nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒08‒21
nine papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. Periodic Integration and Seasonal Unit Roots By del Barrio Castro, Tomás; Osborn, Denise R.
  2. "Johansen Test with Fourier-Type Smooth Nonlinear Trends in Cointegrating Relations" By Takamitsu Kurita; Mototsugu Shintani
  3. Stationarity with Occasionally Binding Constraints By James A. Duffy; Sophocles Mavroeidis; Sam Wycherley
  4. Noise reduction for functional time series By Cees Diks; Bram Wouters
  5. Gaussian semiparametric estimation Gaussian semiparametric estimation of two-dimensional intrinsically stationary random fields By Yoshihiro Yajima; Yasumasa Matsuda
  6. "Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant Unconditional Variance in Heavy-Tailed Time Series". By Josep Lluís Carrion-i-Silvestre; Andreu Sansó
  7. Random Subspace Local Projections By Viet Hoang Dinh; Didier Nibbering; Benjamin Wong
  8. Robust Impulse Responses using External Instruments: the Role of Information By Davide Brignone; Alessandro Franconi; Marco Mazzali
  9. Information-Theoretic Time-Varying Density Modeling By Bram van Os

  1. By: del Barrio Castro, Tomás; Osborn, Denise R.
    Abstract: Seasonality is pervasive across a wide range of economic time series and it substantially complicates the analysis of unit root non-stationarity in such series. This paper reviews recent contributions to the literature on non-stationary seasonal processes, focussing on periodically integrated (P I) and seasonally integrated (SI) processes. Whereas an SI process captures seasonal non-stationarity essentially through an annual lag, a P I process has (a restricted form of) seasonally-varying autoregressive coefficients. The fundamental properties of both types of process are compared, noting in particular that a simple SI process observed S times a year has S unit roots, in contrast to the single unit root of a P I process. Indeed, for S > 2 and even (such as processes observed quarterly or monthly), an SI process has a pair of complex-valued unit roots at each seasonal frequency except the Nyquist frequency, where a single real root applies. Consequently, recent literature concerned with testing the unit roots implied by SI processes employs complex-valued unit root processes, and these are discussed in some detail. A key feature of the discussion is to show how the demodulator operator can be used to convert a unit root process at a seasonal frequency to a conventional zero-frequency unit root process, thereby enabling the well-known properties of the latter to be exploited. Further, circulant matrices are introduced and it is shown how they are employed in theoretical analyses to capture the repetitive nature of seasonal processes. Discriminating between SI and P I processes requires care, since testing for unit roots at seasonal frequencies may lead to a P I process (erroneously) appearing to have an SI form, while an application to monthly US industrial production series illustrates how these types of seasonal non-stationarity can be distinguished in practice. Although univariate processes are discussed, the methods considered in the paper can be used to analyze cointegration, including cointegration across different frequencies
    Keywords: Periodic Integration, Seasonal Integration, Vector of Seasons, Circulant Matrices, Demodulator Operator, Industrial Production.
    JEL: C32
    Date: 2023
  2. By: Takamitsu Kurita (Faculty of Economics, Kyoto Sangyo University); Mototsugu Shintani (Faculty of Economics, The University of Tokyo)
    Abstract: We develop methodology for testing cointegrating rank in vector autoregressive (VAR) models in the presence of Fourier-type smooth nonlinear deterministic trends in cointegrating relations. The limiting distribution of log-likelihood ratio test statistics is derived and approximated limit quantiles are tabulated. A sequential procedure to select cointegrating rank is evaluated by Monte Carlo simulations. Our empirical application to economic data also demonstrates the usefulness of the proposed methodology in a practical context.
    Date: 2023–06
  3. By: James A. Duffy; Sophocles Mavroeidis; Sam Wycherley
    Abstract: This paper studies a class of multivariate threshold autoregressive models, known as censored and kinked structural vector autoregressions (CKSVAR), which are notably able to accommodate series that are subject to occasionally binding constraints. We develop a set of sufficient conditions for the processes generated by a CKSVAR to be stationary, ergodic, and weakly dependent. Our conditions relate directly to the stability of the deterministic part of the model, and are therefore less conservative than those typically available for general vector threshold autoregressive (VTAR) models. Though our criteria refer to quantities, such as refinements of the joint spectral radius, that cannot feasibly be computed exactly, they can be approximated numerically to a high degree of precision. Our results also permit us to provide a treatment of unit roots and cointegration in the CKSVAR, for the case where the model is configured so as to generate linear cointegration.
    Date: 2023–07
  4. By: Cees Diks; Bram Wouters
    Abstract: A novel method for noise reduction in the setting of curve time series with error contamination is proposed, based on extending the framework of functional principal component analysis (FPCA). We employ the underlying, finite-dimensional dynamics of the functional time series to separate the serially dependent dynamical part of the observed curves from the noise. Upon identifying the subspaces of the signal and idiosyncratic components, we construct a projection of the observed curve time series along the noise subspace, resulting in an estimate of the underlying denoised curves. This projection is optimal in the sense that it minimizes the mean integrated squared error. By applying our method to similated and real data, we show the denoising estimator is consistent and outperforms existing denoising techniques. Furthermore, we show it can be used as a pre-processing step to improve forecasting.
