nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒11‒29
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression By Vito Polito; Yunyi Zhang
  2. Pivotal Test Statistic for Nonparametric Cointegrating Regression Functions By Sepideh Mosaferi; Mark S. Kaiser
  3. Better the Devil You Know: Improved Forecasts from Imperfect Models By Dong Hwan Oh; Andrew J. Patton

  1. By: Vito Polito; Yunyi Zhang
    Abstract: We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure non-explosive dynamics. As such, it is employable to account for nonlinearity in macroeconomic dynamics not only during typical business cycles but also in a wide range of extreme events, like deep recessions and strong expansions. The methodology is put to the test using aggregate data for the United States that include the abnormal realizations during the recent Covid-19 pandemic. The model delivers plausible and stable structural inference, and accurate out-of-sample forecasts. This performance compares favourably against a number of alternative methodologies recently proposed to deal with large outliers in macroeconomic data caused by the pandemic.
    Keywords: nonlinear time series, regime switching models, extreme events, Covid-19, macroeconomic forecasting
    JEL: C45 C50 E37
    Date: 2021
  2. By: Sepideh Mosaferi; Mark S. Kaiser
    Abstract: This article focuses on cointegrating regression models in which covariate processes exhibit long range or semi-long range memory behaviors, and may involve endogeneity in which covariate and response error terms are not independent. We assume semi-long range memory is produced in the covariate process by tempering of random shock coefficients. The fundamental properties of long memory processes are thus retained in the covariate process. We modify a test statistic proposed for the long memory case by Wang and Phillips (2016) to be suitable in the semi-long range memory setting. The limiting distribution is derived for this modified statistic and shown to depend only on the local memory process of standard Brownian motion. Because, unlike the original statistic of Wang and Phillips (2016), the limit distribution is independent of the differencing parameter of fractional Brownian motion, it is pivotal. Through simulation we investigate properties of nonparametric function estimation for semi-long range memory cointegrating models, and consider behavior of both the modified test statistic under semi-long range memory and the original statistic under long range memory. We also provide a brief empirical example.
    Date: 2021–11
  3. By: Dong Hwan Oh; Andrew J. Patton
    Abstract: Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis- speci ed model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspeci cation of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we nd signi cant fore- cast improvements from applying the proposed method across distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.
    Keywords: Model misspecification; Local maximum likelihood; Volatility forecasting; Value-at-risk and expected shortfall forecasting; Yield curve forecasting
    JEL: C53 C51 C58 C14
    Date: 2021–11–05

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