nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒09‒26
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Measuring multiscaling in financial time-series By Riccardo Junior Buonocore; Tomaso Aste; Tiziana Di Matteo
  2. A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series By Gautier Marti; Philippe Very; Philippe Donnat; Frank Nielsen
  3. Adding Flexibility to Markov Switching Models By E. Otranto
  4. Can Univariate Time Series Models of Inflation Help Discriminate Between Alternative Sources of Inflation Persistence By Naveen Srinivasan; Pankaj Kumar
  5. Low-Frequency Econometrics By Ulrich K. Müller; Mark W. Watson
  6. Inequality Constrained State Space Models By Qian, Hang
  7. Selection of an estimation window in the presence of data revisions and recent structural breaks By Hännikäinen, Jari
  8. A Comparative Study of Stock Price Forecasting using nonlinear models By Lawrence Xaba; Ntebogang Moroke; Johnson Arkaah; Charlemagne Pooe
  9. Modeling Dependency and Conditional Volatility between Asian Economic Community (AEC) Country Exchange Rate and Inflation Using the Copula-GARCH Model. By KANTA TANNIYOM; Paponpat Taveeapiradeecharoen; Prapatchon Jariyapan
  10. Interconnections between Eurozone and US booms and busts using a Bayesian Panel Markov-Switching VAR mode By Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk
  11. Long memory through marginalization of large systems and hidden cross-section dependence By Chevillon G.; Hecq A.W.; Laurent S.F.J.A.

  1. By: Riccardo Junior Buonocore; Tomaso Aste; Tiziana Di Matteo
    Abstract: We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analysing the multi/uni-scaling behaviour of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range [2,5]. We discuss the right aggregation horizon to mitigate this bias.
    Date: 2015–09
  2. By: Gautier Marti; Philippe Very; Philippe Donnat; Frank Nielsen
    Abstract: We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights by providing through the data perturbations we propose a multi-view of the assets' clustering behaviour. The perturbation framework is illustrated on an extensive credit default swap time series database available online at
    Date: 2015–09
  3. By: E. Otranto
    Abstract: Very often time series are subject to abrupt changes in the level, which are generally represented by Markov Switching (MS) models, hypothesizing that the level is constant within a certain state (regime). This is not a realistic framework because in the same regime the level could change with minor jumps with respect to a change of state; this is a typical situation in many economic time series, such as the Gross Domestic Product or the volatility of financial markets. We propose to make the state flexible, introducing a very general model which provides oscillations of the level of the time series within each state of the MS model; these movements are driven by a forcing variable. The flexibility of the model allows for consideration of extreme jumps in a parsimonious way (also in the simplest 2-state case), without the adoption of a larger number of regimes; moreover this model increases the interpretability and fitting of the data with respect to the analogous MS model. This approach can be applied in several fields, also using unobservable data. We show its advantages in three distinct applications, involving macroeconomic variables, volatilities of financial markets and conditional correlations.
    Keywords: abrupt changes, goodness of fit, Hamilton filter, smoothed changes, time–varying parameters
    JEL: C22 C32 C5
    Date: 2015
  4. By: Naveen Srinivasan (Madras School of Economics); Pankaj Kumar (Indira Gandhi Institute of Development Research and Reserve Bank of India)
    Abstract: When it comes to measuring inflation persistence, a common practice in empirical research is to estimate univariate autoregressive moving average (ARMA) time series models and measure persistence as the sum of the estimated AR coefficients. We examine four potential sources of lag dynamics in inflation: the evolution of policymakers willingness to stabilize output, shifts in the mean inflation rate, imperfect credibility and learning and unemployment persistence. We show that the reduced-form solution for inflation in all these models have an ARMA(p,q) representation. By implication estimating a reduced-form for inflation will not be able to distinguish among these alternative hypotheses. We illustrate this using US and UK data.
    Keywords: Inflation Persistence; Identification; Kalman Filter
    JEL: E31 E52 E58
    Date: 2015–05
  5. By: Ulrich K. Müller; Mark W. Watson
    Abstract: Many questions in economics involve long-run or trend variation and covariation in time series. Yet, time series of typical lengths contain only limited information about this long-run variation. This paper suggests that long-run sample information can be isolated using a small number of low-frequency trigonometric weighted averages, which in turn can be used to conduct inference about long-run variability and covariability. Because the low-frequency weighted averages have large sample normal distributions, large sample valid inference can often be conducted using familiar small sample normal inference procedures. Moreover, the general approach is applicable for a wide range of persistent stochastic processes that go beyond the familiar I(0) and I(1) models.
    JEL: C12 C22 C32
    Date: 2015–09
  6. By: Qian, Hang
    Abstract: The standard Kalman filter cannot handle inequality constraints imposed on the state variables, as state truncation induces a non-linear and non-Gaussian model. We propose a Rao-Blackwellised particle filter with the optimal importance function for forward filtering and the likelihood function evaluation. The particle filter effectively enforces the state constraints when the Kalman filter violates them. We find substantial Monte Carlo variance reduction by using the optimal importance function and Rao-Blackwellisation, in which the Gaussian linear sub-structure is exploited at both the cross-sectional and temporal levels.
