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Quasi Maximum Likelihood Estimation of Vector Multiplicative Error Model using the ECCC-GARCH Representation Journal of Time Series Econometrics Pub Date : 2024-01-03 Yongdeng Xu
We introduce an ECCC-GARCH representation for the vector Multiplicative Error Model (vMEM) that enables maximum likelihood estimation using the multivariate normal distribution. We show via Monte Carlo simulations that the QML estimator possesses desirable small sample properties (towards unbiasedness and efficiency). In the empirical application, we firstly use a two-dimensional vMEM for the squared
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In-Fill Asymptotic Distribution of the Change Point Estimator when Estimating Breaks One at a Time Journal of Time Series Econometrics Pub Date : 2023-03-20 Toshikazu Tayanagi, Eiji Kurozumi
In this study, we investigate the least squares (LS) estimator of a structural change point by the in-fill asymptotic theory, which has been recently used by Jiang, Wang, and Yu (2018. “New Distribution Theory for the Estimation of Structural Break Point in Mean.” Journal of Econometrics 205 (1): 156–76; 2020. “In-Fill Asymptotic Theory for Structural Break Point in Autoregressions.” Econometric Reviews
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Temporally Local Maximum Likelihood with Application to SIS Model Journal of Time Series Econometrics Pub Date : 2023-03-07 Christian Gourieroux, Joann Jasiak
The parametric estimators applied by rolling are commonly used for the analysis of time series with nonlinear patterns, including time varying parameters and local trends. This paper examines the properties of rolling estimators in the class of temporally local maximum likelihood (TLML) estimators. We consider the TLML estimators of (a) constant parameters, (b) stochastic, stationary parameters and
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Simple Factor Realized Stochastic Volatility Models Journal of Time Series Econometrics Pub Date : 2023-02-07 Hiroyuki Kawakatsu
This paper considers the use of multiple noisy daily realized variance measures to extract a denoised latent variance process. The class of stochastic volatility models used for signal extraction has the important feature that they can be written as a linear state space model. As a result, prediction of the denoised latent variance and likelihood evaluation can be carried out efficiently using the
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Realized BEKK-CAW Models Journal of Time Series Econometrics Pub Date : 2022-10-17 Manabu Asai, Mike K. P. So
Estimating time-varying conditional covariance matrices of financial returns play important role in portfolio analysis, risk management, and financial econometrics research. The availability of high-frequency financial data can provide an additional data source for dynamic covariance modeling. In this paper, we propose to use the information of asset return vector and realized covariance measures simultaneously
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Improving the Estimation and Predictions of Small Time Series Models Journal of Time Series Econometrics Pub Date : 2022-04-13 Gareth Liu-Evans
A new approach is developed for improving the point estimation and predictions of parametric time-series models. The method targets performance criteria such as estimation bias, root mean squared error, variance, or prediction error, and produces closed-form estimators focused towards these targets via a computational approximation method. This is done for an autoregression coefficient, for the mean
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Goodness-of-Fit Tests for SPARMA Models with Dependent Error Terms Journal of Time Series Econometrics Pub Date : 2022-02-04 Yacouba Boubacar Maïnassara, Abdoulkarim Ilmi Amir
In this paper we consider tests for lack of fit in a class of seasonal periodic autoregressive moving average (SPARMA) models under the assumption that the errors are uncorrelated but non-independent (i.e. weak SPARMA models). We derive the asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations of these weak SPARMA models. We then deduce the modified
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Estimating SPARMA Models with Dependent Error Terms Journal of Time Series Econometrics Pub Date : 2022-02-03 Yacouba Boubacar Maïnassara, Abdoulkarim Ilmi Amir
We are interested in a class of seasonal autoregressive moving average (SARMA) models with periodically varying parameters, so-called seasonal periodic autoregressive moving average (SPARMA) models under the assumption that the errors are uncorrelated but non-independent (i.e. weak SPARMA models). Relaxing the classical independence assumption on the errors considerably extends the range of application
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Multivariate Hyper-Rotated GARCH-BEKK Journal of Time Series Econometrics Pub Date : 2022-01-07 Manabu Asai, Michael McAleer
For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the ‘curse of dimensionality’. Recently, Laurent, Rombouts and Violante (2014 “Multivariate Rotated ARCH Models” Journal of Econometrics 179: 16–30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized
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The Export of Commodities and the Validity of the Export-Led Growth (ELG) Hypothesis for the Brazilian Economy: An Analysis of the Commodity Boom Period Journal of Time Series Econometrics Pub Date : 2022-01-01 Aniela Fagundes Carrara, Tiago Luiz Pesquero
The present study examines the Brazilian economy in the light of the export-led growth (ELG) hypothesis, in order to examine if this hypothesis is valid for periods in which commodities occupy a significant part of exports, for this reason, for the period known as the “commodity boom”. In order to address the proposed objective, the estimation method used was the autoregression with vector error correction
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Frontmatter Journal of Time Series Econometrics Pub Date : 2022-01-01
Article Frontmatter was published on January 1, 2022 in the journal Journal of Time Series Econometrics (volume 14, issue 1).
