nep-ecm New Economics Papers
on Econometrics
Issue of 2015‒11‒07
ten papers chosen by
Sune Karlsson
Örebro universitet

  1. Regression Discontinuity Designs with Nonclassical Measurement Error By YANAGI, Takahide
  2. Asymptotic variance approximations for invariant estimators in uncertain asset-pricing models By Gospodinov, Nikolay; Kan, Raymond; Robotti, Cesare
  3. Using low frequency information for predicting high frequency variables By Claudia Foroni; Pierre Guérin; Massimiliano Marcellino
  4. W By Eric Neumayer; Thomas Plümper
  5. Moment Matching in the Present Value identity, and a New Model By Dooruj McRambaccussing
  6. Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models By Johan Dahlin; Thomas B. Sch\"on
  7. Rare event simulation related to financial risks: efficient estimation and sensitivity analysis By Ankush Agarwal; Stefano De Marco; Emmanuel Gobet; Gang Liu
  8. Splines and seasonal unit roots in weekly agricultural prices By Caceres-Hernandez, Jose; Martin-Rodriguez, Gloria
  9. Spatial Heterogeneity in Production Functions Models By Sille, AG; Salvioni, C.; Benedetti, R.
  10. Extremal dependence tests for contagion By Renée Fry-McKibbin; Cody Yu-Ling Hsiao

