nep-ecm New Economics Papers
on Econometrics
Issue of 2014‒03‒01
twelve papers chosen by
Sune Karlsson
Orebro University

  1. Linearity and Misspecification Tests for Vector Smooth Transition Regression Models By Timo Teräsvirta; Yukai Yang
  2. A Monte Carlo Study of a Factor Analytical Method for Fixed-Effects Dynamic Panel Models By Norkute, Milda
  3. Fractional Cointegration Rank Estimation By Katarzyna Lasak; Carlos Velasco
  4. Buffered autoregressive models with conditional heteroscedasticity: An application to exchange rates By Zhu, Ke; Li, Wai Keung; Yu, Philip L.H.
  5. The Estimation of Multi-dimensional Fixed Effects Panel Data Models By László Balázsi; László Mátyás; Tom Wansbeek
  6. A New Spread Estimator By Michael Bleaney; Zhiyong Li
  7. Evaluating Specification Tests in the Context of Value-Added Estimation By Guarino, Cassandra; Reckase, Mark D.; Stacy, Brian; Wooldridge, Jeffrey M.
  8. An Application of Principal Component Analysis on Multivariate Time-Stationary Spatio-Temporal Data By Wolfgang Karl Härdle; Helmut Thome; ;
  9. Data-based priors for vector autoregressions with drifting coefficients By Korobilis, Dimitris
  10. Large deviation asymptotics for the left tail of the sum of dependent positive random variables By Peter Tankov
  11. Nowcasting and forecasting economic growth in the euro area using principal components By Irma Hindrayanto; Siem Jan Koopman; Jasper de Winter
  12. How to measure the quality of financial tweets By Paola Cerchiello; Paolo Giudici