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A Robust Test for Monotonicity in Asset Returns Journal of Time Series Econometrics Pub Date : 2022-01-01 Cleiton G. Taufemback, Victor Troster, Muhammad Shahbaz
In this paper, we propose a robust test of monotonicity in asset returns that is valid under a general setting. We develop a test that allows for dependent data and is robust to conditional heteroskedasticity or heavy-tailed distributions of return differentials. Many postulated theories in economics and finance assume monotonic relationships between expected asset returns and certain underlying characteristics
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On a Different way of Understanding the Edge-Effect for the Inference of ARMA-type Processes (in Z d ) Journal of Time Series Econometrics Pub Date : 2022-01-01 Chrysoula Dimitriou-Fakalou
The edge-effect concerning the standard estimators’ bias for the parameters of multi-indexed ARMA-type series is a common hurdle; it is investigated whether an alternative ARMA parameterization might release any unwelcome complication. The theoretical blocks, of when the factorized model is free of the edge-effect, are provided and simulation results are used to reinforce the same views. Estimation
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Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors Journal of Time Series Econometrics Pub Date : 2022-01-01 Alessandra Canepa
Johansen’s (2000. “A Bartlett Correction Factor for Tests of on the Cointegrating Relations.” Econometric Theory 16: 740–78) Bartlett correction factor for the LR test of linear restrictions on cointegrated vectors is derived under the i.i.d. Gaussian assumption for the innovation terms. However, the distribution of most data relating to financial variables is fat-tailed and often skewed; there is
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Estimating Impulse-Response Functions for Macroeconomic Models using Directional Quantiles Journal of Time Series Econometrics Pub Date : 2021-12-16 Gabriel Montes-Rojas
A multivariate vector autoregressive model is used to construct the distribution of the impulse-response functions of macroeconomics shocks. In particular, the paper studies the distribution of the short-, medium-, and long-term effects after a shock. Structural and reduced form quantile vector autoregressive models are developed where heterogeneity in conditional effects can be evaluated through multivariate
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Seasonal Adjustment of Daily Time Series Journal of Time Series Econometrics Pub Date : 2021-04-18 Daniel Ollech
Currently, the methods used by producers of official statistics do not facilitate the seasonal and calendar adjustment of daily time series, even though an increasing number of series with daily observations are available. The aim of this paper is the development of a procedure to estimate and adjust for periodically recurring systematic effects and the influence of moving holidays in time series with
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Variable Selection in Regression Models Using Global Sensitivity Analysis Journal of Time Series Econometrics Pub Date : 2021-03-15 William Becker, Paolo Paruolo, Andrea Saltelli
Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion of model fit, hence defining a ranking of regressors
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Estimation of Continuous and Discrete Time Co-integrated Systems with Stock and Flow Variables Journal of Time Series Econometrics Pub Date : 2021-02-13 Daniel González Olivares, Isai Guizar
This paper proposes an exact discrete time error correction model for co-integrated systems in continuous time and outlines a computationally efficient algorithm that leads to the Gaussian estimates of the model’s parameters. Its performance in estimation is assessed by contrasting our estimates with those obtained after applying Johansen’s discrete time approach to cointegrated systems. The data,
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Frontmatter Journal of Time Series Econometrics Pub Date : 2021-01-01
Article Frontmatter was published on January 1, 2021 in the journal Journal of Time Series Econometrics (volume 13, issue 1).
