nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒04‒10
four papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A Sufficient Statistical Test for Dynamic Stability By Ahmed, Muhammad Ashfaq; Nawaz, Nasreen
  2. Inference of Grouped Time-Varying Network Vector Autoregression Models By Degui Li; Bin Peng; Songqiao Tang; Weibiao Wu
  3. Improved inference in financial factor models By Elliot Beck; Gianluca De Nard; Michael Wolf
  4. Identifying Optimal Indicators and Lag Terms for Nowcasting Models By Jing Xie

  1. By: Ahmed, Muhammad Ashfaq; Nawaz, Nasreen
    Abstract: In the existing Statistics and Econometrics literature, there does not exist a statistical test which may test for all kinds of roots of the characteristic polynomial leading to an unstable dynamic response, i.e., positive and negative real unit roots, complex unit roots and the roots lying inside the unit circle. This paper develops a test which is sufficient to prove dynamic stability (in the context of roots of the characteristic polynomial) of a univariate as well as a multivariate time series without having a structural break. It covers all roots (positive and negative real unit roots, complex unit roots and the roots inside the unit circle whether single or multiple) which may lead to an unstable dynamic response. Furthermore, it also indicates the number of roots causing instability in the time series. The test is much simpler in its application as compared to the existing tests as the series is strictly stationary under the null.
    Keywords: Dynamic stability, Real and complex roots, Unit circle
    JEL: C01 C12
    Date: 2023–03–16
  2. By: Degui Li; Bin Peng; Songqiao Tang; Weibiao Wu
    Abstract: This paper considers statistical inference of time-varying network vector autoregression models for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with normalized distance matrix estimates is combined with a generalized information criterion to consistently estimate the latent group number and membership. A post-grouping local linear smoothing method is proposed to estimate the group-specific time-varying momentum and network effects, substantially improving the convergence rates of the preliminary estimates which ignore the latent structure. In addition, a post-grouping specification test is conducted to verify the validity of the parametric model assumption for group-specific time-varying coefficient functions, and the asymptotic theory is derived for the test statistic constructed via a kernel weighted quadratic form under the null and alternative hypotheses. Numerical studies including Monte-Carlo simulation and an empirical application to the global trade flow data are presented to examine the finite-sample performance of the developed model and methodology.
    Date: 2023–03
  3. By: Elliot Beck; Gianluca De Nard; Michael Wolf
    Abstract: Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama-French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In this paper, we show that using weighted least squares (WLS) or adaptive least squares (ALS) to estimate model parameters generally leads to smaller HC standard errors compared to ordinary least squares (OLS), which translates into improved inference in the form of shorter confidence intervals and more powerful hypothesis tests. In an extensive empirical analysis based on historical stock returns and commonly used factors, we find that conditional heteroskedasticity is pronounced and that WLS and ALS can dramatically shorten confidence intervals compared to OLS, especially during times of financial turmoil.
    Keywords: CAPM, conditional heteroskedasticity, factor models, HC standard errors
    JEL: C12 C13 C21
    Date: 2023–03
  4. By: Jing Xie
    Abstract: Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p, q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
    Keywords: Nowcasting; Mixed Frequency; Forecasting; Business Cycles; selection procedure; Annex I. AS-ARIMAX procedure; nowcasting method; evaluation comparison; baseline model; Global
    Date: 2023–03–03

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