nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒08‒29
ten papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Time-Varying Poisson Autoregression By Giovanni Angelini; Giuseppe Cavaliere; Enzo D'Innocenzo; Luca De Angelis
  2. Variations on two-parameter families of forecasting functions: seasonal/nonseasonal Models, comparison to the exponential smoothing and ARIMA models, and applications to stock market data By Nabil Kahouadji
  3. Weak Identification of Long Memory with Implications for Inference By Jia Li; Peter C. B. Phillips; Shuping Shi; Jun Yu
  4. Asymptotics of Polynomial Time Trend Estimation and Hypothesis Testing under Rank Deficiency By Peter C. B. Phillips
  5. Testing for explosive bubbles: a review By Anton Skrobotov
  6. A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy. By Hall, Viv B; Thomson, Peter
  7. Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes By Gregory Benton; Wesley J. Maddox; Andrew Gordon Wilson
  8. Forecast combination puzzle in the HAR model By Clements, Adam; Vasnev, Andrey
  9. Density forecast comparison in small samples By Laura Coroneo; Fabrizio Iacone; Fabio Profumo
  10. An Econometrician amongst Statisticians: T. W. Anderson By Peter C. B. Phillips

  1. By: Giovanni Angelini; Giuseppe Cavaliere; Enzo D'Innocenzo; Luca De Angelis
    Abstract: In this paper we propose a new time-varying econometric model, called Time-Varying Poisson AutoRegressive with eXogenous covariates (TV-PARX), suited to model and forecast time series of counts. {We show that the score-driven framework is particularly suitable to recover the evolution of time-varying parameters and provides the required flexibility to model and forecast time series of counts characterized by convoluted nonlinear dynamics and structural breaks.} We study the asymptotic properties of the TV-PARX model and prove that, under mild conditions, maximum likelihood estimation (MLE) yields strongly consistent and asymptotically normal parameter estimates. Finite-sample performance and forecasting accuracy are evaluated through Monte Carlo simulations. The empirical usefulness of the time-varying specification of the proposed TV-PARX model is shown by analyzing the number of new daily COVID-19 infections in Italy and the number of corporate defaults in the US.
    Date: 2022–07
  2. By: Nabil Kahouadji
    Abstract: We introduce twenty four two-parameter families of advanced time series forecasting functions using a new and nonparametric approach. We also introduce the concept of powering and derive nonseasonal and seasonal models with examples in education, sales, finance and economy. We compare the performance of our twenty four models to both Holt--Winters and ARIMA models for both nonseasonal and seasonal times series. We show in particular that our models not only do not require a decomposition of a seasonal time series into trend, seasonal and random components, but leads also to substantially lower sum of absolute error and a higher number of closer forecasts than both Holt--Winters and ARIMA models. Finally, we apply and compare the performance of our twenty four models using five-year stock market data of 467 companies of the S&P500.
    Date: 2022–07
  3. By: Jia Li (Singapore Management University); Peter C. B. Phillips (Cowles Foundation, Yale University, University of Auckland, Singapore Management University, University of Southampton); Shuping Shi (Macquarie University); Jun Yu (Singapore Management University)
    Abstract: This paper explores weak identification issues arising in commonly used models of economic and financial time series. Two highly popular configurations are shown to be asymptotically observationally equivalent: one with long memory and weak autoregressive dynamics, the other with antipersistent shocks and a near-unit autoregressive root. We develop a data-driven semiparametric and identification-robust approach to inference that reveals such ambiguities and documents the prevalence of weak identification in many realized volatility and trading volume series. The identification-robust empirical evidence generally favors long memory dynamics in volatility and volume, a conclusion that is corroborated using social-media news flow data.
    Keywords: Realized volatility; Weak identification; Disjoint confidence sets, Trading volume, Long memory
    JEL: C12 C13 C58
    Date: 2022–06
  4. By: Peter C. B. Phillips (Cowles Foundation, Yale University, University of Auckland, Singapore Management University, University of Southampton)
    Abstract: Limit theory is developed for least squares regression estimation of a model involving time trend polynomials and a moving average error process with a unit root. Models with these features can arise from data manipulation such as overdifferencing and model features such as the presence of multicointegration. The impact of such features on the asymptotic equivalence of least squares and generalized least squares is considered. Problems of rank deficiency that are induced asymptotically by the presence of time polynomials in the regression are also studied, focusing on the impact that singularities have on hypothesis testing using Wald statistics and matrix normalization. The paper is largely pedagogical but contains new results, notational innovations, and procedures for dealing with rank deficiency that are useful in cases of wider applicability.
    Keywords: Asymptotic deficiency, Asymptotic equivalence, Hypothesis testing, Least squares regression, MA unit root, Trend regression, Wald statistic
    JEL: C23
    Date: 2022–05
  5. By: Anton Skrobotov
    Abstract: This review discusses methods of testing for explosive bubbles in time series. A large number of recently developed testing methods under various assumptions about innovation of errors are covered. The review also considers the methods for dating explosive (bubble) regimes. Special attention is devoted to time-varying volatility in the errors. Moreover, the modelling of possible relationships between time series with explosive regimes is discussed.
