nep-ecm New Economics Papers
on Econometrics
Issue of 2019‒12‒23
twenty-one papers chosen by
Sune Karlsson
Örebro universitet

  1. Inference and Specification Testing in Threshold Regression with Endogeneity By Ping Yu; Qin Liao; Peter C.B. Phillips
  2. Bayesian Estimation and Comparison of Conditional Moment Models By Chib, Siddhartha; Shin, Minchul; Simoni, Anna
  3. Two-Step Estimation of the Nonlinear Autoregressive Distributed Lag Model By Jin Seo Cho; Matthew Greenwood-Nimmo; Yong Cheol Shin
  4. Estimation and specification testing of panel data models with non-ignorable persistent heterogeneity, contemporaneous and intertemporal simultaneity and observable and unobservable dynamics By Hajivassiliou, Vassilis
  5. A Regularized Factor-augmented Vector Autoregressive Model By Maurizio Daniele; Julie Schnaitmann
  6. Fully Modified Least Squares for Multicointegrated Systems By Igor Kheifets; Peter C.B. Phillips
  7. Asymmetric Double Pareto Distributions: Maximum Likelihood Estimation with Application to the Growth Rate Distribution of Firms. By Halvarsson, Daniel
  8. Dynamic Conditional Eigenvalue GARCH By Simon Hetland; Rasmus Søndergaard Pedersen; Anders Rahbek
  9. Forecasting with a Panel Tobit Model By Laura Liu; Hyungsik Roger Moon; Frank Schorfheide
  10. Does Predictive Ability of an Asset Price Rest in 'Memory'? Insights from a New Approach. By Mohamed CHIKHI; Claude DIEBOLT; Tapas MISHRA
  11. Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes By Cannon, Alex J.
  12. CENTER-OUTWARD QUANTILES AND THE MEASUREMENT OF MULTIVARIATE RISK By Jan Bierlant; Sven Buitendag; Eustasio Del Barrio; Marc Hallin
  13. A Study on Volatility Spurious Almost Integration Effect: A Threshold Realized GARCH Approach By Dinghai Xu
  14. Measuring “Dark Matter” in Asset Pricing Models By Hui Chen; Winston Wei Dou; Leonid Kogan
  15. Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility By Mark Bognanni; John Zito
  16. Regression with an Imputed Dependent Variable By Thomas Crossley; Peter Levell; Stavros Poupakis
  17. Text Selection By Bryan T. Kelly; Asaf Manela; Alan Moreira
  18. OLS estimation of the intra-household distribution of consumption By Valérie Lechene; Krishna Pendakur; Alexander Wolf
  19. New Methodology for Difference-In-Difference Where Both No Auto-correlation Assumption And Stable Unit Treatment Value Assumption May Not Hold In Policy Impact Assessment (Japanese) By KAINOU Kazunari
  20. Inference in economic experiments By Hirschauer, Norbert; Grüner, Sven; Mußhoff, Oliver; Becker, Claudia
  21. Regularized Quantile Regression Averaging for probabilistic electricity price forecasting By Bartosz Uniejewski; Rafal Weron

  1. By: Ping Yu (The University of Hong Kong); Qin Liao (The University of Hong Kong); Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: We propose three new methods of inference for the threshold point in endogenous threshold regression and two speciï¬ cation tests designed to assess the presence of endogeneity and threshold effects without necessarily relying on instrumentation of the covariates. The ï¬ rst inferential method is a parametric two-stage least squares method and is suitable when instruments are available. The second and third methods are based on smoothing the objective function of the integrated difference kernel estimator in different ways and these methods do not require instrumentation. All three methods are applicable irrespective of endogeneity of the threshold variable. The two speciï¬ cation tests are constructed using a score-type principle. The threshold effect test extends conventional parametric structural change tests to the nonparametric case. A wild bootstrap procedure is suggested to deliver ï¬ nite sample critical values for both tests. Simulations show good ï¬ nite sample performance of these procedures and the methods provide flexibility in testing and inference for practitioners working with threshold models.
    Keywords: Threshold regression, Endogeneity, Identification, Confidence interval, 2SLS, IDKE, Secification testing, Bootstrap, U-statistic
    JEL: C21 C24 C26
    Date: 2019–11
  2. By: Chib, Siddhartha (Olin Business School, Washington University in St. Louis); Shin, Minchul (Federal Reserve Bank of Philadelphia); Simoni, Anna (CREST, CNRS, Ecole Polytechnique)
    Abstract: We provide a Bayesian analysis of models in which the unknown distribution of the outcomes is specified up to a set of conditional moment restrictions. This analysis is based on the nonparametric exponentially tilted empirical likelihood (ETEL) function, which is constructed to satisfy a sequence of unconditional moments, obtained from the conditional moments by an increasing (in sample size) vector of approximating functions (such as tensor splines based on the splines of each conditioning variable). The posterior distribution is shown to satisfy the Bernstein-von Mises theorem, subject to a growth rate condition on the number of approximating functions, even under misspecification of the conditional moments. A large-sample theory for comparing different conditional moment models is also developed. The central result is that the marginal likelihood criterion selects the model that is less misspecified, that is, the model that is closer to the unknown true distribution in terms of the Kullback-Leibler divergence. Several examples are provided to illustrate the framework and results.
