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Econometrics
| Edited by: | Sune Karlsson |
| Orebro University | |
| Issue date: | 2014-06-14 |
| Papers: | 11 |
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Contents.
- Low-dimensional decomposition, smoothing and forecasting of sparse functional data
Date: 2014 By: Alexander Dokumentov
Rob J HyndmanURL: http://d.repec.org/n?u=RePEc:msh:ebswps:2014-16&r=ecm
Keywords: Tikhonov regularisation, Smoothing, Forecasting, Ridge regression, PCA, LASSO, Maximum-margin matrix factorisation, Mortality rates, Sparse longitudinal data JEL: C10 C14 C33 - On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests
Date: 2014-01-15 By: Eric Ghysels
J. Isaac Miller (Department of Economics, University of Missouri-Columbia)URL: http://d.repec.org/n?u=RePEc:umc:wpaper:1403&r=ecm
Keywords: linear interpolation, cointegration, trace test, residual-based cointegration tests JEL: C12 C32 - On Forecast Evaluation
Date: 2014-06-06 By: Wilmer Osvaldo Martínez-Rivera
Manuel Dario Hernández-Bejarano
Juan Manuel Julio-RománURL: http://d.repec.org/n?u=RePEc:col:000094:011604&r=ecm
Keywords: Forecast evaluation, Stochastic order, Multiple comparison. JEL: C53 C12 C14 - Inextricability of Autonomy and Confluence in Econometrics
Date: 2014-06 By: Duo Qin (Department of Economics, SOAS, University of London, UK) URL: http://d.repec.org/n?u=RePEc:soa:wpaper:189&r=ecm
Keywords: exogeneity, structural invariance, omitted variable bias, multicollinearity, model selection and design JEL: B23 C13 C18 C50 - A Modified Confidence Set for the Structural Break Date in Linear Regression Models
Date: 2014-05-07 By: Yamamoto, Yohei URL: http://d.repec.org/n?u=RePEc:hit:econdp:2014-08&r=ecm
Keywords: coverage ratio, nonlocal asymptotics, heteroskedasticity and autocorrelation consistent covariance, condence set JEL: C12 C38 - Fast computation of reconciled forecasts for hierarchical and grouped time series
Date: 2014 By: Rob J Hyndman
Alan Lee
Earo WangURL: http://d.repec.org/n?u=RePEc:msh:ebswps:2014-17&r=ecm
Keywords: combining forecasts, grouped time series, hierarchical time series, reconciling forecasts, weighted least squares. JEL: C32 C53 C63 - Interpreting Financial Market Crashes as Earthquakes: A New early Warning System for Medium Term Crashes
Date: 2014-06-03 By: Francine Gresnigt (Erasmus University Rotterdam)
Erik Kole (Erasmus University Rotterdam)
Philip Hans Franses (Erasmus University Rotterdam)URL: http://d.repec.org/n?u=RePEc:dgr:uvatin:20140067&r=ecm
Keywords: Financial crashes; Hawkes process; self-exciting process; Early Warning System JEL: C13 C15 C53 G17 - Theory and practice of GVAR modeling
Date: 2014-05-01 By: Chudik, Alexander (Federal Reserve Bank of Dallas)
Pesaran, M. Hashem (University of Southern California and Trinity College)URL: http://d.repec.org/n?u=RePEc:fip:feddgw:180&r=ecm The Global Vector Autoregressive (GVAR) approach has proven to be a very useful approach to analyze interactions in the global macroeconomy and other data networks where both the cross-section and the time dimensions are large. This paper surveys the latest developments in the GVAR modeling, examining both the theoretical foundations of the approach and its numerous empirical applications. We provide a synthesis of existing literature and highlight areas for future research. Keywords: Global Vector Autoregressive; global macroeconomy JEL: C32 E17 - Stochastic Frontier Models for Long Panel Data Sets: Measurement of the Underlying Energy Efficiency for the OECD Countries
Date: 2014-06 By: Massimo Filippini (ETH Zurich, Switzerland)
Elisa Tosetti (ETH Zurich, Switzerland)URL: http://d.repec.org/n?u=RePEc:eth:wpswif:14-198&r=ecm In this paper we propose a general approach for estimating stochastic frontier mod- els, suitable when using long panel data sets. We measure efficiency as a linear combi- nation of a finite number of unobservable common factors, having coefficients that vary across firms, plus a time-invariant component. We adopt recently developed economet- ric techniques for large, cross sectionally correlated, non-stationary panel data models to estimate the frontier function. Given the long time span of the panel, we investigate whether the variables, including the unobservable common factors, are non-stationary, and, if so, whether they are cointegrated. To empirically illustrate our approach, we estimate a stochastic frontier model for energy demand, and compute the level of the “underlying energy efficiency” for 24 OECD countries over the period 1980 to 2008. In our specification, we control for variables such as Gross Domestic Product, energy price, climate and technological progress, that are known to impact on energy consumption. We also allow for hetero- geneity across countries in the impact of these factors on energy demand. Our panel unit root tests suggest that energy demand and its key determinants are integrated and that they exhibit a long-run relation. The estimation of efficiency scores points at European countries as the more efficient in consuming energy. Keywords: Energy demand; panels; common factors; principal components. JEL: C10 C31 C33 - A multiple indicator model for panel data: an application to ICT area-level variation
Date: 2014-05 By: Eva Ventura
Albert SatorraURL: http://d.repec.org/n?u=RePEc:upf:upfgen:1419&r=ecm Consider the case in which we have data from repeated surveys covering several geographic areas, and our goal is to characterize these areas on a latent trait that underlies multiple indicators. This characterization occurs, for example, in surveys of information and communication technologies (ICT) conducted by statistical agencies, the objective of which is to assess the level of ICT in each area and its variation over time. It is often of interest to evaluate the impact of area-specific covariates on the ICT level of the area. This paper develops a methodology based on structural equations models (SEMs) that allows not only the ability to estimate the level of the latent trait in each of the areas (building an ICT index) but also to assess the variation of this index in time, as well as its association with the area-specific covariates. The methodology is illustrated using the ICT annual survey data collected in the Spanish region of Catalonia for the years 2008 to 2011. Keywords: structural equations model; confirmatory factor analysis; longitudinal analysis; index; digital divide; Information and Communication Technologies (ICT) - Entropy methods for identifying hedonic models
Date: 2014-06 By: DUPUY Arnaud
GALICHON Alfred
HENRY MarcURL: http://d.repec.org/n?u=RePEc:irs:cepswp:2014-07&r=ecm This paper contributes to the literature on hedonic models in two ways. First, it makes use of Queyranne's reformulation of a hedonic model in the discrete case as a network flow problem in order to provide a proof of existence and integrality of a hedonic equilibrium and efficient computational techniques of hedonic prices. Second, elaborating on entropic methods developped in Galichon and Salanié (2014), this paper proposes a new identification strategy for hedonic models in a single market. This methodology allows one to introduce heterogeneities in both consumers' and producers' attributes and to recover producers' profits and consumers' utilities based on the observation of production and consumption patterns and the set of hedonic prices. Keywords: Hedonic models; Entropic methods; Identification JEL: D12 J30 L11
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, it must include this copyright notice. It may not be sold, or placed in something else for sale.
General information on the NEP project can be found at http://nep.repec.org/. For comments please write to the director of NEP, Marco Novarese at < director @ nep point repec point org >.
NEP is sponsored by the Department of Economics, University of Auckland Business School.
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