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The Behavior of Divorce Rates: A Smooth Transition Regression Approach Journal of Time Series Econometrics Pub Date : 2021-01-01 Marko Korhonen, Mikko Puhakka
We explore the behavior of the divorce rates in 17 OECD countries for the period 1960–2010. Many studies have found persistence in divorce rates after changes in divorce laws. We formulate a nonlinear model to explain the behavior of divorce rates, over time, after a large change in divorce rate, including changes in divorce laws. We argue that the stationary smooth transition regression (STR) approach
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Consumption, Aggregate Wealth and Expected Stock Returns: An FCVAR Approach Journal of Time Series Econometrics Pub Date : 2021-01-01 Ricardo Quineche
In their seminal work, Lettau, M., and S. Ludvigson, 2001, “Consumption, Aggregate Wealth, and Expected Stock Returns.” The Journal of Finance 56 (3): 815–49. https://doi.org/10.1111/0022-1082.00347, demonstrated that there exists a long-run relationship between consumption, asset holdings, and labor income. They denoted this relationship as cay and showed it to be quite successful in predicting the
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Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications Journal of Time Series Econometrics Pub Date : 2021-01-01 Manas Tripathi, Saurabh Kumar, Sarveshwar Kumar Inani
This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays an essential role for the economic policy of a country. Due to the floating exchange rate regime, and ever-changing economic conditions, analysts have observed significant volatility in the exchange rates. However, exchange rate forecasting has been a challenging task before the analysts over the years. Various
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Modeling House Price Synchronization across the U.S. States and their Time-Varying Macroeconomic Linkages Journal of Time Series Econometrics Pub Date : 2021-01-01 Hardik A. Marfatia
This paper analyzes the time-varying impact of macroeconomic forces on the synchronization in housing movements across all the U.S. states. Using a Bayesian modeling approach, the house price movements are decomposed into national, regional and state-specific factors. We then analyze the time-varying impact of macroeconomic forces on these national and regional factors. Evidence suggests that in several
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A General Frequency Domain Estimation Method for Gegenbauer Processes Journal of Time Series Econometrics Pub Date : 2020-09-24 Richard Hunt, Shelton Peiris, Neville Weber
In this paper a new method for estimation of all the parameters of a k-factor Gegenbauer process is developed using a broadband nonlinear least-squares regression technique in the frequency-domain, with similarities to a Whittle estimator. Simulation studies where the underlying distribution is symmetric suggest that while the new method may have a slightly lower level of accuracy than existing methods
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A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior Journal of Time Series Econometrics Pub Date : 2020-08-07 Sebastian Ankargren, Måns Unosson, Yukai Yang
Abstract We propose a Bayesian vector autoregressive (VAR) model for mixed-frequency data. Our model is based on the mean-adjusted parametrization of the VAR and allows for an explicit prior on the “steady states” (unconditional means) of the included variables. Based on recent developments in the literature, we discuss extensions of the model that improve the flexibility of the modeling approach.
