Data Transformation and Forecasting in Models with Unit Roots and Cointegration

We perform a series of Monte Carlo experiments in order to evaluate the impact of data transformation on forecasting models, and find that vector error-corrections dominate differenced data vector autoregressions when the correct data transformation is used, but not when data are incorrectly tansformed, even if the true model contains cointegrating restrictions. We argue that one reason for this is the failure of standard unit root and cointegration tests under incorrect data transformation.Integratedness, Cointegratedness, Nonlinear transformation

Consistent Estimation with a Large Number of Weak Instruments

This paper conducts a general analysis of the conditions under which consistent estimation can be achieved in instrumental variables regression when the available instruments are weak in the local-to-zero sense. More precisely, the approach adopted in this paper combines key features of the local-to-zero framework of Staiger and Stock (1997) and the many-instrument framework of Morimune (1983) and Bekker (1994) and generalizes both of these frameworks in the following ways. First, we consider a general local-to-zero framework which allows for an arbitrary degree of instrument weakness by modeling the first-stage coefficients as shrinking toward zero at an unspecified rate, say b n -1 . Our local-to-zero setup, in fact, reduces to that of Staiger and Stock (1997) in the case where b n = / n . In addition, we examine a broad class of single-equation estimators which extends the well-known k -class to include, amongst others, the Jackknife Instrumental Variables Estimator (JIVE) of Angrist, Imbens, and Krueger (1999). Analysis of estimators within this extended class based on a pathwise asymptotic scheme, where the number of instruments K n is allowed to grow as a function of the sample size, reveals that consistent estimation depends importantly on the relative magnitudes of r n , the growth rate of the concentration parameter, and K n . In particular, it is shown that members of the extended class which satisfy certain general condtions, such as LIML and JIVE, are consistent provided that sqrt{ K n / r n } → 0, as n → ∞. On the other hand, the two-stage least squares (2SLS) estimator is shown not to satisfy the needed conditions and is found to be consistent only if K n / r n → 0, as n → ∞. A main point of our paper is that the use of many instruments may be beneficial from a point estimation standpoint in empirical applications where the available instruments are weak but abundant, as it provides an extra source, by which the concentration parameter can grow, thus, allowing consistent estimation to be achievable, in certain cases, even in the presence of weak instruments. Our results, thus, add to the findings of Staiger and Stock (1997) who study a local-to-zero framework where K n is held fixed and the concentration parameter does not diverge as sample size grows; in consequence, no single-equation estimator is found to be consistent under their setup

Alternative Approximations of the Bias and MSE of the IV Estimator under Weak Identification with an Application to Bias Correction

We provide analytical formulae for the asymptotic bias (ABIAS) and mean squared error (AMSE) of the IV estimator, and obtain approximations thereof based on an asymptotic scheme which essentially requires the expectation of the first stage F -statistic to converge to a finite (possibly small) positive limit as the number of instruments approaches infinity. The approximations so obtained are shown, via regression analysis, to yield good approximations for ABIAS and AMSE functions, and the AMSE approximation is shown to perform well relative to the approximation of Donald and Newey (2001). Additionally, the manner in which our framework generalizes that of Richardson and Wu (1971) is discussed. One consequence of the asymptotic framework adopted here is that consistent estimators for the ABIAS and AMSE can be obtained. As a result, we are able to construct a number of bias corrected OLS and IV estimators, which we show to be consistent under a sequential asymptotic scheme. These bias-corrected estimators are also robust, in the sense that they remain consistent in a conventional asymptotic setup, where the model is fully identified. A small Monte Carlo experiment documents the relative performance of our bias adjusted estimators versus standard IV, OLS, LIML estimators, and it is shown that our estimators have lower bias than LIML for various levels of endogeneity and instrument relevance

A hartai németek hosszúra nyúlt "rövid 20. százada" (könyvismertető)

A recenzált mű: EILER Ferenc: Németek, helyi társadalom és hatalom: Harta, 1920–1989. MTA Etnikai-nemzeti Kisebbségkutató Intézet, Budapest, 2011. 201 p

PROFILE OF PARTICIPANTS IN FISH AND WILDLIFE RELATED OUTDOOR RECREATIONAL ACTIVITIES IN THE UNITED STATES

Resource /Energy Economics and Policy,