nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒09‒12
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Power of unit root tests against nonlinear and noncausal alternatives By Frédérique Bec; Alain Guay; Heino Bohn Nielsen; Sarra Saïdi
  2. A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series By Webel, Karsten
  3. The Econometrics of Financial Duration Modeling By Giuseppe Cavaliere; Thomas Mikosch; Anders Rahbek; Frederik Vilandt
  4. A Hawkes model with CARMA(p,q) intensity By Lorenzo Mercuri; Andrea Perchiazzo; Edit Rroji
  5. Skewed SVARs: tracking the structural sources of macroeconomic tail risks By Carlos Montes-Galdón; Eva Ortega

  1. By: Frédérique Bec; Alain Guay; Heino Bohn Nielsen; Sarra Saïdi (Université de Cergy-Pontoise, THEMA)
    Abstract: The increasing sophistication of economic and financial time series modelling creates a need for a test of the time dependence structure of the series which does not require a proper specification of the alternative. Indeed, the latter is unknown beforehand. Yet, the stationarity has to be established before proceeding to the estimation and testing of causal/noncausal or linear/nonlinear models as their econometric theory has been developed under the maintained assumption of stationarity. In this paper, we propose a new unit root test statistics which is both asymptotically consistent against all stationary alternatives and still keeps good power properties in finite sample. A large simulation study is performed to assess the power of our test compared to existing unit root tests built specifically for various kinds of stationary alternatives, when the true DGP is either causal or noncausal, linear or nonlinear stationary. Based on various sample sizes and degrees of persistence, it turns out that our new test performs very well in terms of power in finite sample, no matter the alternative under consideration.
    Keywords: Unit root test, Threshold autoregressive model, Noncausal model.
    JEL: C12 C22 C32
    Date: 2022
  2. By: Webel, Karsten
    Abstract: Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal adjustment. However, traditional methods in official statistics often fail to model and seasonally adjust them appropriately mainly since data of such temporal granularity exhibit stylised facts that are not observable in monthly and quarterly data. Prime examples include irregular spacing, coexistence of multiple seasonal patterns with integer versus non-integer seasonal periodicities and potential interactions as well as small sample issues, such as missing values and a high sensitivity to outliers. We provide an overview of recent modelling and seasonal adjustment approaches that are capable of handling these distinctive features, or at least some of them. Hourly counts of TARGET2 customer transactions and daily realised electricity consumption in Germany are discussed for illustrative purposes.
    Keywords: official statistics,seasonality,signal extraction,time series decomposition,unobserved components
    JEL: C01 C02 C14 C18 C22 C40 C50
    Date: 2022
  3. By: Giuseppe Cavaliere; Thomas Mikosch; Anders Rahbek; Frederik Vilandt
    Abstract: We discuss estimation and inference in financial durations models. For the classical autoregressive conditional duration (ACD) models by Engle and Russell (1998, Econometrica 66, 1127-1162), we show the surprising result that the large sample behavior of likelihood estimators depends on the tail behavior of the durations. Even under stationarity, asymptotic normality breaks down for tail indices smaller than one. Instead, estimators are mixed Gaussian with non-standard rates of convergence. We exploit here the crucial fact that for duration data the number of observations within any time span is random. Our results apply to general econometric models where the number of observed events is random.
    Date: 2022–08
  4. By: Lorenzo Mercuri; Andrea Perchiazzo; Edit Rroji
    Abstract: In this paper we introduce a new model named CARMA(p,q)-Hawkes process as the Hawkes model with exponential kernel implies a strictly decreasing behaviour of the autocorrelation function and empirically evidences reject the monotonicity assumption on the autocorrelation function. The proposed model is a Hawkes process where the intensity follows a Continuous Time Autoregressive Moving Average (CARMA) process and specifically is able to reproduce more realistic dependence structures. We also study the conditions of stationarity and positivity for the intensity and the strong mixing property for the increments. Furthermore we compute the likelihood, present a simulation method and discuss an estimation method based on the autocorrelation function. A simulation and estimation exercise highlights the main features of the CARMA(p,q)-Hawkes.
    Date: 2022–08
  5. By: Carlos Montes-Galdón (European Central Bank); Eva Ortega (Banco de España)
    Abstract: This paper proposes a vector autoregressive model with structural shocks (SVAR) that are identified using sign restrictions and whose distribution is subject to time-varying skewness. It also presents an efficient Bayesian algorithm to estimate the model. The model allows for the joint tracking of asymmetric risks to macroeconomic variables included in the SVAR. It also provides a narrative about the structural reasons for the changes over time in those risks. Using euro area data, our estimation suggests that there has been a significant variation in the skewness of demand, supply and monetary policy shocks between 1999 and 2019. This variation lies behind a significant proportion of the joint dynamics of real GDP growth and inflation in the euro area over this period, and also generates important asymmetric tail risks in these macroeconomic variables. Finally, compared to the literature on growth- and inflation-at-risk, we found that financial stress indicators do not suffice to explain all the macroeconomic tail risks.
    Keywords: Bayesian SVAR, skewness, growth-at-risk, inflation-at-risk
    JEL: C11 C32 C51 E31 E32
    Date: 2022–03

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