsten; Smyk, Anna
    Abstract: Infra-monthly economic time series have become increasingly popular in official statistics in recent years. This evolution has been largely fostered by official statistics' digital transformation during the last decade. The COVID-19 pandemic outbreak in 2020 has added fuel to the fire as many data users immediately asked for timely weekly and even daily data on economic developments. Such infra-monthly data often display seasonal behavior that calls for adjustment. For that reason, JDemetra+, the official software for harmonized seasonal adjustment of monthly and quarterly data in the European Statistical System and the European System of Central Banks, has been augmented recently with a regARIMA-esque pretreatment model and extended versions of the ARIMA model-based, STL and X-11 seasonal adjustment approaches that are tailored to the specifics of infra-monthly data and accessible through an ecosystem of R packages. This ecosystem also provides easy access to structural time series modeling. We give a comprehensive overview of the packages' current developmental stage and illustrate selected capabilities, including code snippets, using daily births in France, hourly electricity consumption in Germany, and weekly initial claims for unemployment insurance in the United States.
    Keywords: extended Airline model, high-frequency data, official statistics, signalextraction, unobserved-components decomposition
    JEL: C01 C02 C14 C18 C22 C40 C50
    Date: 2023
  4. By: Andrii Babii; Eric Ghysels; Jonas Striaukas
    Abstract: This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
    Date: 2023–08
  5. By: Neville Francis; Michael T. Owyang; Daniel Soques
    Abstract: We calculate impulse response functions from regime-switching models where the driving variable can respond to the shock. Two methods used to estimate the impulse responses in these models are generalized impulse response functions and local projections. Local projections depend on the observed switches in the data, while generalized impulse response functions rely on correctly specifying regime process. Using Monte Carlos with different misspecifications, we determine under what conditions either method is preferred. We then extend model-average impulse responses to this nonlinear environment and show that they generally perform better than either generalized impulse response functions and local projections. Finally, we apply these findings to the empirical estimation of regime-dependent fiscal multipliers and find multipliers less than one and generally small differences across different states of slack.
    Keywords: generalized impulse response functions; local projections; threshold models; model averaging
    JEL: C22 C24 E62
    Date: 2023–08–29
  6. By: Damien Challet; Vincent Ragel
    Abstract: We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales. We compare the ability of vanilla and extended long short term memory networks (LSTMs) to predict asset price volatility, known to have a long memory. Generally, the number of epochs needed to train extended LSTMs is divided by two, while the variation of validation and test losses among models with the same hyperparameters is much smaller. We also show that the model with the smallest validation loss systemically outperforms rough volatility predictions by about 20% when trained and tested on a dataset with multiple time series.
    Date: 2023–08
  7. By: Camilo Granados; Daniel Parra-Amado
    Abstract: This study examines whether and how important it is to adjust output gap frameworks during the COVID-19 pandemic and similar unprecedentedly large-scale episodes. Our proposed modelling framework comprises a Bayesian Structural Vector Autoregressions with an identification setup based on a permanent-transitory decomposition that exploits the long-run relationship of consumption with output and whose residuals are scaled up around the COVID-19 period. Our results indicate that (i) a single structural error is usually sufficient to explain the permanent component of the gross domestic product (GDP); (ii) the adjusted method allows for the incorporation of the COVID-19 period without assuming sudden changes in the modelling setup after the pandemic; and (iii) the proposed adjustment generates approximation improvements relative to standard filters or similar models with no adjustments or alternative ones, but where the specific rare observations are not known. Importantly, abstracting from any adjustment may lead to over or underestimating the gap, to too-quick gap recoveries after downturns, or too-large volatility around the median potential output estimations. **** RESUMEN: Esta investigación examina si y cómo es importante ajustar la estimación de la brecha de producto (PIB) durante la pandemia de COVID-19. Para ello, proponemos dentro de un enfoque bayesiano un modelo de Vectores Autoregresivos estructurales (BSVAR) con un esquema de identificación basado en la descomposición de choques permanentes y transitorios que explota la relación de largo plazo entre el consumo y el PIB, y cuyos residuales se escalan alrededor del periodo de COVID-19. Nuestros resultados indican que (i) Con un sólo choque estructural es suficiente para explicar la componente permanente del PIB; (ii) el método ajustado permite la incorporación del período de COVID-19 sin asumir cambios bruscos en la configuración de modelización después de la pandemia; y (iii) el ajuste propuesto genera mejoras en la aproximación en comparación con filtros estándar u otros modelos similares sin ajustes o alternativos, pero donde las observaciones específicas poco comunes no son conocidas. Es importante destacar que prescindir de cualquier ajuste puede llevar a sobreestimar o subestimar la brecha de PIB, a una recuperación de la brecha demasiado rápida después de las caídas o a una volatilidad demasiado grande alrededor de la mediana de dichas estimaciones.
    Keywords: Bayesian methods, business cycles, potential output, output gaps, structural estimation, Métodos Bayesianos, Ciclos económicos, Producto potencial, Brecha de producto, Estimación estructural
    JEL: C11 C53 E3 E32 E37
    Date: 2023–09

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