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Time-varying NoVaS Versus GARCH: Point Prediction, Volatility Estimation and Prediction Intervals Journal of Time Series Econometrics Pub Date : 2020-07-06 Jie Chen, Dimitris N. Politis
Abstract The NoVaS methodology for prediction of stationary financial returns is reviewed, and the applicability of the NoVaS transformation for volatility estimation is illustrated using realized volatility as a proxy. The realm of applicability of the NoVaS methodology is then extended to non-stationary data (involving local stationarity and/or structural breaks) for one-step ahead point prediction
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Bayesian Estimation of the Functional Spatial Lag Model Journal of Time Series Econometrics Pub Date : 2020-06-03 Alassane Aw, Emmanuel Nicolas Cabral
Abstract The spatial lag model (SLM) has been widely studied in the literature for spatialised data modeling in various disciplines such as geography, economics, demography, regional sciences, etc. This is an extension of the classical linear model that takes into account the proximity of spatial units in modeling. In this paper, we propose a Bayesian estimation of the functional spatial lag (FSLM)
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A Comparison of Hurst Exponent Estimators in Long-range Dependent Curve Time Series Journal of Time Series Econometrics Pub Date : 2020-05-26 Han Lin Shang
Abstract The Hurst exponent is the simplest numerical summary of self-similar long-range dependent stochastic processes. We consider the estimation of Hurst exponent in long-range dependent curve time series. Our estimation method begins by constructing an estimate of the long-run covariance function, which we use, via dynamic functional principal component analysis, in estimating the orthonormal functions
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INAR(1) Processes with Inflated-parameter Generalized Power Series Innovations Journal of Time Series Econometrics Pub Date : 2020-05-19 Tito Lívio, Marcelo Bourguignon, Fernando Nascimento
Abstract In this paper, new models are studied by proposing the family of generalized power series distributions with inflated parameter (IGPSD) for the innovation process of the INAR(1) model. The main properties of the process were established, such as mean, variance, autocorrelation and transition probability. The methods of estimation by Yule–Walker and the conditional maximum likelihood were used
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Cointegrated Dynamics for a Generalized Long Memory Process: Application to Interest Rates Journal of Time Series Econometrics Pub Date : 2020-03-07 Manabu Asai, Shelton Peiris, Michael McAleer, David E. Allen
Abstract Recent developments in econometric methods enable estimation and testing of general long memory processes, which include the general Gegenbauer process. This paper considers the error correction model for a vector general long memory process, which encompasses the vector autoregressive fractionally integrated moving average and general Gegenbauer processes. We modify the tests for unit roots
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Penalized Averaging of Parametric and Non-Parametric Quantile Forecasts Journal of Time Series Econometrics Pub Date : 2019-12-18 Jan G. De Gooijer, Dawit Zerom
Abstract We propose a hybrid penalized averaging for combining parametric and non-parametric quantile forecasts when faced with a large number of predictors. This approach goes beyond the usual practice of combining conditional mean forecasts from parametric time series models with only a few predictors. The hybrid methodology adopts the adaptive LASSO regularization to simultaneously reduce predictor
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Checking Model Adequacy for Count Time Series by Using Pearson Residuals Journal of Time Series Econometrics Pub Date : 2019-08-15 Christian Weiß, Lukas Scherer, Boris Aleksandrov, Martin Feld
Abstract After having fitted a model to a given count time series, one has to check the adequacy of this model fit. The (standardized) Pearson residuals, being easy to compute and interpret, are a popular diagnostic approach for this purpose. But which types of model inadequacy might be uncovered by which statistics based on the Pearson residuals? In view of being able to apply such statistics in practice
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Dynamic D-Vine Copula Model with Applications to Value-at-Risk (VaR) Journal of Time Series Econometrics Pub Date : 2019-05-06 Paula V. Tófoli, Flávio A. Ziegelmann, Osvaldo Candido, Pedro L. Valls Pereira
Abstract Vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a restricted ARMA(1, m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate
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Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its Applications Journal of Time Series Econometrics Pub Date : 2019-01-23 Olusegun M. Otunuga, Gangaram S. Ladde, Nathan G. Ladde
Abstract In this work, an attempt is made to apply the Local Lagged Adapted Generalized Method of Moments (LLGMM) to estimate state and parameters in stochastic differential dynamic models. The development of LLGMM is motivated by parameter and state estimation problems in continuous-time nonlinear and non-stationary stochastic dynamic model validation problems in biological, chemical, engineering
