
on Econometrics 
By:  Yunus Emre Ergemen (Aarhus University and CREATES) 
Abstract:  A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary longrange dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unitroot or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vectorautoregressive dynamics; deterministic trends can be featured. Estimations are performed using conditionalsumofsquares criteria based on projected series by which latent characteristics are proxied. Resulting estimates are consistent and asymptotically normal at standard parametric rates. A simulation study provides reliability on the estimation method. The method is then applied to the longrun relationship between debt and GDP. 
Keywords:  Long memory, factor models, panel data, endogeneity, fixed effects, debt and GDP 
JEL:  C32 C33 
Date:  2016–01–13 
URL:  http://d.repec.org/n?u=RePEc:aah:create:201602&r=ecm 
By:  Ricardo T. Fernholz 
Abstract:  This paper introduces nonparametric econometric methods that characterize general power law distributions under basic stability conditions. These methods extend the literature on power laws in the social sciences in several directions. First, we show that any stationary distribution in a random growth setting is shaped entirely by two factors  the idiosyncratic volatilities and reversion rates (a measure of crosssectional mean reversion) for different ranks in the distribution. This result is valid regardless of how growth rates and volatilities vary across different agents, and hence applies to analyses based on Gibrat's law and its extensions. We also discuss results that use our methods to link these two econometric factors to mobility, as measured by the expected transition times from one rank in the distribution to another. Second, we present techniques to estimate these two factors using panel data. 
Date:  2016–01 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1602.00159&r=ecm 
By:  Margherita Gerolimetto (Department of Economics, University of Venice Cà Foscari); Stefano Magrini (Department of Economics, University of Venice Cà Foscari) 
Abstract:  It is quite common in crosssectional convergence analyses that data exhibit strong spatial dependence. While the literature adopting the regression approach is now fully aware that neglecting this feature may lead to inaccurate results and has therefore suggested a number of statistical tools for addressing the issue, research is only at a very initial stage within the distribution dynamics approach. In particular, in the continuous statespace framework, a few authors opted for spatial prefiltering the data in order to guarantee the statistical properties of the estimates. In this paper, we follow an alternative route that starts from the idea that spatial dependence is not just noise but can be a substantive element of the data generating process. In particular, we develop a tool that, building on a meanbias adjustment procedure established in the literature, explicitly allows for spatial dependence in distribution dynamics analysis thus eliminating the need for prefiltering. Using this tool, we then reconsider the evidence on convergence across US states. 
Keywords:  Distribution Dynamics, Nonparametric Smoothing, Spatial Dependence. 
JEL:  C14 C21 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:ven:wpaper:2016:02&r=ecm 
By:  Markku Lanne (University of Helsinki and CREATES); Jani Luoto (University of Helsinki) 
Abstract:  Signidentified structural vector autoregressive (SVAR) models have recently become popular. However, the conventional approach to sign restrictions only yields set identification, and implicitly assumes an informative prior distribution of the impulse responses whose influence does not vanish asymptotically. In other words, within the set the impulse responses are driven by the implicit prior, and the likelihood has no significance. In this paper, we introduce a Bayesian SVAR model where unique identification is achieved by statistical properties of the data. Our setup facilitates assuming a genuinely noninformative prior and thus learning from the data about the impulse responses. While the shocks are statistically identified, they carry no economic meaning as such, and we propose a procedure for labeling them by their probabilities of satisfying each of the given sign restrictions. The impulse responses of the identified economic shocks can subsequently be computed in a straightforward manner. Our approach is quite flexible in that it facilitates labeling only a subset of the signrestricted shocks, and also concluding that none of the sign restrictions is plausible. We illustrate the methods by two empirical applications to U.S. macroeconomic data. 
Keywords:  Structural vector autoregression, independence, posterior model probability, monetary policy shock 
JEL:  C32 C51 C52 
Date:  2016–01–25 
URL:  http://d.repec.org/n?u=RePEc:aah:create:201604&r=ecm 
By:  W. Robert Reed (University of Canterbury) 
Abstract:  This note demonstrates that unit root tests can suffer from inflated Type I error rates when data are cointegrated. Results from Monte Carlo simulations show that three commonly used unit root tests – the ADF, PhillipsPerron, and DFGLS tests – frequently overreject the true null of a unit root for at least one of the cointegrated variables in reasonably sized samples. While the addition of lagged differenced (LD) terms can sometimes eliminate the size distortion, standard diagnostics such as (i) testing for serial correlation in the residuals and (ii) using information criteria to select lags are unable to identify the appropriate number of terms. 
Keywords:  Unit root testing, cointegration, DFGLS test, Augmented DickeyFuller test, PhillipsPerron test, Monte Carlo, simulation 
JEL:  C32 C22 C18 
Date:  2016–01–22 
URL:  http://d.repec.org/n?u=RePEc:cbt:econwp:16/01&r=ecm 
By:  Azeem Shaikh; John List; Yang Xu 
Abstract:  Empiricism in the sciences allows us to test theories, formulate optimal policies, and learn how the world works. In this manner, it is critical that our empirical work provides accurate conclusions about underlying data patterns. False positives represent an especially important problem, as vast public and private resources can be misguided if we base decisions on false discovery. This study explores one especially pernicious influence on false positivesmultiple hypothesis testing (MHT). While MHT potentially affects all types of empirical work, we consider three common scenarios where MHT influences inference within experimental economics: jointly identifying treatment effects for a set of outcomes, estimating heterogenous treatment effects through subgroup analysis, and conducting hypothesis testing for multiple treatment conditions. Building upon the work of Romano and Wolf (2010), we present a correction procedure that incorporates the three scenarios, and illustrate the improvement in power by comparing our results with those obtained by the classic studies due to Bonferroni (1935) and Holm (1979). Importantly, under weak assumptions, our testing procedure asymptotically controls the familywise error rate  the probability of one false rejection  and is asymptotically balanced. We showcase our approach by revisiting the data reported in Karlan and List (2007), to deepen our understanding of why people give to charitable causes. 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:feb:artefa:00402&r=ecm 
By:  TrinoManuel Ñíguez (Banco de España); Javier Perote (Universidad de Salamanca) 
Abstract:  In this study, we propose a new seminonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any nonGaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate GramCharlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce welldefined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit twostage consistent estimation, and it represents the DECO as well as the salient nonGaussian features of portfolio return distributions. The in and outofsample performance of a MMEDECO model of a portfolio of 10 assets demonstrates that it can be a useful tool for risk management purposes. 