    Date: 2022–07
  6. By: Hall, Viv B; Thomson, Peter
    Abstract: We investigate whether the boosted HP filter (bHP) proposed by Phillips and Shi (2021) might be preferred for New Zealand trend and growth cycle analysis, relative to using the standard HP filter (HP1600). We do this for a representative range of quarterly macroeconomic time series typically used in small theoretical and empirical macroeconomic models, and address the following questions. Tradition dictates that business cycle periodicities lie between 6 and 32 quarters (e.g. Baxter and King, 1999) (BK). In the context of more recent business cycle durations, should periodicities up to 40 quarters or more now be considered? Phillips and Shi (2021) propose two stopping rules for selecting a bHP trend. Does it matter which is applied? We propose other trend selection criteria based on the cut-off frequency and sharpness of the trend filter. Are stylised business cycle facts from bHP filtering materially different to those produced from HP1600? In particular, does bHP filtering lead to New Zealand growth cycles which are noticeably different from those associated with HP1600 or BK filtering? HP1600 is commonly used as an omnibus filter across all key macroeconomic variables. Does the greater flexibility of bHP filtering provide better alternatives? We conclude that the 6 to 32 quarter business cycle periodicity is sufficient to reflect New Zealand growth cycles and determine stylised business cycle facts and, for our representative 13-variable macroeconomic data set, using a bHP filter (2HP1600) as an omnibus filter is preferable to using the HP1600 filter.
    Keywords: Boosting, Hodrick-Prescott filter, Business cycles, Transfer function sharpness, New Zealand,
    Date: 2022
  7. By: Gregory Benton; Wesley J. Maddox; Andrew Gordon Wilson
    Abstract: A broad class of stochastic volatility models are defined by systems of stochastic differential equations. While these models have seen widespread success in domains such as finance and statistical climatology, they typically lack an ability to condition on historical data to produce a true posterior distribution. To address this fundamental limitation, we show how to re-cast a class of stochastic volatility models as a hierarchical Gaussian process (GP) model with specialized covariance functions. This GP model retains the inductive biases of the stochastic volatility model while providing the posterior predictive distribution given by GP inference. Within this framework, we take inspiration from well studied domains to introduce a new class of models, Volt and Magpie, that significantly outperform baselines in stock and wind speed forecasting, and naturally extend to the multitask setting.
    Date: 2022–07
  8. By: Clements, Adam; Vasnev, Andrey
    Abstract: The Heterogeneous Autoregressive (HAR) model of Corsi (2009) has become the benchmark model for predicting realized volatility given its simplicity and consistent empirical performance. Many modifications and extensions to the original model have been proposed that often only provide incremental forecast improvements. In this paper, we take a step back and view the HAR model as a forecast combination that combines three predictors: previous day realization (or random walk forecast), previous week average, and previous month average. When applying the Ordinary Least Squares (OLS) to combine the predictors, the HAR model uses optimal weights that are known to be problematic in the forecast combination literature. In fact, the simple average forecast often outperforms the optimal combination in many empirical applications. We investigate the performance of the simple average forecast for the realized volatility of the Dow Jones Industrial Average equity index. We find dramatic improvements in forecast accuracy across all horizons and different time periods. This is the first time the forecast combination puzzle is identified in this context.
    Keywords: Realized volatility, forecast combination, HAR model
    JEL: C53 C58
    Date: 2021–02–24
  9. By: Laura Coroneo; Fabrizio Iacone; Fabio Profumo
    Abstract: We apply fixed-b and fixed-m asymptotics to tests of equal predictive accuracy and of encompassing for density forecasts. We verify in an original Monte Carlo design that fixed-smoothing asymptotics delivers correctly sized tests in this framework, even when only a small number of out of sample observations is available. We use the proposed density forecast comparison tests with fixed-smoothing asymptotics to assess the predictive ability of density forecasts from the European Central Bank's Survey of Professional Forecasters (ECB SPF).
    Keywords: density forecast comparison, ECB SPF, Diebold-Mariano test, forecast encompassing, fixed-smoothing asymptotics
    JEL: C12 C22 E17
    Date: 2022–06
  10. By: Peter C. B. Phillips (Cowles Foundation, Yale University, University of Auckland, Singapore Management University, University of Southampton)
    Abstract: T. W. Anderson did pathbreaking work in econometrics during his remarkable career as an eminent statistician. His primary contributions to econometrics are reviewed here, including his early research on estimation and inference in simultaneous equations models and reduced rank regression. Some of his later works that connect in important ways to econometrics are also briefly covered, including limit theory in explosive autoregression, asymptotic expansions, and exact distribution theory for econometric estimators. The research is considered in the light of its influence on subsequent and ongoing developments in econometrics, notably confidence interval construction under weak instruments and inference in mildly explosive regressions.
    Keywords: Asymptotic expansions, Confidence interval construction, Explosive autoregression, LIML, Reduced rank regression, Simultaneous equation models, Weak identification regression, MA unit root, Trend regression, Wald statistic
    JEL: C23
    Date: 2022–06

This nep-ets issue is ©2022 by Jaqueson K. Galimberti. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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