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Modelling with Dispersed Bivariate Moving Average Processes Journal of Time Series Econometrics Pub Date : 2019-01-22 Yuvraj Sunecher, Naushad Mamode Khan, Vandna Jowaheer
Abstract This paper proposes a non-stationary bivariate integer-valued moving average of order 1 (BINMA(1)) model where the respective innovations are marginal COM-Poisson and unrelated. As opposed to other such bivariate time series model, the dependence between the series in the above is constructed via the relation between the current series with survivor elements of the other series at the preceding
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Forecasting Volatility Returns of Oil Price Using Gene Expression Programming Approach. Journal of Time Series Econometrics Pub Date : 2019-01-04 Alexander Amo Baffour, Jingchun Feng, Liwei Fan, Beryl Adormaa Buanya
This study employs four (4) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants namely GARCH (1, 1), Glosten–Jagannathan–Runkle (GJR), Auto Regressive Integrated Moving Average (ARIMA)-GARCH and ARIMA-GJR as benchmark models to assess the performance of a proposed novel Gene Expression Programming (GEP) based univariate time series modeling approach used to conduct ex ante oil
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A Neural Network Method for Nonlinear Time Series Analysis Journal of Time Series Econometrics Pub Date : 2018-12-29 Jinu Lee
Abstract This paper is concerned with approximating nonlinear time series by an artificial neural network based on radial basis functions. A new data-driven modelling strategy is suggested for the adaptive framework by combining the statistical techniques of forward selection, cross validation and information criterion. The proposed method is fast and simple to implement while avoiding some typical
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Political Business Cycles in Australia Elections and Party Ideology Journal of Time Series Econometrics Pub Date : 2018-11-20 Bill Kolios
Abstract Party ideology, elections and economic performance can have a significant impact on the overall economic performance. Governments are formed by parties that compete at elections and, based on their ideology, have different preferences regarding the size and scope of government. With respect to economic policy, left-wing parties advocate for government intervention in order to ease the effects
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Risk Analysis of Cumulative Intraday Return Curves Journal of Time Series Econometrics Pub Date : 2018-10-13 Piotr Kokoszka, Hong Miao, Stilian Stoev, Ben Zheng
Abstract Motivated by the risk inherent in intraday investing, we propose several ways of quantifying extremal behavior of a time series of curves. A curve can be extreme if it has shape and/or magnitude much different than the bulk of observed curves. Our approach is at the nexus of functional data analysis and extreme value theory. The risk measures we propose allow us to assess probabilities of
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Finite-Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model Journal of Time Series Econometrics Pub Date : 2018-10-05 Antonis Demos, Dimitra Kyriakopoulou
Abstract We derive the analytical expressions of bias approximations for maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of the EGARCH (1,1) parameters that enable us to correct after the bias of all estimators. The bias-correction mechanism is constructed under the specification of two methods that are analytically described. We also evaluate the residual bootstrapped estimator
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Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation Journal of Time Series Econometrics Pub Date : 2018-07-21 David Ardia, Keven Bluteau, Lennart F. Hoogerheide
Abstract Numerical standard error (NSE) is an estimate of the standard deviation of a simulation result if the simulation experiment were to be repeated many times. We review standard methods for computing NSE and perform a Monte Carlo experiments to compare their performance in the case of high/extreme autocorrelation. In particular, we propose an application to risk management where we assess the
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What Proportion of Time is a Particular Market Inefficient? … A Method for Analysing the Frequency of Market Efficiency when Equity Prices Follow Threshold Autoregressions Journal of Time Series Econometrics Pub Date : 2018-07-20 Muhammad Farid Ahmed, Stephen Satchell
Abstract We assume that equity returns follow multi-state threshold autoregressions and generalize existing results for threshold autoregressive models presented in Knight and Satchell 2011. “Some new results for threshold AR(1) models,” Journal of Time Series Econometrics 3(2011):1–42 and Knight, Satchell, and Srivastava (2014) for the existence of a stationary process and the conditions necessary
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Sequential Testing with Uniformly Distributed Size Journal of Time Series Econometrics Pub Date : 2018-02-06 Stanislav Anatolyev, Grigory Kosenok