    Date: 2023–07
  5. By: Yoshihiro Yajima; Yasumasa Matsuda
    Abstract: We consider Gaussian semiparametric estimation (GSE) for two-dimensional intrinsically stationary random fields (ISRFs) observed on a regular grid and derive its asymptotic properties. Originally GSE was proposed to estimate long memory time series models in a semiparametric way either for stationary or nonstationary cases. We try an extension of GSE for time series to anisotropic ISRFs observed on two dimensional lattice that include isotropic fractional Brownian fields (FBF) as special cases, which have been employed to describe many physical spatial behaviours. The GSE extended to ISRFs is consistent and has a limiting normal distribution with variance independent of any unknown parameters as sample size goes to infinity, under conditions we specify in this paper. We conduct a computational simulation to compare the performances of it with those of an alternative estimator on the spatial domain.
    Date: 2023–07
  6. By: Josep Lluís Carrion-i-Silvestre (AQR-IREA Research Group. Departament d’Econometria, Estadística i Economia Aplicada. Universitat de Barcelona. Av. Diagonal, 690. 08034 Barcelona. Spain.); Andreu Sansó (Department d’Economia Aplicada. Universitat de les Illes Balears and MOTIBO Research Group, Balearic Islands Health Research Institute (Idisba).)
    Abstract: This paper focuses on testing the stability of the unconditional variance when the stochastic processes may have heavy-tailed distributions. Finite sample distributions that depend both on the effective sample size and the tail index are approximated using Extreme Value distributions and summarized using response surfaces. A modification of the Iterative Cumulative Sum of Squares (ICSS) algorithm to detect the presence of multiple structural breaks is suggested, adapting the algorithm to the tail index of the underlying distribution of the process. We apply the algorithm to eighty absolute log-exchange rate returns, finding evidence of (i) infinite variance in about a third of the cases, (ii) finite changing unconditional variance for another third of the time series - totalling about one hundred structural breaks - and (iii) finite constant unconditional variance for the remaining third of the time series.
    Keywords: CUMSUMQ test, Unconditional variance, Multiple structural changes, Heavy tails, Generalized Extreme Value distribution. JEL classification: C12, C22.
    Date: 2023–07
  7. By: Viet Hoang Dinh; Didier Nibbering; Benjamin Wong
    Abstract: We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response function in a Monte Carlo exercise where we simulate data from a real business cycle model with fiscal foresight. (ii) Our results suggest that random subspace methods are more accurate than factor models if the underlying large data set has a factor structure similar to typical macroeconomic data sets such as FRED-MD. (iii) Our approach leads to differences in the estimated impulse response functions relative to standard methods when applied to two widely-studied empirical applications.
    Keywords: Local Projections, Random Subspace, Impulse Response Functions, Large Data Sets
    JEL: C22 E32
    Date: 2023–07
  8. By: Davide Brignone; Alessandro Franconi; Marco Mazzali
    Abstract: External-instrument identification leads to biased responses when the shock is not invertible and the measurement error is present. We propose to use this identification strategy in a structural Dynamic Factor Model, which we call Proxy DFM. In a simulation analysis, we show that the Proxy DFM always successfully retrieves the true impulse responses, while the Proxy SVAR systematically fails to do so when the model is either misspecified, does not include all relevant information, or the measurement error is present. In an application to US monetary policy, the Proxy DFM shows that a tightening shock is unequivocally contractionary, with deteriorations in domestic demand, labor, credit, housing, exchange, and financial markets. This holds true for all raw instruments available in the literature. The variance decomposition analysis highlights the importance of monetary policy shocks in explaining economic fluctuations, albeit at different horizons.
    Date: 2023–07
  9. By: Bram van Os (Erasmus University Rotterdam)
    Abstract: We present a comprehensive framework for constructing dynamic density models by combining optimization with concepts from information theory. Specifically, we propose to recursively update a time-varying conditional density by maximizing the log-likelihood contribution of the latest observation subject to a Kullback-Leibler divergence (KLD) regularization centered at the one-step ahead predicted density. The resulting Relative Entropy Adaptive Density (READY) update has attractive optimality properties, is reparametrization invariant and can be viewed as an intuitive regularized estimator of the pseudo-true density. Popular existing models, such as the ARMA(1, 1) and GARCH(1, 1), can be retrieved as special cases. Furthermore, we show that standard score-driven models with inverse Fisher scaling can be derived as convenient local approximations of the READY update. Empirical usefulness is illustrated by the modeling of employment growth and asset volatility.
    Date: 2023–06–29

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