    Keywords: Rao-Blackwellisation, Kalman filter, Particle filter, Sequential Monte Carlo
    JEL: C32 C53
    Date: 2015–09–03
  7. By: Hännikäinen, Jari
    Abstract: In this paper, we analyze the forecasting performance of a set of widely used window selection methods in the presence of data revisions and recent structural breaks. Our Monte Carlo and empirical results for U.S. real GDP and inflation show that the expanding window estimator often yields the most accurate forecasts after a recent break. It performs well regardless of whether the revisions are news or noise, or whether we forecast first-release or final values. We find that the differences in the forecasting accuracy are large in practice, especially when we forecast inflation after the break of the early 1980s.
    Keywords: Recent structural break, choice of estimation window, forecasting, real-time data
    JEL: C22 C53 C82
    Date: 2015–09–18
  8. By: Lawrence Xaba (North West University); Ntebogang Moroke (North West University); Johnson Arkaah (North West University); Charlemagne Pooe (South African Reserve Bank)
    Abstract: This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Data used was daily close stock prices of five banks in the South African banking sector and was obtained from the Johannesburg Stock Exchange (JSE). It covered the period from 2010 to 2012 with a total of 563 observations. Nonlinearity and nonstationarity tests used confirmed the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.
    Keywords: Stock price, nonlinear time series models, error metrics
    JEL: C10 C32 E32
  9. By: KANTA TANNIYOM (Chiang Mai Rajabhat University); Paponpat Taveeapiradeecharoen (Chiang Mai University); Prapatchon Jariyapan (Chiang Mai University)
    Abstract: Structural dependence and conditional volatility are solutions to comprehend financial crisis behavior. Investigation has been widely analyzed especially to what circumstances occurred in EURO zone countries. This leads many economic researchers attention to prepare uncertainty beyond relationship and variation. This paper aims at estimating the dependency and conditional volatilities the growth rate of AEC exchange rate and inflation of Thailand using COPULA-GARCH models. The motivation of this journal is to reach the most rational policy for BANK of Thailand, since exchange rate is one among tangible strategies. Both margins are distributed by skewed-t, and ARMA-GARCH is fitted to monthly data. Growth rate of those variables residual independence are checked by bivariate random dependence which is represented by P-Value and for Marginal Persistence Volatilities will be tested by using Dynamic Conditional Correlation Method, Fifteen static copulas are applied to those dependencies. AIC, SIC and Kendall’s tau will be an appropriate approach to assess results. Empirical results show huge coefficients of correlations between AEC exchange rates and Thailand inflation in the short-term period and slightly correlated in the long-term period of conditional volatility and dependency. In addition, there is evidence to convince that it was a positive relationship.
    Keywords: Copula; Conditional Valatility; ARMA-GARCH; Exchange Rate; Dynamic Conditional Correlation; Bivariate Independent Test
    JEL: C01 C50 C58
  10. By: Monica Billio (University Ca’ Foscari of Venice; Italy); Roberto Casarin (University Ca’ Foscari of Venice; Italy); Francesco Ravazzolo (BI Norwegian Business School, and Norges Bank, Norway); Herman K. van Dijk (Erasmus University Rotterdam, the Netherlands)
    Abstract: Interconnections between Eurozone and United States booms and busts and among major Eurozone economies are analyzed using a Panel Markov-Switching VAR model. The model accommodates changes in low and high data frequencies and incorporates endogenous time-varying transition matrices of country-specific Markov chains. These country-specific Markov chains depend on their own past history and the history of other chains, thus allowing for interconnections between cycles, and an endogenous common Eurozone cycle is derived by aggregating the country-specific cycles. The model is estimated using a simulation based Bayesian approach in which an efficient multi-move sampling algorithm is defined to draw time-varying Markov-switching chains. Using industrial production growth and credit spread data for all countries, several empirical results have emerged. Recession, slow gro wth and expansion are empirically identified as three regimes with slow growth becoming persistent in the Eurozone in recent years different from the US. The Eurozone and the US regimes appear not fully synchronized, with evidence of more recessions in the Eurozone. Second, turning point analysis indicates larger synchronization at the beginning of the Great Financial Crisis: this shock affects the US first, leading the Eurozone cycle, and spreads then rapidly among these economies. Third, amplification effects influence recession probabilities for Eurozone countries when shocks occur. The evidence is different for the US where this reinforcement does not exist. In recent years there are more imbalances among regimes in Eurozone countries. Fourth, a credit shock results in substantial negative industrial production growth for several months in Germany, Spain and the US.
    Keywords: Bayesian Modelling; Panel VAR; Markov-switching; International Business Cycles; Interaction mechanisms
    JEL: C11 C15 C53 E37
    Date: 2015–09–15
  11. By: Chevillon G.; Hecq A.W.; Laurent S.F.J.A. (GSBE)
    Abstract: This paper shows that large dimensional vector autoregressive VAR models of finite order can generate long memory in the marginalized univariate series. We derive high-level assumptions under which the final equation representation of a VAR1 leads to univariate fractional white noises and verify the validity of these assumptions for two specific models. We consider the implications of our findings for the variances of asset returns where the so-called golden-rule of realized variances states that they tend always to exhibit fractional integration of a degree close to 04.
    Keywords: Econometric and Statistical Methods and Methodology: General; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Financial Econometrics;
    JEL: C10 C32 C58
    Date: 2015

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