nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒04‒04
four papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Long Run Risk in Stationary Structural Vector Autoregressive Models By Christian Gourieroux; Joann Jasiak
  2. A multivariate extension of the Misspecification-Resistant Information Criterion By Gery Andr\'es D\'iaz Rubio; Simone Giannerini; Greta Goracci
  3. Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates By Gary Koop; Stuart McIntyre; James Mitchell; Aubrey Poon
  4. Forecasting US Inflation Using Bayesian Nonparametric Models By Todd E. Clark; Florian Huber; Gary Koop; Massimiliano Marcellino

  1. By: Christian Gourieroux; Joann Jasiak
    Abstract: This paper introduces a local-to-unity/small sigma process for a stationary time series with strong persistence and non-negligible long run risk. This process represents the stationary long run component in an unobserved short- and long-run components model involving different time scales. More specifically, the short run component evolves in the calendar time and the long run component evolves in an ultra long time scale. We develop the methods of estimation and long run prediction for the univariate and multivariate Structural VAR (SVAR) models with unobserved components and reveal the impossibility to consistently estimate some of the long run parameters. The approach is illustrated by a Monte-Carlo study and an application to macroeconomic data.
    Date: 2022–02
  2. By: Gery Andr\'es D\'iaz Rubio; Simone Giannerini; Greta Goracci
    Abstract: The Misspecification-Resistant Information Criterion (MRIC) proposed in [H.-L. Hsu, C.-K. Ing, H. Tong: On model selection from a finite family of possibly misspecified time series models. The Annals of Statistics. 47 (2), 1061--1087 (2019)] is a model selection criterion for univariate parametric time series that enjoys both the property of consistency and asymptotic efficiency. In this article we extend the MRIC to the case where the response is a multivariate time series and the predictor is univariate. The extension requires novel derivations based upon random matrix theory. We obtain an asymptotic expression for the mean squared prediction error matrix, the vectorial MRIC and prove the consistency of its method-of-moments estimator. Moreover, we prove its asymptotic efficiency. Finally, we show with an example that, in presence of misspecification, the vectorial MRIC identifies the best predictive model whereas traditional information criteria like AIC or BIC fail to achieve the task.
    Date: 2022–02
  3. By: Gary Koop; Stuart McIntyre; James Mitchell; Aubrey Poon
    Abstract: Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common features of the regional nowcasting context, using an application to regional productivity in the UK. We evaluate the contribution that different features of our model provide to the accuracy of point and density nowcasts, in particular the role of hierarchical aggregation constraints. We show that these aggregation constraints, imposed in stochastic form, play a key role in delivering improved regional nowcasts; they prove to be more important than adding region-specific predictors when the equivalent national data are known, but not when this aggregate is unknown.
    Keywords: Regional data; Mixed frequency; Nowcasting; Bayesian methods; Real-time data; Vector autoregressions
    JEL: C32 C53 E37
    Date: 2022–03–03
  4. By: Todd E. Clark; Florian Huber; Gary Koop; Massimiliano Marcellino
    Abstract: The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial benefits, both overall and in the left tail, with nonparametric modeling of the conditional mean being of particular importance.
    Keywords: nonparametric regression; Gaussian process; Dirichlet process mixture; inflation forecasting
    JEL: C11 C32 C53
    Date: 2022–03–02

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