Abstract Sequential procedures for the testing for structural stability do not provide enough guidance on the shape of boundaries that are used to decide on acceptance or rejection, requiring only that the overall size of the test is asymptotically controlled. We introduce and motivate a reasonable criterion for the shape of boundaries which requires that the test size be uniformly distributed over
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A Flexible Observation-Driven Stationary Bivariate Negative Binomial INAR(1) with Non-homogeneous Levels of Over-dispersion Journal of Time Series Econometrics Pub Date : 2017-12-06 Naushad Mamode Khan, Yuvraj Sunecher, Vandna Jowaheer
Abstract The existing bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with negative binomial (NB) innovations is developed under stationary moment conditions and in particular under same level of over-dispersion index. In this paper, we propose a flexible BINAR(1) under NB innovations where the counting series are subject to two different levels of over-dispersion under same stationary
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The Chow-Lin method extended to dynamic models with autocorrelated residuals Journal of Time Series Econometrics Pub Date : 2017-08-26 Aurélien Poissonnier
Abstract I provide a closed-form solution to temporal disaggregation or interpolation models which is both general in terms of dynamic structure of the model (lags of the high-frequency variable) and flexible in terms of autocorrelation of its residual. As for static models, I show that assuming autocorrelated residuals in dynamic models is practically convenient. To illustrate the potential of the
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A Generalized ARFIMA Model with Smooth Transition Fractional Integration Parameter Journal of Time Series Econometrics Pub Date : 2017-07-19 Heni Boubaker
Abstract This paper proposes a model of time-varying fractional integration where the long-memory parameter, d $d$ , in an ARFIMA model is allowed to depend on t $t$ and evolve according to a Smooth Transition Regressive (STR) model advanced by Teräsvirta (1994, 1998) . To estimate the time-varying fractional integration parameter, we suggest a new multi-step estimation method based on the wavelet
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Volatility Modeling with Leverage Effect under Laplace Errors Journal of Time Series Econometrics Pub Date : 2017-07-18 Zhengjun Jiang, Weixuan Xia
Abstract This paper discusses four GARCH-type models (A-GARCH, NA-GARCH, GJR-GARCH, and E-GARCH) in representing volatility of financial returns with leverage effect. In these models, errors are assumed to follow a Laplace distribution in order to deal with the typical leptokurtic feature of financial returns. The properties of these models are analyzed theoretically in terms of unconditional variance
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On Trend Breaks and Initial Condition in Unit Root Testing Journal of Time Series Econometrics Pub Date : 2017-07-18 Anton Skrobotov
Abstract Recent approaches in unit root testing have taken into account the influences of initial conditions and data trend breaks via pre-testing and union of rejection testing strategies. This paper reviews existing methods, extends the methods of (Harvey, D. I., S. J. Leybourne, and A. M. R. Taylor. 2012b. “Unit Root Testing under a Local Break in Trend.” Journal of Econometrics 167:140–167), and
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Signal Extraction for Nonstationary Time Series with Diverse Sampling Rules Journal of Time Series Econometrics Pub Date : 2017-01-01 Thomas Trimbur, Tucker McElroy
This paper presents a flexible framework for signal extraction of time series measured as stock or flow at diverse sampling frequencies. Our approach allows for a coherent treatment of series across diverse sampling rules, a deeper understanding of the main properties of signal estimators and the role of measurement, and a straightforward method for signal estimation and interpolation for discrete
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Testing for Nonlinearity in Conditional Covariances Journal of Time Series Econometrics Pub Date : 2017-01-01 Bilel Sanhaji
Abstract We propose two Lagrange multiplier tests for nonlinearity in conditional covariances in multivariate GARCH models. The null hypothesis is the scalar BEKK model in which covolatilities of time series are driven by a linear function of their own lags and lagged squared innovations. The alternative hypothesis is an extension of the model in which covolatilities are modeled by a nonlinear function
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Do They Still Matter? – Impact of Fossil Fuels on Electricity Prices in the Light of Increased Renewable Generation Journal of Time Series Econometrics Pub Date : 2017-01-01 Johannes Lips
Abstract During the last years, the German energy sector and especially its electricity market was affected by a major energy transition, the so called „Energiewende“. This transition led to an increase of electricity production from renewable sources and thereby affected the whole electricity market. Therefore, it provides lessons for countries, which are only beginning a similar transition away from
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The Impact of the Initial Condition on Covariate Augmented Unit Root Tests Journal of Time Series Econometrics Pub Date : 2017-01-01 Chrystalleni Aristidou, David I. Harvey, Stephen J. Leybourne
