nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒08‒22
three papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. A General Limit Theory for Nonlinear Functionals of Nonstationary Time Series By Qiying Wang; Peter C. B. Phillips
  2. Econometric Analysis of Asset Price Bubbles By Shuping Shi; Peter C. B. Phillips
  3. LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling By Bartosz Uniejewski; Katarzyna Maciejowska

  1. By: Qiying Wang (University of Sydney); Peter C. B. Phillips (Cowles Foundation, Yale University, University of Auckland, Singapore Management University, University of Southampton)
    Abstract: Limit theory is provided for a wide class of covariance functionals of a nonstationary process and stationary time series. The results are relevant to estimation and inference in nonlinear nonstationary regressions that involve unit root, local unit root or fractional processes and they include both parametric and nonparametric regressions. Self normalized versions of these statistics are considered that are useful in inference. Numerical evidence reveals a strong bimodality in the ?nite sample distributions that persists for very large sample sizes although the limit theory is Gaussian. New self normalized versions are introduced that deliver improved approximations.
    Keywords: Endogeneity, Limit theory, Local time, Nonlinear functional, Nonstationarity, Sample covariance, Zero energy
    JEL: C22 C23
    Date: 2022–07
  2. By: Shuping Shi (Macquarie University); Peter C. B. Phillips (Cowles Foundation, Yale University, University of Auckland, Singapore Management University, University of Southampton)
    Abstract: In the presence of bubbles, asset prices consist of a fundamental and a bubble component, with the bubble component following an explosive dynamic. The general idea for bubble identification is to apply explosive root tests to a proxy of the unobservable bubble. Three notable proxies are the real asset prices, log price-payoff ratios, and estimated non-fundamental components. The rationale for all three proxy choices rests on the definition of bubbles, which has been presented in various forms in the literature. This chapter provides a theoretical framework that incorporates several definitions of bubbles (and fundamentals) and offers guidance for selecting proxies. For explosive root tests, we introduce the recursive evolving test of Phillips et al. (2015b,c) along with its asymptotic properties. This procedure can serve as a real-time monitoring device and has been shown to outperform several other tests. Like all other recursive testing procedures, the PSY algorithm faces the issue of multiplicity in testing that contaminates conventional significance values. To address this issue, we propose a multiple-testing algorithm to determine appropriate test critical values and show its satisfactory performance in finite samples by simulations. To illustrate, we conduct a pseudo real-time bubble monitoring exercise in the S&P 500 stock market from January 1990 to June 2020. The empirical results reveal the importance of using a good proxy for bubbles and addressing the multiplicity issue.
    Keywords: Bubbles; econometrics identification; market fundamental; explosive root; multiplicity; S&P 500 composite index
    JEL: C15 C22
    Date: 2022–06
  3. By: Bartosz Uniejewski; Katarzyna Maciejowska
    Abstract: This paper develops a novel, fully automated forecast averaging scheme, which combines LASSO estimation method with Principal Component Averaging (PCA). LASSO-PCA (LPCA) explores a pool of predictions based on a single model but calibrated to windows of different sizes. It uses information criteria to select tuning parameters and hence reduces the impact of researchers' at hock decisions. The method is applied to average predictions of hourly day-ahead electricity prices over 650 point forecasts obtained with various lengths of calibration windows. It is evaluated on four European and American markets with almost two and a half year of out-of-sample period and compared to other semi- and fully automated methods, such as simple mean, AW/WAW, LASSO and PCA. The results indicate that the LASSO averaging is very efficient in terms of forecast error reduction, whereas PCA method is robust to the selection of the specification parameter. LPCA inherits the advantages of both methods and outperforms other approaches in terms of MAE, remaining insensitive the the choice of a tuning parameter.
    Date: 2022–07

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