nep-ecm New Economics Papers
on Econometrics
Issue of 2016‒02‒04
thirteen papers chosen by
Sune Karlsson
Örebro universitet

  1. System Estimation of Panel Data Models under Long-Range Dependence By Yunus Emre Ergemen
  2. Empirical Methods for Dynamic Power Law Distributions in the Social Sciences By Ricardo T. Fernholz
  3. Distribution Dynamics in the US. A Spatial Perspective By Margherita Gerolimetto; Stefano Magrini
  4. Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression By Markku Lanne; Jani Luoto
  5. Univariate Unit Root Tests Perform Poorly When Data Are Cointegrated By W. Robert Reed
  6. Multiple Hypothesis Testing in Experimental Economics By Azeem Shaikh; John List; Yang Xu
  7. Multivariate moments expansion density: application of the dynamic equicorrelation model By Trino-Manuel Ñíguez; Javier Perote
  8. Moment conditions and Bayesian nonparametrics By Bornn, Luke; Neil Shephard; Reza Solgi
  9. Exogeneity tests, weak identification, incomplete models and non-Gaussian distributions: Invariance and finite-sample distributional theory By Firmin Doko Tchatoka; Jean-Marie Dufour
  10. Some Contra-Arguments for the Use of Stable Distributions in Financial Modeling By Lev B. Klebanov; Greg Temnov; Ashot V. Kakosyan
  11. The impact of the initial condition on covariate augmented unit root tests By Chrystalleni Aristidou; David Harvey; Stephen Leybourne
  13. How to improve accuracy for DFA technique By Alessandro Stringhi; Silvia Figini

  1. 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 long-range dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic trends can be featured. Estimations are performed using conditional-sum-of-squares 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 long-run 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
  2. 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 cross-sectional 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
  3. 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 cross-sectional 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 state-space framework, a few authors opted for spatial pre-filtering 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 mean-bias adjustment procedure established in the literature, explicitly allows for spatial dependence in distribution dynamics analysis thus eliminating the need for pre-filtering. 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
  4. By: Markku Lanne (University of Helsinki and CREATES); Jani Luoto (University of Helsinki)
    Abstract: Sign-identified 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 sign-restricted 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
  5. 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, Phillips-Perron, and DF-GLS 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, DF-GLS test, Augmented Dickey-Fuller test, Phillips-Perron test, Monte Carlo, simulation
    JEL: C32 C22 C18
    Date: 2016–01–22
  6. 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 positives-multiple 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
  7. By: Trino-Manuel Ñíguez (Banco de España); Javier Perote (Universidad de Salamanca)
    Abstract: In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (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 Gram-Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined 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 two-stage consistent estimation, and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME-DECO model of a portfolio of 10 assets demonstrates that it can be a useful tool for risk management purposes.
    Keywords: density forecasting, dynamic equicorrelation, Gram-Charlier series, multivariate GARCH, semi-nonparametric method
    JEL: C16 G1
    Date: 2016–01
  8. By: Bornn, Luke; Neil Shephard; Reza Solgi
    Date: 2016–01
  9. By: Firmin Doko Tchatoka (School of Economics, University of Adelaide); Jean-Marie Dufour (McGill University)
    Abstract: We study the distribution of Durbin-Wu-Hausman (DWH) tests for exogeneity from a finite-sample viewpoint, under the null and alternative hypotheses. We consider linear structural models with possibly non-Gaussian 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 finite-sample distributional theory is yet available, such as the standard statistic proposed by Hausman (1978). The distributions of the test statistics may be non-standard – so corrections to usual asymptotic critical values are needed – but the characterizations are sufficiently explicit to yield finite-sample (Monte-Carlo) 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 non-Gaussian 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 finite-sample 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; Durbin-Wu-Hausman test; weak instrument; incomplete model; no n-Gaussian; weak identification; identification robust; finite-sample theory; pivotal; invariance; Monte Carlo test; power
    JEL: C3 C12 C15 C52
    Date: 2016–01
  10. By: Lev B. Klebanov; Greg Temnov; Ashot V. Kakosyan
    Abstract: In the present paper, we discuss contra-arguments concerning the use of Pareto-Lev\'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 heavy-tailed distributions with such type of models is also questionable. The idea of alternative modeling is given.
    Date: 2016–01
  11. By: Chrystalleni Aristidou; David Harvey; Stephen Leybourne
    Abstract: We examine the behaviour of OLS-demeaned/detrended and GLS-demeaned/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 non-negligible. We show that the asymptotic power of such tests is very sensitive to the initial condition; OLS- and GLS-based 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.
  12. By: Scaillet, Olivier
    Abstract: This note shows that adding monotonicity or convexity constraints on the regression function does not restore well-posedness in nonparametric instrumental variable regression. The minimum distance problem without regularisation is still locally ill-posed.
    Keywords: Nonparametric Estimation, Instrumental Variable, Ill-Posed Inverse Problems
    JEL: C13 C14 C26
    Date: 2016
  13. 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

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