nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒04‒16
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Assessing Gamma kernels and BSS/LSS processes By Ole E. Barndorff-Nielsen
  2. Assessing Identifying Restrictions in SVAR Models By Michele Piffer
  3. Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series By Matteo Barigozzi; Marc Hallin
  4. Non-Stationary Dynamic Factor Models for Large Datasets By Barigozzi, Matteo; Lippi, Marco; Luciani, Matteo
  5. Copula–based Specification of vector MEMs By Fabrizio Cipollini; Robert F. Engle; Giampiero M. Gallo
  6. Bayesian Compressed Vector Autoregressions By Gary Koop; Dimitris Korobilis; Davide Pettenuzzo
  7. Bias Correction Methods for Dynamic Panel Data Models with Fixed Effects By Abonazel, Mohamed R.
  8. Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances By Leopoldo Catania; Anna Gloria Billé

  1. By: Ole E. Barndorff-Nielsen (Aarhus University, THIELE Center and CREATES)
    Abstract: This paper reviews the roles of gamma type kernels in the theory and modelling for Brownian and Lévy semistationary processes. Applications to financial econometrics and the physics of turbulence are pointed out.
    Keywords: Ambit Stochastics; autocorrelation functions; Brownian semistationary processes; financial econometrics; fractional differentiation; identification; Levy semistationary processes; path properties; turbulence modelling; volatility/intermittency.
    JEL: C1 C5
    Date: 2016–04–05
  2. By: Michele Piffer
    Abstract: This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a prior distribution that covers the parameter space both where the restrictions are satisfied and where they are not satisfied. I show that the data determine whether the probability mass in favour of the restrictions increases or not from prior to posterior. Using two applications, I find support for the restrictions used by Baumeister & Hamilton (2015a) in their two-equation model of labor demand and supply, and I find support for the true data generating process in a simulation exercise on the New Keynesian model.
    Keywords: Identification, Bayesian econometrics, sign restrictions
    JEL: C32 C11
    Date: 2016
  3. By: Matteo Barigozzi; Marc Hallin
    Abstract: In this paper, we define weighted directed networks for large panels of financial time series wherethe edges and the associated weights are reflecting the dynamic conditional correlation structureof the panel. Those networks produce a most informative picture of the interconnections amongthe various series in the panel. In particular, we are combining this network-based analysis and ageneral dynamic factor decomposition in a study of the volatilities of the stocks of the Standard&Poor’s 100 index over the period 2000-2013. This approach allows us to decompose the panelinto two components which represent the two main sources of variation of financial time series:common or market shocks, and the stock-specific or idiosyncratic ones. While the common components,driven by market shocks, are related to the non-diversifiable or systematic components ofrisk, the idiosyncratic components show important interdependencies which are nicely describedthrough network structures. Those networks shed some light on the contagion phenomenons associatedwith financial crises, and help assessing how systemic a given firm is likely to be. We showhow to estimate them by combining dynamic principal components and sparse VAR techniques.The results provide evidence of high positive intra-sectoral and lower, but nevertheless quite important,negative inter-sectoral, dependencies, the Energy and Financials sectors being the mostinterconnected ones. In particular, the Financials stocks appear to be the most central vertices inthe network, making them the main source of contagion.
    Keywords: Time Series; Dynamic Factor Models; Network Analysis; Volatility; Systemic Risk
    Date: 2015–10
  4. By: Barigozzi, Matteo; Lippi, Marco; Luciani, Matteo
    Abstract: We develop the econometric theory for Non-Stationary Dynamic Factor models for large panels of time series, with a particular focus on building estimators of impulse response functions to unexpected macroeconomic shocks. We derive conditions for consistent estimation of the model as both the cross-sectional size, n, and the time dimension, T, go to infinity, and whether or not cointegration is imposed. We also propose a new estimator for the non-stationary common factors, as well as an information criterion to determine the number of common trends. Finally, the numerical properties of our estimator are explored by means of a MonteCarlo exercise and of a real-data application, in which we study the effects of monetary policy and supply shocks on the US economy.
    Keywords: Dynamic Factor model ; , common trends ; impulse response functions ; unit root processes
    JEL: C00 C01 E00
    Date: 2016–03–04
  5. By: Fabrizio Cipollini (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Robert F. Engle (Department of Finance, Stern School of Business, New York University); Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze)
    Abstract: The Multiplicative Error Model (Engle (2002)) for nonnegative valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with nonnegative support. A multivariate extension allows for the innovations to be contemporaneously correlated. We overcome the lack of sufficiently flexible probability density functions for such processes by suggesting a copula function approach to estimate the parameters of the scale factors and of the correlations of the innovation processes. We illustrate this vector MEM with an application to the interactions between realized volatility, volume and the number of trades. We show that significantly superior realized volatility forecasts are delivered in the presence of other trading activity indicators and contemporaneous correlations.
    Keywords: GARCH; MEM; Realized Volatility; Trading Volume; Trading Activity; Copula; Volatility Forecasting.
    JEL: C32 C58 C53 G12
    Date: 2016–04
  6. By: Gary Koop; Dimitris Korobilis; Davide Pettenuzzo
    Keywords: multivariate time series, random projection, forecasting
    JEL: C11 C32 C53
    Date: 2016–03
  7. By: Abonazel, Mohamed R.
    Abstract: This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally, we display new estimators that presented by Youssef and Abonazel (2015) as more efficient estimators than the conventional estimators.
    Keywords: Bias-corrected estimators; First-order autoregressive panel model; Generalized method of moments estimators; Kantorovich inequality; Least squares dummy variable estimators.
    JEL: C01 C1 C23 C4 C5 C87
    Date: 2016–04–11
  8. By: Leopoldo Catania (DEF, University of Rome "Tor Vergata"); Anna Gloria Billé (DEF, University of Rome "Tor Vergata")
    Abstract: We propose a new class of models specifically tailored for spatio{temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series econometrics. In particular, we allow for time{varying spatial autoregressive coefficients as well as time{varying regressor coefficients and cross{sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite sample properties of the Maximum Likelihood estimator for the new class of models as well as its exibility in explaining several dynamic spatial dependence processes. The new proposed class of models are found to be economically preferred by rational investors through an application in portfolio optimization.
    Keywords: SARAR, time varying parameters, spatio{temporal data, score driven models
    Date: 2016–03–31

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