
on Econometrics 
By:  Wenger, Kai; Leschinski, Christian; Sibbertsen, Philipp 
Abstract:  It is well known that standard tests for a mean shift are invalid in longrange dependent time series. Therefore, several long memory robust extensions of standard testing principles for a changeinmean have been proposed in the literature. These can be divided into two groups: those that utilize consistent estimates of the longrun variance and selfnormalized test statistics. Here, we review this literature and complement it by deriving a new long memory robust version of the supWald test. Apart from giving a systematic review, we conduct an extensive Monte Carlo study to compare the relative performance of these methods. Special attention is paid to the interaction of the test results with the estimation of the longmemory parameter. Furthermore, we show that the power of selfnormalized test statistics can be improved considerably by using an estimator that is robust to mean shifts. 
Keywords:  Fractional Integration; Structural Breaks; Long Memory 
JEL:  C12 C22 
Date:  2017–06 
URL:  http://d.repec.org/n?u=RePEc:han:dpaper:dp598&r=ecm 
By:  Maddalena Cavicchioli; Mario Forni; Marco Lippi; Paolo zaffaroni 
Abstract:  In this paper we introduce three dynamic eigenvalue ratio estimators for the number of dynamic factors. Two of them, the Dynamic Eigenvalue Ratio (DER) and the Dynamic Growth Ratio (DGR) are dynamic counterparts of the eigenvalue ratio estimators (ER and GR) proposed by Ahn and Horenstein (2013). The third, the Dynamic eigenvalue Difference Ratio (DDR), is a new one but closely related to the test statistic proposed by Onatsky (2009). The advantage of such estimators is that they do not require preliminary determination of discretionary parameters. Finally, a static counterpart of the latter estimator, called eigenvalue Difference Ratio estimator (DR), is also proposed. We prove consistency of such estimators and evaluate their performance under simulation. We conclude that both DDR and DR are valid alternatives to existing criteria. Application to real data gives new insights on the number of factors driving the US economy. 
Keywords:  Generalized dynamic factor model, dynamic principal components, number of factors, static factor model 
JEL:  C01 C13 C38 
Date:  2016–10 
URL:  http://d.repec.org/n?u=RePEc:mod:recent:123&r=ecm 
By:  Shujie Ma; Oliver Linton; Jiti Gao 
Abstract:  We propose an estimation methodology for a semiparametric quantile factor panel model. We provide tools for inference that are robust to the existence of moments and to the form of weak crosssectional dependence in the idiosyncratic error term. We apply our method to CRSP daily data. 
Keywords:  Crosssectional dependence, FamaFrench model, inference, sieve estimation. 
JEL:  C14 C21 C23 G12 
Date:  2017 
URL:  http://d.repec.org/n?u=RePEc:msh:ebswps:20178&r=ecm 
By:  Stefano Giglio; Dacheng Xiu 
Abstract:  We propose a threepass method to estimate the risk premia of observable factors in a linear asset pricing model, which is valid even when the observed factors are just a subset of the true factors that drive asset prices or they are measured with error. We show that the risk premium of a factor can be identified in a linear factor model regardless of the rotation of the other control factors as long as they together span the space of true factors. Motivated by this rotation invariance result, our approach uses principal components to recover the factor space and combines the estimated principal components with each observed factor to obtain a consistent estimate of its risk premium. Our methodology also accounts for potential measurement error in the observed factors and detects when such factors are spurious or even useless. The methodology exploits the blessings of dimensionality, and we therefore apply it to a large panel of equity portfolios to estimate risk premia for several workhorse linear models. The estimates are robust to the choice of test portfolios within equities as well as across many asset classes. 
JEL:  C32 C38 C55 C58 G12 
Date:  2017–06 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:23527&r=ecm 
By:  Carvalho, Carlos Viana de; Masini, Ricardo Pereira; Medeiros, Marcelo C. 
Abstract:  We consider a new, flexible and easytoimplement method to estimate causal effects of an intervention on a single treated unit and when a control group is not readily available. We propose a twostep methodology where in the first stage a counterfactual is estimated from a largedimensional set of variables from a pool of untreated units using shrinkage methods, such as the Least Absolute Shrinkage Operator (LASSO). In the second stage, we estimate the average intervention effect on a vector of variables, which is consistent and asymptotically normal. Our results are valid uniformly over a wide class of probability laws. Furthermore, we show that these results still hold when the exact date of the intervention is unknown. Tests for multiple interventions and for contamination effects are also derived. By a simple transformation of the variables of interest, it is also possible to test for intervention effects on several moments (such as the mean or the variance) of the variables of interest. A Monte Carlo experiment evaluates the properties of the method in finite samples and compares it with other alternatives such as the differencesindifferences, factor and the synthetic control methods. In an application we evaluate the effects on inflation of an anti tax evasion program. 