    Keywords: Bayesian inference; Bernstein-von Mises theorem; Conditional moment restrictions; Exponentially tilted empirical likelihood; Marginal likelihood; Misspecification; Posterior consistency
    JEL: C11 C13 C14 C52
    Date: 2019–12–09
  3. By: Jin Seo Cho (Yonsei Univ); Matthew Greenwood-Nimmo (Univ of Melbourne); Yong Cheol Shin (Univ of York)
    Abstract: We consider estimation of and inference on the nonlinear autoregressive distributed lag (NARDL) model, which is a single-equation error correction model that allows for asymmetry with respect to positive and negative changes in the explanatory variable(s). We show that the NARDL model exhibits an asymptotic singularity issue that frustrates efforts to derive the asymptotic properties of the single-step estimator. Consequently, we propose a two-step estimation framework, in which the parameters of the long-run relationship are estimated first using the fully-modified least squares estimator before the dynamic parameters are estimated by OLS in the second step. We show that our two-step estimators are consistent for the parameters of the NARDL model and we derive their limit distributions. We also develop Wald test statistics for the hypotheses of short-run and long-run parameter asymmetry. We demonstrate the utility of our framework with an application to postwar dividend-smoothing in the U.S.
    Keywords: Nonlinear Autoregressive Distributed Lag (NARDL) Model; Fully-Modified Least Squares Estimator; Two-Step Estimation; Wald Test Statistic; Dividend-Smoothing.
    JEL: C22 G35
    Date: 2019–12
  4. By: Hajivassiliou, Vassilis
    Abstract: This paper proposes efficient estimation methods for panel data limited dependent variables (LDV) models possessing a variety of complications: non-ignorable persistent heterogeneity; contemporaneous and intertemporal endogeneity; and observable and unobservable dynamics. An important problem handled by the novel framework of this paper involves contemporaneous and intertemporal simultaneity caused by social strategic interactive effects or contagion across economic agents over time. The paper first shows how a simple modification of estimators based on the Random Effects principle can preserve the consistency and asymptotic efficiency of the method in panel data despite non-ignorable persistent heterogeneity driven by correlations between the individual-specific component of the error term and the regressors. The approach is extremely easy to implement and allows straightforward classical and omnibus tests of the significance of such correlations that lie behind the non-ignorable persistent heterogeneity. The method applies to linear as well as nonlinear panel data models, static or dynamic. Two major extensions of the existing literature are that the method works for time-invariant as well as time-varying regressors, and that these dependencies may be non-linear functions of the regressors. The paper then combines this modified random effects approach with two simulationbased estimation strategies to overcome analytical as well as computational intractabilities in a widely applicable class of nonlinear models for panel data, namely the class of LDV models with contemporaneous and intertemporal endogeneity. The effectiveness of the estimation methods in providing asymptotically efficient estimates in such cases is illustrated with three discrete-response econometric models for panel data.
    Keywords: limited dependent variable models; simulation-based estimation; endogeneity; correlated random effects; initial conditions in nonlinear dynamic panel data models; strategic and social interaction; contagion
    JEL: C51 C52 C15
    Date: 2019–09
  5. By: Maurizio Daniele; Julie Schnaitmann
    Abstract: We propose a regularized factor-augmented vector autoregressive (FAVAR) model that allows for sparsity in the factor loadings. In this framework, factors may only load on a subset of variables which simplifies the factor identification and their economic interpretation. We identify the factors in a data-driven manner without imposing specific relations between the unobserved factors and the underlying time series. Using our approach, the effects of structural shocks can be investigated on economically meaningful factors and on all observed time series included in the FAVAR model. We prove consistency for the estimators of the factor loadings, the covariance matrix of the idiosyncratic component, the factors, as well as the autoregressive parameters in the dynamic model. In an empirical application, we investigate the effects of a monetary policy shock on a broad range of economically relevant variables. We identify this shock using a joint identification of the factor model and the structural innovations in the VAR model. We find impulse response functions which are in line with economic rationale, both on the factor aggregates and observed time series level.
    Date: 2019–12
  6. By: Igor Kheifets (Instituto Tecnologico Autonomo de Mexico); Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: Multicointegration is traditionally deï¬ ned as a particular long run relationship among variables in a parametric vector autoregressive model that introduces links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric model, using a semiparametric formulation that reveals the explicit role that singularity of the long run conditional covariance matrix plays in determining multicointegration. The semiparametric framework has the advantage that short run dynamics do not need to be modeled and estimation by standard techniques such as fully modiï¬ ed least squares (FM-OLS) on the original I(1) system is straightforward. The paper derives FM-OLS limit theory in the multicointegrated setting, showing how faster rates of convergence are achieved in the direction of singularity and that the limit distribution depends on the distribution of the conditional one-sided long run covariance estimator used in FM-OLS estimation. Wald tests of restrictions on the regression coefficients have nonstandard limit theory which depends on nuisance parameters in general. The usual tests are shown to be conservative when the restrictions are isolated to the directions of singularity and, under certain conditions, are invariant to singularity otherwise. Simulations show that approximations derived in the paper work well in ï¬ nite samples. We illustrate our ï¬ ndings by analyzing ï¬ scal sustainability of the US government over the post-war period.
