nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒02‒14
six papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Bayesian Testing Of Granger Causality In Functional Time Series By Rituparna Sen; Anandamayee Majumdar; Shubhangi Sikaria
  2. A New Test on Asset Return Predictability with Structural Breaks By Zongwu Cai; Seong Yeon Chang
  3. Boosting the Forecasting Power of Conditional Heteroskedasticity Models to Account for Covid-19 Outbreaks By Massimo Guidolin; Davide La Cara; Massimiliano Marcellino
  4. Economic analysis using higher frequency time series: Challenges for seasonal adjustment By Ollech, Daniel
  5. Dynamic Factor Model for Functional Time Series: Identification, Estimation, and Prediction By Sven Otto; Nazarii Salish
  6. Unit root tests: Common pitfalls and best practices By Traoré, Fousseini; Diop, Insa

  1. By: Rituparna Sen; Anandamayee Majumdar; Shubhangi Sikaria
    Abstract: We develop a multivariate functional autoregressive model (MFAR), which captures the cross-correlation among multiple functional time series and thus improves forecast accuracy. We estimate the parameters under the Bayesian dynamic linear models (DLM) framework. In order to capture Granger causality from one FAR series to another we employ Bayes Factor. Motivated by the broad application of functional data in finance, we investigate the causality between the yield curves of two countries. Furthermore, we illustrate a climatology example, examining whether the weather conditions Granger cause pollutant daily levels in a city.
    Date: 2021–12
  2. By: Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Seong Yeon Chang (Department of Economics, Soongsil University, Seoul 06978, Korea)
    Abstract: This paper considers predictive regressions in which a structural break is allowed on an unknown date. We establish novel testing procedures for asset return predictability using empirical likelihood methods based on weighted-score equations. The theoretical results are useful in practice because our unified framework does not require distinguishing whether the predictor variables are stationary or nonstationary. Simulations show that the empirical likelihood-based tests perform well in terms of size and power in finite samples. As an empirical analysis, we test asset returns predictability using various predictor variables.
    Keywords: Autoregressive process; Empirical likelihood; Structural break; Unit root; Weighted estimation
    JEL: C12 C14 C32 G12
    Date: 2022–02
  3. By: Massimo Guidolin; Davide La Cara; Massimiliano Marcellino
    Abstract: With reference to S&P 500 daily returns, we report evidence of an in-sample predictive accuracy breakdown for realized variance by GARCH models in correspondence to the March 2020 Covid-19 outbreak. However, a variety of macroeconomic risk, political and social media sentiment uncertainty factors, and crucially a few variables capturing the evolution of the Covid-19 pandemics, successfully predict the direction and size of GARCH forecast errors between November 2019 and June 2020. Predictors related to diagnosed cases, their rate of growth, and the progression of the curve of deceased, infected people in the United States are featured prominently. We test a number of “augmented” GARCH models to include the most precisely estimated exogenous variables and find that they offer precise forecasts in samples that include the Covid-19 outbreak. In genuine out-of-sample tests, augmenting GARCH with Covid-19 related exogenous variables increases the percentage of days in which the direction of change in realized variance is correctly predicted.
    Keywords: Conditionally heteroskedastic models, Covid-19, volatility forecasting
    JEL: C32 C53 E47 G01
    Date: 2021
  4. By: Ollech, Daniel
    Abstract: The COVID-19 pandemic has increased the need for timely and granular information to assess the state of the economy in real time. Weekly and daily indices have been constructed using higher frequency data to address this need. Yet the seasonal and calendar adjustment of the underlying time series is challenging. Here, we analyse the features and idiosyncracies of such time series relevant in the context of seasonal adjustment. Drawing on a set of time series for Germany - namely hourly electricity consumption, the daily truck toll mileage, and weekly Google Trends data - used in many countries to assess economic development during the pandemic, we discuss obstacles, difficulties, and adjustment options. Furthermore, we develop a taxonomy of the central features of seasonal higher frequency time series.
    Keywords: COVID-19,DSA,Calendar adjustment,Time series characteristics
    JEL: C14 C22 C87 E66
    Date: 2021
  5. By: Sven Otto; Nazarii Salish
    Abstract: A functional dynamic factor model for time-dependent functional data is proposed. We decompose a functional time series into a predictive low-dimensional common component consisting of a finite number of factors and an infinite-dimensional idiosyncratic component that has no predictive power. The conditions under which all model parameters, including the number of factors, become identifiable are discussed. Our identification results lead to a simple-to-use two-stage estimation procedure based on functional principal components. As part of our estimation procedure, we solve the separation problem between the common and idiosyncratic functional components. In particular, we obtain a consistent information criterion that provides joint estimates of the number of factors and dynamic lags of the common component. Finally, we illustrate the applicability of our method in a simulation study and to the problem of modeling and predicting yield curves. In an out-of-sample experiment, we demonstrate that our model performs well compared to the widely used term structure Nelson-Siegel model for yield curves.
    Date: 2022–01
  6. By: Traoré, Fousseini; Diop, Insa
    Abstract: Since the seminal paper by Granger and Newbold (1974) on spurious regressions, applied econometricians have become aware of the consequences of unit roots in empirical analysis with time series data. Yet one can still find many published papers with unit root tests implemented in an inappropriate way. The objective of this Technical Note is to highlight the common pitfalls and best practices when testing for unit roots. In addition to the theoretical discussion, we provide examples using price data from Kenya, Mali, Togo, and South Africa to illustrate the procedures we think are worth following.
    Keywords: KENYA; MALI; TOGO; SOUTH AFRICA; AFRICA; AFRICA SOUTH OF SAHARA; CENTRAL AFRICA; EAST AFRICA; NORTH AFRICA; SOUTHERN AFRICA; WEST AFRICA; econometrics; parity; approaches; best practices; macroeconomics; tests; models; unit root; stationary tests
    Date: 2021

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