nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒09‒05
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Financial Market Modeling with Quantum Neural Networks By Carlos Pedro Gon\c{c}alves
  2. Time-dependent scaling patterns in high frequency financial data By Noemi Nava; Tiziana Di Matteo; Tomaso Aste
  3. Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions By Teixeira de Carvalho Guillén, Osmani; Hecq, Alain; Victor Issler, João; Saraiva, Diogo
  4. Factor structural time series models for official statistics with an application to hours worked in Germany By Weigand, Roland; Wanger, Susanne; Zapf, Ines
  5. When is Nonfundamentalness in VARs a Real Problem? An Application to News Shocks By Paul Beaudry; Patrick Fève; Alain Guay; Franck Portier
  6. Testing for unit roots with cointegrated data By Reed, W. Robert

  1. By: Carlos Pedro Gon\c{c}alves
    Abstract: Econophysics has developed as a research field that applies the formalism of Statistical Mechanics and Quantum Mechanics to address Economics and Finance problems. The branch of Econophysics that applies of Quantum Theory to Economics and Finance is called Quantum Econophysics. In Finance, Quantum Econophysics' contributions have ranged from option pricing to market dynamics modeling, behavioral finance and applications of Game Theory, integrating the empirical finding, from human decision analysis, that shows that nonlinear update rules in probabilities, leading to non-additive decision weights, can be computationally approached from quantum computation, with resulting quantum interference terms explaining the non-additive probabilities. The current work draws on these results to introduce new tools from Quantum Artificial Intelligence, namely Quantum Artificial Neural Networks as a way to build and simulate financial market models with adaptive selection of trading rules, leading to turbulence and excess kurtosis in the returns distributions for a wide range of parameters.
    Date: 2015–08
  2. By: Noemi Nava; Tiziana Di Matteo; Tomaso Aste
    Abstract: We measure the influence of different time-scales on the dynamics of financial market data. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures: 1) an amplitude scaling exponent and 2) an entropy like measure. We apply these measures to intra-day, 30-second sampled prices of various stock indices. Our results reveal intra-day trends where different time-horizons contribute with variable relative amplitudes over the course of the trading day. Our findings indicate that the time series we analysed have a non-stationary multi-fractional nature with predominantly persistent behaviour at the middle of the trading session and anti-persistent behaviour at the open and close. We demonstrate that these deviations are statistically significant and robust.
    Date: 2015–08
  3. By: Teixeira de Carvalho Guillén, Osmani; Hecq, Alain; Victor Issler, João; Saraiva, Diogo
    Abstract: Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.
    Date: 2015–02–26
  4. By: Weigand, Roland (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]); Wanger, Susanne (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]); Zapf, Ines (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])
    Abstract: "We introduce a high-dimensional structural time series model, where co-movement between the components is due to common factors. A two-step estimation strategy is presented, which is based on principal components in differences in a first step and state space methods in a second step. The methods add to the toolbox of official statisticians, constructing timely regular statistics from different data sources. In this context, we discuss typical measurement features such as survey errors, statistical breaks, different sampling frequencies and irregular observation patterns, and describe their statistical treatment. The methods are applied to the estimation of paid and unpaid overtime work as well as flows on working-time accounts in Germany, which enter the statistics on hours worked in the national accounts." (Author's abstract, IAB-Doku) ((en))
    Keywords: IAB-Arbeitszeitrechnung - Methode, Arbeitszeit, Arbeitsvolumen, Zeitreihenanalyse, Schätzung, Methodenliteratur, Überstunden, Arbeitszeitkonto
    JEL: C14 C32 C51 C53 C58
    Date: 2015–08–13
  5. By: Paul Beaudry; Patrick Fève; Alain Guay; Franck Portier
    Abstract: When the VAR representation of a times series has a non-fundamental representation, standard SVAR techniques cannot be used to exactly identify the effects of structural shocks. This problem is know to potentially arise when one of the structural shocks represents news about the future. However, as we shall show, in many case the non-fundamental representation of a time series may be very close to its fundamental representation implying that standard SVAR techniques may provide a very good approximation of the effects of structural shocks even when the non-fundamentalness is formally present. This leads to the question: When is non-fundamentalness a real problem? In this paper we derive and illustrate a diagnostic based on a $R^2$ which provides a simple means of detecting whether non-fundamentalness is likely to be a quantitatively important problem in an applied settings. We use the identification of technological news shocks in US data as our running example.
    JEL: E3
    Date: 2015–08
  6. By: Reed, W. Robert
    Abstract: This paper demonstrates that unit root tests can suffer from inflated Type I error rates when data are cointegrated. Results from Monte Carlo simulations show that three commonly used unit root tests - the ADF, Phillips-Perron, and DF-GLS tests - frequently overreject the true null of a unit root for at least one of the cointegrated variables. The reason for this overrejection is that unit root tests, designed for random walk data, are often misspecified when data are cointegrated. While the addition of lagged differenced (LD) terms can eliminate the size distortion, this "success" is spurious, driven by collinearity between the lagged dependent variable and the LD explanatory variables. Accordingly, standard diagnostics such as (i) testing for serial correlation in the residuals and (ii) using information criteria to select among different lag specifications are futile. The implication of these results is that researchers should be conservative in the weight they attach to individual unit root tests when determining whether data are cointegrated.
    Keywords: unit root testing,cointegration,DF-GLS test,augmented Dickey-Fuller test,Phillips-Perron test,simulation
    JEL: C32 C22 C18
    Date: 2015

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