1,415 research outputs found
Large panels with common factors and spatial correlation
No full textThis paper considers methods for estimating the slope coefficients in large panel data models that are robust to the presence of various forms of error cross-section dependence. It introduces a general framework where error cross-section dependence may arise because of unobserved common effects and/or error spill-over effects due to spatial or other forms of local dependencies. Initially, this paper focuses on a panel regression model where the idiosyncratic errors are spatially dependent and possibly serially correlated, and derives the asymptotic distributions of the mean group and pooled estimators under heterogeneous and homogeneous slope coefficients, and for these estimators proposes non-parametric variance matrix estimators. The paper then considers the more general case of a panel data model with a multifactor error structure and spatial error correlations. Under this framework, the Common Correlated Effects (CCE) estimator, recently advanced by Pesaran (2006), continues to yield estimates of the slope coefficients that are consistent and asymptotically normal. Small sample properties of the estimators under various patterns of cross-section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross-sectionally correlated errors. © 2011 Elsevier B.V. All rights reserved
Model Averaging in Risk Management with an Application to Futures Markets
No full textThis paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for individual as well as average' models. The asymptotic as well as the exact finite-sample distribution of the test statistic, dealing with the possibility of parameter uncertainty, are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns on six currencies, four equity indices, four ten year government bonds and four commodities over the period 1991-2007. The empirical evidence supports the use of thick' model averaging strategies over single models or Bayesian type model averaging procedures
Replication Data for: "Is There a Debt-Threshold Effect on Output Growth?"
No full textChudik, Alexander, Mohaddes, Kamiar, Pesaran, M. Hashem, and Raissi, Mehdi, (2017) "Is There a Debt-Threshold Effect on Output Growth?" Review of Economics and Statistics 99:1, 135-150
Replication Data for: "Is There a Debt-Threshold Effect on Output Growth?"
No full textChudik, Alexander, Mohaddes, Kamiar, Pesaran, M. Hashem, and Raissi, Mehdi, (2017) "Is There a Debt-Threshold Effect on Output Growth?" Review of Economics and Statistics 99:1, 135-150
A VECX* model of the Swiss economy
No full textThis paper applies the modelling strategy of Garratt, Lee, Pesaran and Shin (2003) to the estimation of a structural cointegrated VAR model that relates the core macroeconomic variables of the Swiss economy to current and lagged values of a number of key foreign variables. We identify and test a long-run structure between the variables. Moreover, we analyse the dynamic properties of the model using Generalised Impulse Response Functions. In its current form the model can be used to produce forecasts for the endogenous variables either under alternative specifi cations of the marginal model for the exogenous variables, or conditional on some pre-specifi ed path of those variables (for scenario forecasting). In due course the Swiss VECX* model can also be integrated within a Global VAR (GVAR) model where the foreign variables of the model are determined endogenously.Long-run structural vector autoregression
Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns
No full textThis paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004), and the other uses principal components following the work of Stock and Watson (2002). The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.Cross section dependence, Large panels, Principal components, Common correlated effects, Return equations
Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns
No full textThis paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004), and the other uses principal components following the work of Stock and Watson (2002). The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.cross section dependence, large panels, principal components, common correlated effects, return equations
Modelling sovereign bond spreads in the euro area: a non-linear global VAR approach
No full textInstability in the comovement among bond spreads in the euro area is an important feature for dynamic econometric modelling and forecasting. This chapter illustrates the properties of a non-linear GVAR approach to spreads in the euro area where the changing interdependence among these variables is modelled by making each country spread function of a global variable determined by fiscal fundamentals with a time-varying composition. The model naturally accommodates the possibility of multiple equilibria in the relation between default premia and local fiscal fundamentals. The estimation reveals a significant non-linear relation between spreads and fiscal fundamentals that generates time-varying impulse response of local spreads to shocks in other euro area countries spreads. The GVAR framework is then applied to the analysis of the dynamic effects of fiscal stabilization packages on the cost of government borrowing
JEL Classification: C51, C5
Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models
No full textIn this paper we discuss tests for residual cross section dependence in nonlinear panel data models. The tests are based on average pair-wise residual correlation coefficients. In nonlinear models, the definition of the residual is ambiguous and we consider two approaches: deviations of the observed dependent variable from its expected value and generalized residuals. We show the asymptotic consistency of the cross section dependence (CD) test of Pesaran (2004). In Monte Carlo experiments it emerges that the CD test has the correct size for any combination of N and T whereas the LM test relies on T large relative to N. We then analyze the roll-call votes of the 104th U.S. Congress and find considerable dependence between the votes of the members of Congress.cross-section dependence, nonlinear panel data model
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