  1. By: YANAGI, Takahide
    Abstract: This paper develops a nonparametric identification analysis in regression discontinuity (RD) designs where each observable may contain measurement error. Our analysis allows the measurement error to be nonclassical in the sense that it can be arbitrarily dependent of the unobservables as long as the joint distribution satisfies a few smoothness conditions. We provide formal identification conditions under which the standard RD estimand based on the observables identifies a local weighted average treatment effect parameter. We also show that our identifying conditions imply a testable implication of the continuous density of the observable assignment variable.
    Keywords: regression discontinuity, measurement error, nonparametric identification, local average treatment effect, treatment effect heterogeneity
    JEL: C14 C21 C25
    Date: 2015–10–27
  2. By: Gospodinov, Nikolay (Federal Reserve Bank of Atlanta); Kan, Raymond (University of Toronto); Robotti, Cesare (Imperial College Business School)
    Abstract: This paper derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously updated GMM estimators under potentially misspecified models. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model parameters even when the model is rejected by the data. Although the results for the maximum likelihood estimator are only applicable to linear asset-pricing models, the asymptotic distribution of the continuously updated GMM estimator is derived for general, possibly nonlinear, models. The large corrections in the asymptotic variances, which arise from explicitly incorporating model misspecification in the analysis, are illustrated using simulations and an empirical application.
    Keywords: asset pricing; model misspecification; continuously updated GMM; maximum likelihood; asymptotic approximation; misspecification-robust tests
    JEL: C12 C13 G12
    Date: 2015–10–01
  3. By: Claudia Foroni (Norges Bank); Pierre Guérin (Bank of Canada); Massimiliano Marcellino (Bocconi University, IGIER and CEPR)
    Abstract: We analyze how to incorporate low frequency information in models for predicting high frequency variables. In doing so, we introduce a new model, the reverse unrestricted MIDAS (RU-MIDAS), which has a periodic structure but can be estimated by simple least squares methods and used to produce forecasts of high frequency variables that also incorporate low frequency information. We compare this model with two versions of the mixed frequency VAR, which so far had been only applied to study the reverse problem, that is, using the high frequency information for predicting low frequency variables. We then implement a simulation study to evaluate the relative forecasting ability of the alternative models in finite samples. Finally, we conduct several empirical applications to assess the relevance of quarterly survey data for forecasting a set of monthly macroeconomic indicators. Overall, it turns out that low frequency information is important, particularly so when it is just released.
    Keywords: Mixed-Frequency VAR models, temporal aggregation, MIDAS models
    JEL: E37 C53
    Date: 2015–10–29
  4. W
    By: Eric Neumayer; Thomas Plümper
    Abstract: In spatial econometrics, W refers to the matrix that weights the value of the spatially lagged variable of other units. As unimportant as it may appear, W specifies, or at least ought to specify, why and how other units of analysis affect the unit under observation. This article shows that theory must inform five crucial specification choices taken by researchers. Specifically, the connectivity variable employed in W must capture the causal mechanism of spatial dependence. The specification of W further determines the relative relevance of source units from which spatial dependence emanates, and whether receiving units are assumed to be identically or differentially exposed to spatial stimulus. Multiple dimensions of spatial dependence can be modeled as independent, substitutive or conditional links. Finally, spatial effects need not go exclusively in one direction, but can be bi-directional; recipients can simultaneously experience positive spatial dependence from some sources and negative dependence from others. The importance of W stands in stark contrast to applied researchers’ typical use of crude proxy variables (such as geographical proximity) to measure true connectivity, and the practice of adopting standard modeling conventions rather than substantive theory to specify W. This study demonstrates which assumptions these conventions impose on specification choices, and argues that theories of spatial dependence will often conflict with them
    JEL: C1
    Date: 2015
  5. By: Dooruj McRambaccussing
    Abstract: The constrained Vector Autoregression and the fairly recent state space approach are commonly used in the asset pricing literature to estimate present value models. They are used to model time series dynamics of discount rates and expected dividend growth, with the objective of understanding predictability and stock market movements. This paper shows that an ARMA(1,1) structure of price-dividend ratio and realized dividend growth nests an AR(1) specication for expected returns and expected dividend growth. A simpler model is proposed which involves estimating realized dividend growth and the price-dividend ratio as an ARMA(1,1), and matching the variance and autocorrelation of the estimated models to those of the present value to estimate parameters. Monte Carlo results show that the state space model has larger standard errors. Expected returns is persistent in both models, unlike expected dividend growth in the ARMA(1,1). A modest application of the model to the predictability literature shows stronger evidence towards dividend growth predictability.
    Keywords: Present Value, VAR, State Space, Moment Matching
    JEL: G12 G17 C32
    Date: 2015–10
  6. By: Johan Dahlin; Thomas B. Sch\"on
    Abstract: The main propose of this tutorial is to introduce the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state space models (SSMs). Throughout the tutorial, we develop an implementation of the PMH algorithm (and the integrated particle filter) in the statistical programming language R (similar code for MATLAB and Python is also provided at GitHub). Moreover, we provide the reader with some intuition to why the algorithm works and discuss some solutions to numerical problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian SSM with synthetic data and a nonlinear stochastic volatility model with real-world data. We conclude the tutorial by discussing important possible improvements to the algorithm and listing suitable references for further study.
    Date: 2015–11
  7. By: Ankush Agarwal (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - Polytechnique - X - CNRS); Stefano De Marco (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - Polytechnique - X - CNRS); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - Polytechnique - X - CNRS); Gang Liu (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - Polytechnique - X - CNRS)
    Abstract: In this paper, we develop the reversible shaking transformation methods on path space of Gobet and Liu [GL15] to estimate the rare event statistics arising in different financial risk settings which are embedded within a unified framework of isonormal Gaussian process. Namely, we combine splitting methods with both Interacting Particle System (IPS) technique and ergodic transformations using Parallel-One-Path (POP) estimators. We also propose an adaptive version for the POP method and prove its convergence. We demonstrate the application of our methods in various examples which cover usual semi-martingale stochastic models (not necessarily Markovian) driven by Brownian motion and, also, models driven by fractional Brownian motion (non semi-martingale) to address various financial risks. Interestingly, owing to the Gaussian process framework, our methods are also able to efficiently handle the important problem of sensitivities of rare event statistics with respect to the model parameters.
    Keywords: Rare event,Monte Carlo simulation,Markov chains,ergodic properties,interacting particle systems,Malliavin calculus,sensitivity analysis,fractional Brownian motion,credit default swaps,model misspecification,deep out-of-the-money options
    Date: 2015–10–22
  8. By: Caceres-Hernandez, Jose; Martin-Rodriguez, Gloria
    Abstract: In this paper, a methodological proposal is described to test for seasonal unit roots in weekly series of agricultural prices. When the deterministic seasonal component is not fixed over the sample, the tests for unit roots at seasonal frequencies tend to fail to reject the null hypothesis. This being the case, the original series are proposed to be filtered in order to remove the evolving deterministic seasonal component before applying standard procedures for testing for seasonal unit roots. In such a sense, the non-restricted evolving spline model (ESM) and the restricted evolving spline model (RESM) are shown to be useful parametric formulations to capture this type of deterministic seasonal pattern.
    Keywords: agricultural prices, weekly series, unit roots, splines, Agribusiness, Agricultural Finance, C22, Q11,
    Date: 2015
  9. By: Sille, AG; Salvioni, C.; Benedetti, R.
    Abstract: Controlling for unobserved heterogeneity is a fundamental challenge in empirical research, as failing to do so can introduce omitted variables biases and preclude causal inference. In this paper we develop an innovative method – the Iterative Geographically Weighted Regression (IGWR) method – to identify clusters of farms that follow a similar local production econometric model, taking explicitly unobserved spatial heterogeneity into account. The proposed method is the perfect combination of the GWR approach and the adaptive weights smoothing (AWS) procedure. This method is applied to regional samples of olive growing farms in Italy. The main finding is that the conditional global IGWR model fits the data best, proving that explicitly accounting for unobserved spatial heterogeneity is of crucial importance when modeling the production function of firms particularly for those operating in land based industries
    Keywords: Production Economics,
    Date: 2015
  10. By: Renée Fry-McKibbin; Cody Yu-Ling Hsiao
    Abstract: A new test for financial market contagion based on changes in extremal dependence defined as co-kurtosis and co-volatility is developed to identify the propagation mechanism of shocks across international financial markets. The proposed approach captures changes in various aspects of the asset return relationships such as cross-market mean and skewness (co-kurtosis) as well as cross-market volatilities (co-volatility). Monte Carlo experiments show that the tests perform well except for when crisis periods are short in duration. Small crisis sample critical values are calculated for use in this case. In an empirical application involving the global financial crisis of 2008-09, the results show that significant contagion effects are widespread from the US banking sector to global equity markets and banking sectors through either the co-kurtosis or the co-volatility channels, reinforcing that higher order moments matter during crises.
    Keywords: Co-skewness, Co-kurtosis, Co-volatility, Contagion testing, Extremal dependence, Financial crisis, Lagrange multiplier tests.
    JEL: C12 F30 G11 G21
    Date: 2015–11

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