Keywords:  density forecasting, dynamic equicorrelation, GramCharlier series, multivariate GARCH, seminonparametric method 
JEL:  C16 G1 
Date:  2016–01 
URL:  http://d.repec.org/n?u=RePEc:bde:wpaper:1602&r=ecm 
By:  Bornn, Luke; Neil Shephard; Reza Solgi 
Date:  2016–01 
URL:  http://d.repec.org/n?u=RePEc:qsh:wpaper:360971&r=ecm 
By:  Firmin Doko Tchatoka (School of Economics, University of Adelaide); JeanMarie Dufour (McGill University) 
Abstract:  We study the distribution of DurbinWuHausman (DWH) tests for exogeneity from a finitesample viewpoint, under the null and alternative hypotheses. We consider linear structural models with possibly nonGaussian errors, where structural parameters may not be identified and where reduced forms can be incompletely specified (or nonparametric). On level control, we characterize the null distributions of all the test statistics. Through conditioning and invariance arguments, we show that these distributions do not involve nuisance parameters. In particular, this applies to several test statistics for which no finitesample distributional theory is yet available, such as the standard statistic proposed by Hausman (1978). The distributions of the test statistics may be nonstandard Â– so corrections to usual asymptotic critical values are needed Â– but the characterizations are sufficiently explicit to yield finitesample (MonteCarlo) tests of the exogeneity hypothesis. The procedures so obtained are robust to weak identification, missing instruments or misspecified reduced forms, and can easily be adapted to allow for parametric nonGaussian error distributions. We give a general invariance result (block triangular invariance) for exogeneity test statistics. This property yields a convenient exogeneity canonical form and a parsimonious reduction of the parameters on which power depends. In the extreme case where no structural parameter is identified, the distributions under the alternative hypothesis and the null hypothesis are identical, so the power function is flat, for all the exogeneity statistics. However, as soon as identification does not fail completely, this phenomenon typically disappears. We present simulation evidence which confirms the finitesample theory. The theoretical results are illustrated with two empirical examples: the relation between trade and economic growth, and the widely studied problem of the return of education to earnings. 
Keywords:  Exogeneity; DurbinWuHausman test; weak instrument; incomplete model; no nGaussian; weak identification; identification robust; finitesample theory; pivotal; invariance; Monte Carlo test; power 
JEL:  C3 C12 C15 C52 
Date:  2016–01 
URL:  http://d.repec.org/n?u=RePEc:adl:wpaper:201601&r=ecm 
By:  Lev B. Klebanov; Greg Temnov; Ashot V. Kakosyan 
Abstract:  In the present paper, we discuss contraarguments concerning the use of ParetoLev\'y distributions for modeling in Finance. It appears that such probability laws do not provide sufficient number of outliers observed in real data. Connection with the classical limit theorem for heavytailed distributions with such type of models is also questionable. The idea of alternative modeling is given. 
Date:  2016–01 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1602.00256&r=ecm 
By:  Chrystalleni Aristidou; David Harvey; Stephen Leybourne 
Abstract:  We examine the behaviour of OLSdemeaned/detrended and GLSdemeaned/detrended unit root tests that employ stationary covariates, as proposed by Hansen (1995) and Elliott and Jansson (2003), respectively, in situations where the magnitude of the initial condition of the time series under consideration may be nonnegligible. We show that the asymptotic power of such tests is very sensitive to the initial condition; OLS and GLSbased tests achieve relatively high power for large and small magnitudes of the initial condition, respectively. Combining information from both types of test via a simple union of rejections strategy is shown to effectively capture the higher power available across all initial condition magnitudes. 
Keywords:  Unit root tests; stationary covariates; initial condition uncertainty; asymptotic power. 
URL:  http://d.repec.org/n?u=RePEc:not:notgts:16/01&r=ecm 
By:  Scaillet, Olivier 
Abstract:  This note shows that adding monotonicity or convexity constraints on the regression function does not restore wellposedness in nonparametric instrumental variable regression. The minimum distance problem without regularisation is still locally illposed. 
Keywords:  Nonparametric Estimation, Instrumental Variable, IllPosed Inverse Problems 
JEL:  C13 C14 C26 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:gnv:wpaper:unige:79975&r=ecm 
By:  Alessandro Stringhi; Silvia Figini 
Abstract:  This paper extends the existing literature on empirical estimation of the confidence intervals associated to the Detrended Fluctuation Analysis (DFA). We used Montecarlo simulation to evaluate the confidence intervals. Varying the parameters in DFA technique, we point out the relationship between those and the standard deviation of H. The parameters considered are the finite time length L, the number of divisors d used and the values of those. We found that all these parameters play a crucial role, determining the accuracy of the estimation of H. 
Date:  2016–02 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1602.00629&r=ecm 