
on Econometric Time Series 
By:  Nikolaus Hautsch (Humboldt University Berlin and CFS) 
Abstract:  We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects in the processes of highfrequency return volatilities, trade sizes and trading intensities. The model is estimated by simulated maximum likelihood using efficient importance sampling. Analyzing five minutes data from four liquid stocks traded at the New York Stock Exchange, we find that volatilities, volumes and intensities are driven by idiosyncratic dynamics as well as a highly persistent common factor capturing most causal relations and crossdependencies between the individual variables. This confirms economic theory and suggests more parsimonious specifications of highdimensional trading processes. It turns out that common shocks affect the return volatility and the trading volume rather than the trading intensity. 
Keywords:  Net Foreign Assets; Valuation Adjustment; International Financial Integration 
JEL:  C15 C32 C52 
Date:  2007–09–04 
URL:  http://d.repec.org/n?u=RePEc:cfs:cfswop:wp200725&r=ets 
By:  PéguinFeissolle, Anne (GREQAM); Strikholm, Birgit (Dept. of Economic Statistics, Stockholm School of Economics); Teräsvirta, Timo (CREATES, School of Economics and Management) 
Abstract:  In this paper we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in a sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be wellsized and have reasonably good power properties. 
Keywords:  Hypothesis testing; causality 
JEL:  C22 C51 
Date:  2007–08–27 
URL:  http://d.repec.org/n?u=RePEc:hhs:hastef:0672&r=ets 
By:  Erik Hjalmarsson; PÃ¤r Ã–sterholm 
Abstract:  We investigate the properties of Johansen's (1988, 1991) maximum eigenvalue and trace tests for cointegration under the empirically relevant situation of nearintegrated variables. Using Monte Carlo techniques, we show that in a system with nearintegrated variables, the probability of reaching an erroneous conclusion regarding the cointegrating rank of the system is generally substantially higher than the nominal size. The risk of concluding that completely unrelated series are cointegrated is therefore nonnegligible. The spurious rejection rate can be reduced by performing additional tests of restrictions on the cointegrating vector(s), although it is still substantially larger than the nominal size. 
Date:  2007–06–22 
URL:  http://d.repec.org/n?u=RePEc:imf:imfwpa:07/141&r=ets 
By:  Jon Faust; Jonathan H. Wright 
Abstract:  Many recent papers have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various smallmodel methods. The practical relevance of these results is open to question, however, because these papers generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of vintage data synchronized with the Fed's Greenbook forecast. This dataset consists of a large number of variables, as observed at the time of each Greenbook forecast since 1979. Thus, we can compare realtime large dataset predictions to both simple univariate methods and to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook's advantage in evaluating the current state of the economy, neither large dataset methods nor the Greenbook process offers much advantage over a univariate autoregressive forecast. 
JEL:  C32 C53 E32 E37 
Date:  2007–09 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:13397&r=ets 
By:  Kyungchul Song (Department of Economics, University of Pennsylvania) 
Abstract:  This paper investigates the problem of testing conditional independence of Y and Z given λθ(X) for some unknown θ ∈ Θ ⊂ Rd, for a parametric function λθ(·). For instance, such a problem is relevant in recent literatures of heterogeneous treatment effects and contract theory. First, this paper finds that using Rosenblatt transforms in a certain way, we can construct a class of tests that are asymptotically pivotal and asymptotically unbiased against √nconverging Pitman local alternatives. The asymptotic pivotalness is convenient especially because the asymptotic critical values remain invariant over different estimators of the unknown parameter θ. Even when tests are asymptotically pivotal, however, it is often the case that simulation methods to obtain asymptotic critical values are yet unavailable or complicated, and hence this paper suggests a simple wild bootstrap procedure. A special case of the proposed testing framework is to test the presence of quantile treatment effects in a program evaluation data set. Using the JTPA training data set, we investigate the validity of nonexperimental procedures for inferences about quantile treatment effects of the job training program. 
Keywords:  Conditional independence, asymptotic pivotal tests, Rosenblatt transforms, wild bootstrap 
JEL:  C12 C14 C52 
Date:  2007–09–05 
URL:  http://d.repec.org/n?u=RePEc:pen:papers:07026&r=ets 