  1. By: Timo Teräsvirta (Aarhus University and CREATES); Yukai Yang (CORE, Université catholique de Louvain and CREATES)
    Abstract: In this paper, we derive Lagrange multiplier and Lagrange multiplier type specification and misspecification tests for vector smooth transition models. We report results from simulation studies in which the size and power properties of the proposed tests in small samples are considered. The results show that these asymptotic tests generally suffer from size distortion. We find thatWilks’s lambda and Rao’s F statistic both have satisfactory size properties and can be recommended for empirical use. Bootstrapping the standard asymptotic LM statistic offers another solution to the problem. JEL Classification: C12, C32, C52.
    Keywords: Vector STAR models, Linearity test, Misspecification test, Vector nonlinear time series, Serial correlation, Parameter constancy, Residual nonlinearity test
    Date: 2014–06–02
  2. By: Norkute, Milda (Department of Economics, Lund University)
    Abstract: In a recent article Bai (Fixed-Effects Dynamic Panel Models, A Factor Analytical Method. Econometrica 81, 285-314, 2013a) proposes a new factor analytical method (FAM) for the estimation of fixed-effects dynamic panel data models, which has the unique and very useful property that it is asymptotically bias free. In this paper we provide Monte Carlo evidence of the good small-sample performance of FAM, that complement Bai's theoretical study.
    Keywords: Dynamic panel data models; Heteroscedasticity; Monte Carlo simulations
    JEL: C13 C33
    Date: 2014–02–18
  3. By: Katarzyna Lasak (VU University Amsterdam, the Netherlands); Carlos Velasco (Universidad Carlos III de Madrid, Spain)
    Abstract: We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists in estimating the parameters of the model under the null hypothesis of the cointegration rank r=1,2,…,p-1. This step provides consistent estimates of the order of fractional cointegration, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup-likelihood ratio test of no-cointegration on the estimated p-r common trends that are not cointegrated under the null. The order of fractional cointegration is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step to adapt to the representation of the common trends estimated in the first step. The critical values of the proposed tests depend only on the number of common trends under the null, p-r, and on the interval of the orders of fractional cointegration b allowed in the estimation, but not on the order of fractional cointegration of already identified relationships. Hence this reduces the set of simulations required to approximate the critical values, making this procedure convenient for practical purposes. In a Monte Carlo study we analyze the finite sample properties of our procedure and compare with alternative methods. We finally apply these methods to study the term structure of interest rates.
    Keywords: Error correction model; Gaussian VAR model; Likelihood ratio tests; Maximum likelihood estimation
    JEL: C12 C15 C32
    Date: 2014–02–13
  4. By: Zhu, Ke; Li, Wai Keung; Yu, Philip L.H.
    Abstract: This paper introduces a new model called the buffered autoregressive model with generalized autoregressive conditional heteroskedasticity (BAR-GARCH). The proposed model, as an extension of the BAR model in Li et al. (2013), can capture the buffering phenomenon of time series in both conditional mean and conditional variance. Thus, it provides us a new way to study the nonlinearity of a time series. Compared with the existing AR-GARCH and threshold AR-GARCH models, an application to several exchange rates highlights an interesting interpretation of the buffer zone determined by the fitted BAR-GARCH models.
    Keywords: Buffered AR model; Buffered AR-GARCH model; Exchange rate; GARCH model; Nonlinear time series; Threshold AR model.
    JEL: C1 C51 C52 C58 G1
    Date: 2014–02–22
  5. By: László Balázsi; László Mátyás; Tom Wansbeek
    Abstract: The paper introduces for the most frequently used three-dimensional fixed effects panel data models the appropriate Within estimators. It analyzes the behaviour of these estimators in the case of no-self-flow data, unbalanced data and dynamic autoregressive models. Then the main results are generalized for higher dimensional panel data sets as well.
    Date: 2014–02–10
  6. By: Michael Bleaney; Zhiyong Li
    Abstract: A new estimator of bid-ask spreads is presented. When the trade direction is known, any estimate of the spread is associated with a unique series of conjectural mid-prices derived by adjusting the observed transaction price by half the estimated spread. It is shown that the covariance of successive conjectural midprice returns is maximised (or least negative) when the estimated spread is equal to the true spread. A search procedure to maximise this covariance may therefore be used to estimate the true spread. The performance of this estimator under various conditions is examined both theoretically and with Monte Carlo simulations. The simulations confirm the theoretical results. The performance of the estimator is good.
    Keywords: Bid-ask Spread, Feedback Trading, Estimation JEL codes: G10
    Date: 2014–02
  7. By: Guarino, Cassandra (Indiana University); Reckase, Mark D. (Michigan State University); Stacy, Brian (Michigan State University); Wooldridge, Jeffrey M. (Michigan State University)
    Abstract: We study the properties of two specification tests that have been applied to a variety of estimators in the context of value-added measures (VAMs) of teacher and school quality: the Hausman test for choosing between random and fixed effects and a test for feedback (sometimes called a "falsification test"). We discuss theoretical properties of the tests to serve as background. An extensive simulation study provides important further insight to the VAM setting. Unfortunately, while both the Hausman and feedback tests have good power for detecting the kinds of nonrandom assignment that can invalidate VAM estimates, they also reject in situations where estimated VAMs perform very well. Consequently, the tests must be used with caution when student tracking is used to form classrooms.
    Keywords: teacher labor markets, teacher quality, value-added
    JEL: C01 I20 J45 J01
    Date: 2014–02
  8. By: Wolfgang Karl Härdle; Helmut Thome; ;
    Abstract: Principal component analysis denotes a popular algorithmic technique to dimension reduction and factor extraction. Spatial variants have been proposed to account for the particularities of spatial data, namely spatial heterogeneity and spatial autocorrelation, and we present a novel approach which transfers principal component analysis into the spatio-temporal realm. Our approach, named stPCA, allows for dimension reduction in the attribute space while striving to preserve much of the data's variance and maintaining the data's original structure in the spatio-temporal domain. Additionally to spatial autocorrelation stPCA exploits any serial correlation present in the data and consequently takes advantage of all particular features of spatial-temporal data. A simulation study underlines the superior performance of stPCA if compared to the original PCA or its spatial variants and an application on indicators of economic deprivation and urbanism demonstrates its suitability for practical use.
    Keywords: PCA, spatio-temporal analysis, dimension reduction, factor extraction, economic deprivation, urbanism
    JEL: C31 C33 R11
    Date: 2014–02
  9. By: Korobilis, Dimitris
    Abstract: This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.
    Keywords: TVP-VAR, shrinkage, data-based prior, forecasting
    JEL: C11 C22 C32 C52 C53 C63 E17 E58
    Date: 2014–01
  10. By: Peter Tankov
    Abstract: We study the left tail behavior of the logarithm of the distribution function of a sum of dependent positive random variables. Asymptotics are computed under the assumption that the marginal distribution functions decay slowly at zero, meaning that the their logarithms are slowly varying functions. This includes parametric families such as log-normal, gamma, Weibull and many distributions from the financial mathematics literature. We show that the logarithmic asymptotics of the sum in question depend on a characteristic of the copula of the random variables which we term weak lower tail dependence function, and which is computed explicitly for several families of copulas in this paper. In applications, our results may be used to quantify the diversification of long-only portfolios of financial assets with respect to extreme losses. As an illustration, we compute the left tail asymptotics for a portfolio of options in the multidimensional Black-Scholes model.
    Date: 2014–02
  11. By: Irma Hindrayanto; Siem Jan Koopman; Jasper de Winter
    Abstract: Many empirical studies show that factor models have a relatively high forecast compared to alternative short-term forecasting models. These empirical findings have been established for different data sets and for different forecast horizons. However, choosing the appropriate factor model specification is still a topic of ongoing debate. Moreover, the forecast performance during the recent financial crisis is not well documented. In this study we investigate these two issues in depth. We empirically test the forecast performance of three factor model approaches and report our findings in an extended empirical out-of-sample forecasting competition for the euro area and its five largest countries over the period 1992-2012. Besides, we introduce two extensions to the existing factor models to make them more suited for real-time forecasting. We show that the factor models were able to systematically beat the benchmark autoregressive model, both before as well as during the financial crisis. The recently proposed collapsed dynamic factor model shows the highest forecast accuracy for the euro area and the majority of countries we analyzed. The improvement against the benchmark model can range up to 77%, depending on the country and forecast horizon.
    Keywords: Factor models; Principal component analysis; Forecasting, Kalman filter; State space method; Publication lag; Mixed frequency
    Date: 2014–01
  12. By: Paola Cerchiello (Department of Economics and Management, University of Pavia); Paolo Giudici (Department of Economics and Management, University of Pavia)
    Abstract: Twitter text data may be very useful to predict financial tangibles, such as share prices, as well as intangible assets, such as company reputation. While twitter data are becoming widely available to researchers, methods aimed at selecting which twitter data are reliable are, to our knowledge, not yet available. To overcome this problem, and allow to employ twitter data for nowcasting and forecasting purposes, in this contribution we propose an effective statistical method that formalises and extends a quality index employed in the context of the evaluation of academic research: the h-index. Our proposal will be tested on a list of twitterers described by the Financial Times as "the top financial tweeters to follow", for the year 2013. Using our methodology we rank these twitterers and provide confidence intervals to decide whether they are significantly different.
    Date: 2014–02

This nep-ecm issue is ©2014 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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