Stock J, Watson M.
Understanding Changes in International Business Cycle Dynamics. Journal of the European Economic Association. 2005;3 (5) :968-1006.
PDF Stock J.
Forecasting with Many Predictors. In:
and Graham Elliott, Granger CWJ, Timmermann A Handbook of Economic Forecasting. Elsevier ; 2005. pp. 515-554.
PDF Eliasz P, Stock J.
Optimal Tests for Reduced Rank Time Variation in Regression Coefficients and Level Variation in the Multivariate Local Level Model. 2005.
WebsiteAbstractThis paper constructs tests for martingale time variation in regression coefficients in
the regression model yt = xt′βt + ut, where βt is k×1, and Σβ is the covariance matrix of
Δβt. Under the null there is no time variation, so Ho: Σβ = 0; under the alternative there is
time variation in r linear combinations of the coefficients, so Ha: rank(Σβ ) = r, where r
may be less than k. The Gaussian point optimal invariant test for this reduced rank
testing problem is derived, and the test’s asymptotic behavior is studied under local
alternatives. The paper also considers the analogous testing problem in the multivariate
local level model Zt = μt + at, where Zt is a k×1 vector, μt is a level process that is constant
under the null but is subject to reduced rank martingale variation under the alternative,
and at is an I(0) process. The test is used to investigate possible common trend variation
in the growth rate of per-capita GDP in France, Germany and Italy.
PDF Stock J, Watson M.
Implications of Dynamic Factor Models for VAR Analysis. 2005.
WebsiteAbstractThis paper considers VAR models incorporating many time series that interact through a
few dynamic factors. Several econometric issues are addressed including estimation of
the number of dynamic factors and tests for the factor restrictions imposed on the VAR.
Structural VAR identification based on timing restrictions, long run restrictions, and
restrictions on factor loadings are discussed and practical computational methods
suggested. Empirical analysis using U.S. data suggest several (7) dynamic factors,
rejection of the exact dynamic factor model but support for an approximate factor model,
and sensible results for a SVAR that identifies money policy shocks using timing
restrictions.
PDF Stock J, Andrews D.
Inference with Weak Instruments. In:
Blundell R, Newey WK, Persson T Advances in Economics and Econometrics, Theory and Applications: Ninth World Congress of the Econometric Society, Vol III. Cambridge: Cambridge University Press ; 2005.
WebsiteAbstractThis paper reviews recent developments in methods for dealing with weak instru-
ments (IVs) in IV regression models. The focus is more on tests (and confidence
intervals derived from tests) than estimators.
The paper also presents new testing results under “many weak IV asymptotics,”
which are relevant when the number of IVs is large and the coefficients on the IVs
are relatively small. Asymptotic power envelopes for invariant tests are established.
Power comparisons of the conditional likelihood ratio (CLR), Anderson-Rubin, and
Lagrange multiplier tests are made. Numerical results show that the CLR test is on
the asymptotic power envelope. This holds no matter what the relative magnitude
of the IV strength to the number of IVs.
PDF Stock J, Watson M.
An Empirical Comparison of Methods for Forecasting Using Many Predictors. 2005.
WebsiteAbstractThis paper provides a simple shrinkage representation that describes the
operational characteristics of various forecasting methods that are applicable when there
are a large number of orthogonal predictors (such as principal components). These
methods include pretest methods, Bayesian model averaging, empirical Bayes, and
bagging. We then compare these and other many-predictor forecasting methods in the
context of macroeconomic forecasting (real activity and inflation) using 131 monthly
predictors with monthly U.S. economic time series data, 1959:1 - 2003:12. The
theoretical shrinkage representations serve to inform our empirical comparison of these
forecasting methods.
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