nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒05‒15
nine papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series By Degui Li; Runze Li; Han Lin Shang
  2. Artificial neural networks and time series of counts: A class of nonlinear INGARCH models By Malte Jahn
  3. Have the Effects of Shocks to Oil Price Expectations Changed? Evidence from Heteroskedastic Proxy Vector Autoregressions By Martin Bruns; Helmut Luetkepohl
  4. A boosted HP filter for business cycle analysis: evidence from New Zealand’s small open economy By Hall, Viv B.; Thomson, Peter
  5. Testing Granger Non-Causality in Expectiles By Taoufik Bouezmarni; Mohamed Doukali; Abderrahim Taamouti
  6. Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification By Florian Huber; Massimiliano Marcellino
  7. Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions By Thomas Wong; Mauricio Barahona
  8. On the relationship between Markov Switching inference and Fuzzy Clustering: A Monte Carlo evidence By L. Scaffidi Domianello; E. Otranto
  9. Generalized Automatic Least Squares: Efficiency Gains from Misspecified Heteroscedasticity Models By Bulat Gafarov

  1. By: Degui Li; Runze Li; Han Lin Shang
    Abstract: In this paper, we consider detecting and estimating breaks in heterogeneous mean functions of high-dimensional functional time series which are allowed to be cross-sectionally correlated and temporally dependent. A new test statistic combining the functional CUSUM statistic and power enhancement component is proposed with asymptotic null distribution theory comparable to the conventional CUSUM theory derived for a single functional time series. In particular, the extra power enhancement component enlarges the region where the proposed test has power, and results in stable power performance when breaks are sparse in the alternative hypothesis. Furthermore, we impose a latent group structure on the subjects with heterogeneous break points and introduce an easy-to-implement clustering algorithm with an information criterion to consistently estimate the unknown group number and membership. The estimated group structure can subsequently improve the convergence property of the post-clustering break point estimate. Monte-Carlo simulation studies and empirical applications show that the proposed estimation and testing techniques have satisfactory performance in finite samples.
    Date: 2023–04
  2. By: Malte Jahn
    Abstract: Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.
    Date: 2023–04
  3. By: Martin Bruns (School of Economics, University of East Anglia); Helmut Luetkepohl (DIW Berlin and Freie Universitaet Berlin)
    Abstract: Studies of the crude oil market based on structural vector autoregressive (VAR) models typically assume a time-invariant model and transmission of shocks or they consider a time-varying model and shock transmission. We assume a heteroskedastic reduced form VAR model with time invariant slope coefficients and test for time varying impulse responses in a model for the global crude oil market that includes key macroeconomic variables. We find evidence for changes in the transmission of shocks to oil price expectations during the last decades which can be attributed to heteroskedasticity.
    Keywords: Structural vector autoregression, heteroskedastic VAR, proxy VAR, crude oil market
    JEL: C32
    Date: 2023–04
  4. 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
  5. By: Taoufik Bouezmarni (Universite de Sherbrooke); Mohamed Doukali (School of Economics, University of East Anglia); Abderrahim Taamouti (University of Liverpool)
    Abstract: This paper aims to derive a consistent test of Granger causality at a given expectile. We also propose a sup Wald test for jointly testing Granger causality at all expectiles that has the correct asymptotic size and power properties. Expectiles have the advantage of capturing similar information as quantiles, but they also have the merit of being much more straightforward to use than quantiles, since they are defined as least squares analogue of quantiles. Studying Granger causality in expectiles is practically simpler and allows us to examine the causality at all levels of the conditional distribution. Moreover, testing Granger causality at all expectiles provides a su¢ cient condition for testing Granger causality in distribution. A Monte Carlo simulation study reveals that our tests have good finite-sample size and power properties for a variety of data-generating processes and di¤erent sample sizes. Finally, we provide two empirical applications to illustrate the usefulness of the proposed tests.
    Keywords: Granger causality in expectiles, Granger causality in distribution, expectile regression function, asymmetric loss function, sup-Wald test.
    JEL: C12 C22
    Date: 2023–04
  6. By: Florian Huber; Massimiliano Marcellino
    Abstract: Model mis-specification in multivariate econometric models can strongly influence quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be mis-specified along important but unknown dimensions. Coupled with a conjugate prior, this results in a computationally simple model. As opposed to more flexible specifications, our approach avoids overfitting, is simple to implement and estimation is fast. The resulting cBVAR performs well in simulations for several types of mis-specification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs, and lead to milder but more persistent negative effects of uncertainty shocks on output.
    Date: 2023–04
  7. By: Thomas Wong; Mauricio Barahona
    Abstract: We apply different feature engineering methods for time-series to US market price data. The predictive power of models are tested against Numerai-Signals targets.
    Date: 2023–03
  8. By: L. Scaffidi Domianello; E. Otranto
    Abstract: Markov Switching models have had increasing success in time series analysis due to their ability to capture the existence of unobserved discrete states in the dynamics of the variables under study. This result is generally obtained thanks to the inference on states derived from the so–called Hamilton filter. One of the open problems in this framework is the identification of the number of states, generally fixed a priori; it is in fact impossible to apply classical tests due to the problem of the nuisance parameters present only under the alternative hypothesis. In this work we show, by Monte Carlo simulations, that fuzzy clustering is able to reproduce the parametric state inference derived from the Hamilton filter and that the typical indices used in clustering to determine the number of groups can be used to determine the number of states in this framework. The procedure is very simple to apply, considering that it is performed (in a nonparametric way) independently of the data generation process and that the indicators we use are present in most statistical packages.
    Keywords: Simulations;Number of states;Nuisance parameters;Markov chains;Groups identification
    Date: 2023
  9. By: Bulat Gafarov
    Abstract: It is well known that in the presence of heteroscedasticity ordinary least squares estimator is not efficient. I propose a generalized automatic least squares estimator (GALS) that makes partial correction of heteroscedasticity based on a (potentially) misspecified model without a pretest. Such an estimator is guaranteed to be at least as efficient as either OLS or WLS but can provide some asymptotic efficiency gains over OLS if the misspecified model is approximately correct. If the heteroscedasticity model is correct, the proposed estimator achieves full asymptotic efficiency. The idea is to frame moment conditions corresponding to OLS and WLS squares based on miss-specified heteroscedasticity as a joint generalized method of moments estimation problem. The resulting optimal GMM estimator is equivalent to a feasible GLS with estimated weight matrix. I also propose an optimal GMM variance-covariance estimator for GALS to account for any remaining heteroscedasticity in the residuals.
    Date: 2023–04

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