nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒05‒30
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Nonparametric Estimation and Testing for Time-Varying VAR Models By Jiti Gao; Bin Peng; Yayi Yan
  2. High-Frequency-Based Volatility Model with Network Structure By Huiling Yuan; Guodong Li; Junhui Wang
  3. Uniform and distribution-free inference with general autoregressive processes By Tassos Magdalinos; Katerina Petrova
  4. A One-Covariate-at-a-Time Method for Nonparametric Additive Models By Liangjun Su; Thomas Tao Yang; Yonghui Zhang; Qiankun Zhou
  5. Impulse response estimation via flexible local projections By Haroon Mumtaz; Michele Piffer
  6. GMM is Inadmissible Under Weak Identification By Isaiah Andrews; Anna Mikusheva

  1. By: Jiti Gao; Bin Peng; Yayi Yan
    Abstract: Vector autoregressive (VAR) models are widely used in practical studies, e.g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this paper introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of asymptotic properties including the impulse response analyses subject to structural VAR identification conditions, an information criterion to select the optimal lag, and a Wald-type test to determine the constant coefficients. Simulation studies are conducted to evaluate the theoretical findings. Finally, we demonstrate the empirical relevance and usefulness of the proposed methods through an application on US government spending multipliers.
    Keywords: time-varying impulse response, parameter stability, instrumental variable approach
    JEL: C14 C32 E52
    Date: 2022
  2. By: Huiling Yuan; Guodong Li; Junhui Wang
    Abstract: This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons.
    Date: 2022–04
  3. By: Tassos Magdalinos; Katerina Petrova
    Keywords: uniform inference, central limit theory, autoregression, predictive regression, instrumentation, mixed-Gaussianity, t-statistic, confidence intervals
    Date: 2022–04
  4. By: Liangjun Su; Thomas Tao Yang; Yonghui Zhang; Qiankun Zhou
    Abstract: This paper proposes a one-covariate-at-a-time multiple testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem. One-stage and multiple-stage procedures are both considered. The former works well in terms of the true positive rate only if the marginal effects of all signals are strong enough; the latter helps to pick up hidden signals that have weak marginal effects. Simulations demonstrate the good finite sample performance of the proposed procedures. As an empirical application, we use the OCMT procedure on a dataset we extracted from the Longitudinal Survey on Rural Urban Migration in China. We find that our procedure works well in terms of the out-of-sample forecast root mean square errors, compared with competing methods.
    Date: 2022–04
  5. By: Haroon Mumtaz; Michele Piffer
    Abstract: This paper introduces a flexible local projection that generalizes the model by Jord\'a (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first application shows that the fiscal multiplier is stronger in recession than in expansion only in response to contractionary fiscal shocks, but not in response to expansionary fiscal shocks. We then show that financial shocks generate effects on the economy that increase more than proportionately in the size of the shock when the shock is negative, but not when the shock is positive.
    Date: 2022–04
  6. By: Isaiah Andrews; Anna Mikusheva
    Abstract: We consider estimation in moment condition models and show that under squared error loss and bounds on identification strength, asymptotically admissible (i.e. undominated) estimators must be Lipschitz functions of the sample moments. GMM estimators are in general discontinuous in the sample moment function, and are thus inadmissible under weak identification. We show, by contrast, that quasi-Bayes posterior means and bagged, or bootstrap aggregated, GMM estimators have superior continuity properties, while results in the literaure imply that they are equivalent to GMM when identification is strong. In simulations calibrated to published instrumental variables specifications, we find that these alternatives often outperform GMM. Hence, quasi-Bayes and bagged GMM present attractive alternatives to GMM.
    Date: 2022–04

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