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210
The empirical riskreturn relation: a factor analysis approach
, 2007
"... Existing empirical literature on the riskreturn relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large datasets to summarize a large amount of economic info ..."
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Cited by 82 (12 self)
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Existing empirical literature on the riskreturn relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large datasets to summarize a large amount of economic information by few estimated factors, and find that three new factors termed “volatility,” “risk premium,” and “real” factors contain important information about onequarterahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 1620 % of the onequarterahead variation in excess stock market returns, and exhibit stable and statistically significant outofsample forecasting power. We also find a positive conditional riskreturn correlation.
Panels with Nonstationary Multifactor Error Structures ∗
, 2008
"... The presence of crosssectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of crosssectional averages to provide valid inference in the case of stationary panel regressions with a multifac ..."
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Cited by 74 (10 self)
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The presence of crosssectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of crosssectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work and examines the important case where the unobservable common factors follow unit root processes and could be cointegrated. The extension of the results of Pesaran (2006) to the I(1) is remarkable on two counts. Firstly, it is of great interest to note that while intermediate results needed for deriving the asymptotic distribution of the panel estimators differ between the I(1) and I(0) cases, the final results are surprisingly similar. This is in direct contrast to the standard distributional results for I(1) processes that radically differ from those for I(0) processes. Secondly, it is worth noting the significant extra technical demands required to prove the new results. The theoretical findings are further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the crosssectional average based method is robust to a wide variety of data generation processes and has lower biases than the alternative estimation methods considered in the paper.
Unit Roots and Cointegration in Panels
, 2007
"... This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the ..."
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Cited by 54 (3 self)
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This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.
Forecasting with Many Predictors
 Handbook of Economic Forecasting, Vol
, 2006
"... work on macroeconomic modeling and economic forecasting historically has focused on models with only a handful of variables. In contrast, ..."
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Cited by 52 (0 self)
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work on macroeconomic modeling and economic forecasting historically has focused on models with only a handful of variables. In contrast,
An empirical comparison of methods for forecasting using many predictors
, 2005
"... research assistance, and the referees for helpful suggestions. An earlier version of the theoretical results in this paper was circulated earlier under the title “An Empirical ..."
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Cited by 47 (0 self)
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research assistance, and the referees for helpful suggestions. An earlier version of the theoretical results in this paper was circulated earlier under the title “An Empirical
2003): “CrossSection Regression with Common Shocks,” Discussion Paper 1428, Cowles Foundation, Yale University. Available at http://cowles.econ.yale.edu
"... This paper considers regression models for crosssection data that exhibit crosssection dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context. The results of the paper allow for any form of crosssection depe ..."
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Cited by 38 (0 self)
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This paper considers regression models for crosssection data that exhibit crosssection dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context. The results of the paper allow for any form of crosssection dependence and heterogeneity across population units. The probability limits of the LS estimators are determined, and necessary and sufficient conditions are given for consistency. The asymptotic distributions of the estimators are found to be mixed normal after recentering and scaling. The t� Wald, and F statistics are found to have asymptotic standard normal, χ2,andscaledχ2 distributions, respectively, under the null hypothesis when the conditions required for consistency of the parameter under test hold. However, the absolute values of t, Wald, and F statistics are found to diverge to infinity under the null hypothesis when these conditions fail. Confidence intervals exhibit similarly dichotomous behavior. Hence, common shocks are found to be innocuous in some circumstances, but quite problematic in others. Models with factor structures for errors and regressors are considered. Using the general results, conditions are determined under which consistency of the LS estimators holds and fails in models with factor structures. The results are extended to cover heterogeneous and functional factor structures in which common factors have different impacts on different population units.
A Principal Components Approach to CrossSection Dependence in Panels
, 2002
"... The use of GLS to deal with crosssection dependence in panels is not feasible where N is large relative to T since the disturbance covariance matrix is rank deficient. Neither is it the appropriate response if the dependence results from omitted global variables or common shocks correlated with the ..."
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Cited by 38 (3 self)
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The use of GLS to deal with crosssection dependence in panels is not feasible where N is large relative to T since the disturbance covariance matrix is rank deficient. Neither is it the appropriate response if the dependence results from omitted global variables or common shocks correlated with the included regressors. These can be proxied by the principal components of the residuals from a baseline regression. It is shown that the OLS estimates from a regression augmented by these principal components are unbiased and consistent using sequential limits for large T, large N. Simulations show that this leads to a substantial reduction in bias even for relatively small T and N panels. An empirical application indicates that the impact of cross section dependence seems to strengthen the case for long run PPP.
Factor forecasts for the UK
 JOURNAL OF FORECASTING
, 2004
"... Time series models are often adopted for forecasting because of their simplicity and good performance. The number of parameters in these models increases quickly with the number of variables modelled, so that usually only univariate or smallscale multivariate models are considered. Yet, data are no ..."
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Cited by 35 (0 self)
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Time series models are often adopted for forecasting because of their simplicity and good performance. The number of parameters in these models increases quickly with the number of variables modelled, so that usually only univariate or smallscale multivariate models are considered. Yet, data are now readily available for a very large number of macroeconomic variables that are potentially useful when forecasting. We argue that recent developments in the theory of dynamic factor models enable such large data sets to be summarized by relatively few estimated factors, which can then be used to improve forecast accuracy. In this paper we construct a large macroeconomic dataset for the UK, with about 80 variables, model it using a dynamic factor model, and compare the resulting forecasts with those from a set of standard time series models. We find that just six factors are sufficient to explain 50 % of the variability of all the variables in the data set. Moreover, these factors, which can be considered as the main driving forces of the economy, are related to key variables such as interest rates, monetary aggregates, prices, housing and labour market variables, and stock prices. Finally, the factorbased forecasts are shown to improve upon standard time series benchmarks for prices, real aggregates, and financial variables, at virtually no additional modelling or computational costs.
Gravity Models of the IntraEU Trade: Application of the HausmanTaylor Estimation
 University of Edingburgh
, 2004
"... In this paper we follow recent developments of panel data studies and explicitly allow for the existence of unobserved common timespecific factors where their individual responses are also allowed to be heterogeneous across cross section units. In the context of this extended panel data framework ..."
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Cited by 32 (0 self)
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In this paper we follow recent developments of panel data studies and explicitly allow for the existence of unobserved common timespecific factors where their individual responses are also allowed to be heterogeneous across cross section units. In the context of this extended panel data framework we generalize the HausmanTaylor estimation methodology and develop the associated econometric theory. We apply our proposed estimation technique along with the conventional panel data approaches to a comprehensive analysis of the gravity equation of bilateral trade flows amongst the 15 European countries over 19602001. Empirical results clearly demonstrate that our proposed approach fits the data reasonably well and provides much more sensible results than the conventional approach based on the fixed time dummies. These findings may highlight the importance of allowing for a certain degree of cross section dependence through unobserved heterogeneous time specific common effects, otherwise the resulting estimates would be severely biased. JEL Classification: C33, F14.