Date:  2017–06–13 
URL:  http://d.repec.org/n?u=RePEc:fgv:eesptd:454&r=ecm 
By:  ChiaLin Chang (Department of Applied Economics Department of Finance National Chung Hsing University Taichung, Taiwan.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.) 
Abstract:  In the class of univariate conditional volatility models, the three most popular are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH) model of Nelson (1990, 1991). For purposes of deriving the mathematical regularity properties, including invertibility, to determine the likelihood function for estimation, and the statistical conditions to establish asymptotic properties, it is convenient to understand the stochastic properties underlying the three univariate models. The random coefficient autoregressive process was used to obtain GARCH by Tsay (1987), an extension of which was used by McAleer (2004) to obtain GJR. A random coefficient complex nonlinear moving average process was used by McAleer and Hafner (2014) to obtain EGARCH. These models can be used to capture asymmetry, which denotes the different effects on conditional volatility of positive and negative effects of equal magnitude, and possibly also leverage, which is the negative correlation between returns shocks and subsequent shocks to volatility (see Black 1979). McAleer (2014) showed that asymmetry was possible for GJR, but not leverage. McAleer and Hafner showed that leverage was not possible for EGARCH. Surprisingly, the conditions for asymmetry in EGARCH seem to have been ignored in the literature, or have concentrated on the incorrect conditions, with no clear explanation, and hence with associated misleading interpretations. The purpose of the paper is to derive the regularity condition for asymmetry in EGARCH to provide the correct interpretation. It is shown that, in practice, EGARCH always displays asymmetry, though not leverage. 
Keywords:  Conditional volatility models, Random coefficient complex nonlinear moving average process, EGARCH, Asymmetry, Leverage, Regularity condition. 
JEL:  C22 C52 C58 G32 
Date:  2017–06 
URL:  http://d.repec.org/n?u=RePEc:ucm:doicae:1717&r=ecm 
By:  Heckman, James J. (University of Chicago); Pinto, Rodrigo (University of California, Los Angeles) 
Abstract:  This paper presents a new monotonicity condition for unordered discrete choice models with multiple treatments. Unlike a less general version of monotonicity in binary and ordered choice models, monotonicity in unordered discrete choice models along with other standard assumptions does not necessarily identify causal effects defined by variation in instruments, although in some cases it does. Our condition implies and is implied by additive separability of the choice equations in terms of observables and unobservables. These results follow from properties of binary matrices developed in this paper. We investigate conditions under which Unordered Monotonicity arises as a consequence of choice behavior. We represent IV estimators of counterfactuals as solutions to discrete mixture problems. 
Keywords:  instrumental variables, monotonicity, revealed preference, Generalized Roy Model, binary matrices, discrete choice, selection bias, identification, discrete mixtures 
JEL:  I21 C93 J15 
Date:  2017–06 
URL:  http://d.repec.org/n?u=RePEc:iza:izadps:dp10821&r=ecm 
By:  Imhof, David 
Abstract:  This paper tests how well the method proposed by Bajari and Ye (2003) performs to detect bidrigging cartels. In the case investigated in this paper, the bidrigging cartel rigged all contracts during the collusive period, and all firms participated to the bidrigging cartel. The two econometric tests constructed by Bajari and Ye (2003) produce a high number of false negative results: the tests do not reject the null hypothesis of competition, although they should have rejected it. A robustness analysis replicates the econometric tests on two different subsamples, composed solely by cover bids. On the first subsample, both tests produce again a high number of false negative results. However, on the second subsample, one test performs better to detect the bidrigging cartel. The paper interprets these results, discusses alternative methods, and concludes with recommendations for competition agencies. 
Keywords:  Bid rigging; Detection methods; Screens; Conditional independence test; Exchangeability test 
JEL:  C00 C40 D22 D40 K40 L40 
Date:  2017–06–12 
URL:  http://d.repec.org/n?u=RePEc:fri:fribow:fribow00483&r=ecm 
By:  Francesco Bianchi; Giovanni Nicolò 
Abstract:  We propose a novel approach to deal with the problem of indeterminacy in Linear Rational Expectations models. The method consists of augmenting the original model with a set of auxiliary exogenous equations that are used to provide the adequate number of explosive roots in presence of indeterminacy. The solution in this expanded state space, if it exists, is always determinate, and is identical to the indeterminate solution of the original model. The proposed approach accommodates determinacy and any degree of indeterminacy, and it can be implemented even when the boundaries of the determinacy region are unknown. As a result, the researcher can estimate the model by using standard packages without restricting the estimates to a certain area of the parameter space. We apply our method to simulated and actual data from a prototypical NewKeynesian model for both regions of the parameter space. We show that our method successfully recovers the true parameter values independent of the initial values. 
JEL:  C19 C51 C62 C63 
Date:  2017–06 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:23521&r=ecm 