Abstract We examine the behaviour of OLS-demeaned/detrended and GLS-demeaned/detrended unit root tests that employ stationary covariates, as proposed by Hansen (1995, “Rethinking the Univariate Approach to Unit Root Testing.” Econometric Theory 11:1148–71) and Elliott and Jansson (2003, “Testing for Unit Roots with Stationary Covariates.” Journal of Econometrics 115:75–89), respectively, in situations
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Testing for a Change in Mean under Fractional Integration Journal of Time Series Econometrics Pub Date : 2017-01-01 Fabrizio Iacone, Stephen J. Leybourne, A.M. Robert Taylor
Abstract We consider testing for the presence of a change in mean, at an unknown point in the sample, in data that are possibly fractionally integrated, and of unknown order. This testing problem has recently been considered in a number of papers, most notably Shao (2011, “A Simple Test of Changes in Mean in the Possible Presence of Long-Range Dependence.” Journal of Time Series Analysis 32:598–606)
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Size corrected Significance Tests in Seemingly Unrelated Regressions with Autocorrelated Errors Journal of Time Series Econometrics Pub Date : 2017-01-01 Spyridon D. Symeonides, Yiannis Karavias, Elias Tzavalis
Abstract Refined asymptotic methods are used to produce degrees-of-freedom- adjusted Edgeworth and Cornish-Fisher size corrections of the t and F testing procedures for the parameters of a S.U.R. model with serially correlated errors. The corrected tests follow the Student-t and F distributions, respectively, with an approximation error of order Oτ3$$O\left({{\tau ^3}} \right)$$, where τ=1/T$$\tau
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Analyzing the Full BINMA Time Series Process Using a Robust GQL Approach Journal of Time Series Econometrics Pub Date : 2016-01-06 Naushad Mamode Khan, Yuvraj Sunecher, Vandna Jowaheer
We investigate a new bivariate-integer valued moving average time series process where the innovation series follow the bivariate Poisson assumption under stationary moments and constant cross-correlations. Furthermore, due to the complication involved in specifying the joint likelihood function, this paper considers a robust generalized quasi-likelihood approach to estimate the mean, serial and dependence
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Tail Behavior and Dependence Structure in the APARCH Model Journal of Time Series Econometrics Pub Date : 2016-01-03 Farrukh Javed, Krzysztof Podgórski
Abstract The APARCH model attempts to capture asymmetric responses of volatility to positive and negative ‘news shocks’ – the phenomenon known as the leverage effect. Despite its potential, the model’s properties have not yet been fully investigated. While the capacity to account for the leverage is clear from the defining structure, little is known how the effect is quantified in terms of the model’s
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Optimal Real-Time Filters for Linear Prediction Problems Journal of Time Series Econometrics Pub Date : 2016-01-01 Marc Wildi, Tucker McElroy
Abstract The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the data-generating process into an uncorrelated white noise process. By design, this universal decomposition is indifferent to particular features of a specific prediction problem (e. g., forecasting or signal extraction) – or features driven by the priorities of the data-users. A single optimization
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Fixed and Recursive Right-Tailed Dickey–Fuller Tests in the Presence of a Break under the Null Journal of Time Series Econometrics Pub Date : 2016-01-01 Robert Sollis
Abstract Right-tailed Dickey–Fuller-type unit root tests against the explosive alternative have become popular in economics and finance for detecting asset price bubbles. This paper studies the size properties of fixed sample and recursive right-tailed Dickey–Fuller tests if the relevant series contains a unit root, but a structural break in the drift parameter occurs. It is shown that positive size
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A Note on the QMLE Limit Theory in the Non-stationary ARCH(1) Model Journal of Time Series Econometrics Pub Date : 2016-01-01 Stelios Arvanitis, Alexandros Louka
In this note we extend the standard results for the limit theory of the popular quasi-maximum likelihood estimator (QMLE) in the context of the nonstationary autoregressive conditional heteroskedastic ARCH(1) model by allowing the innovation process not to possess fourth moments. Depending on the value of the index of stability, we either derive α-stable weak limits with nonstandard rates or inconsistency
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On the Univariate Representation of BEKK Models with Common Factors Journal of Time Series Econometrics Pub Date : 2016-01-01 Alain Hecq, Sébastien Laurent, Franz C. Palm
Abstract Simple low order multivariate GARCH models imply marginal processes with a lot of persistence in the form of high order lags. This is not what we find in many situations however, where parsimonious univariate GARCH(1,1) models for instance describe quite well the conditional volatility of some asset returns. In order to explain this paradox, we show that in the presence of common GARCH factors