    Keywords: Cointegration, Multicointegration, Fully modified regression, Singular long run variance matrix, Degenerate Wald test, Fiscal sustainability
    JEL: C12 C13 C22
    Date: 2019–11
  7. By: Halvarsson, Daniel (The Ratio Institute)
    Abstract: This paper considers a flexible class of asymmetric double Pareto distributions (ADP) that allows for skewness and asymmetric heavy tails. The inference problem is examined for maximum likelihood. Consistency is proven for the general case when all parameters are unknown. After deriving the Fisher information matrix, asymptotic normality and efficiency are established for a restricted model with the location parameter known. The asymptotic properties of the estimators are then examined using Monte Carlo simulations. To assess its goodness of fit, the ADP is applied to companies’ growth rates, for which it is unequivocally favored over competing models
    Keywords: Distribution Theory; Double Pareto Distribution; Maximum Likelihood; Firm Growth
    JEL: C16 C46
    Date: 2019–12–18
  8. By: Simon Hetland (Department of Economics, University of Copenhagen, Denmark); Rasmus Søndergaard Pedersen (Department of Economics, University of Copenhagen, Denmark); Anders Rahbek (Department of Economics, University of Copenhagen, Denmark)
    Abstract: In this paper we consider a multivariate generalized autoregressive conditional heteroskedastic (GARCH) class of models where the eigenvalues of the conditional covariance matrix are time-varying. The proposed dynamics of the eigenvalues is based on applying the general theory of dynamic conditional score models as proposed by Creal, Koopman and Lucas (2013) and Harvey (2013). We denote the obtained GARCH model with dynamic conditional eigenvalues (and constant conditional eigenvectors) as the ?-GARCH model. We provide new results on asymptotic theory for the Gaussian QMLE, and for testing of reduced rank of the (G)ARCH loading matrices of the time-varying eigenvalues. The theory is applied to US data, where we ?find that the eigenvalue structure can be reduced similar to testing for the number in factors in volatility models.
    Keywords: Multivariate GARCH; GO-GARCH; Reduced Rank; Asymptotic Theory
    JEL: C32 C51 C58
    Date: 2019–12–17
  9. By: Laura Liu (Indiana University, Bloomington, Indiana); Hyungsik Roger Moon (University of Southern California and Yonsei); Frank Schorfheide (University of Pennsylvania CEPR, NBER, and PIER)
    Abstract: We use a dynamic panel Tobit model with heteroskedasticity to generate point, set, and density forecasts for a large cross-section of short time series of censored observations. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coeffients and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. We construct set forecasts that explicitly target the average coverage probability for the cross-section. We present a novel application in which we forecast bank-level charge-off rates for credit card and residential real estate loans, comparing various versions of the panel Tobit model.
    Keywords: Bayesian inference, density forecasts, interval forecasts, loan charge-offs, panel data, point forecasts, set forecasts, Tobit model
    JEL: C11 C14 C23 C53 G21
    Date: 2019–05
  10. By: Mohamed CHIKHI; Claude DIEBOLT; Tapas MISHRA
    Abstract: Despite an inherent share of unpredictability, asset prices such as in stock and Bitcoin markets are naturally driven by significant magnitudes of memory; depending on the strength of path dependence, prices in such markets can be (at least partially) predicted. Being able to predict asset prices is always a boon for investors, more so, if the forecasts are largely unconditional and can only be explained by the series’ own historical trajectories. Although memory dynamics have been exploited in forecasting stock prices, Bitcoin market pose additional challenge, because the lack of proper financial theoretic model limits the development of adequate theory-driven empirical construct. In this paper, we propose a class of autoregressive fractionally integrated moving average (ARFIMA) model with asymmetric exponential generalized autoregressive score (AEGAS) errors to accommodate a complex interplay of ‘memory’ to drive predictive performance (an out-of-sample forecasting). Our conditional variance includes leverage effects, jumps and fat tail-skewness distribution, each of which affects magnitude of memory both the stock and Bitcoin price system would possess enabling us to build a true forecast function. We estimate several models using the Skewed Student-t maximum likelihood and find that the informational shocks in asset prices, in general, have permanent effects on returns. The ARFIMA-AEGAS is appropriate for capturing volatility clustering for both negative (long Value-at-Risk) and positive returns (short Value-at-Risk). We show that this model has better predictive performance over competing models for both long and/or some short time horizons. The predictions from this model beats comfortably the random walk model. Accordingly, we find that the weak efficiency assumption of financial markets stands violated for all price returns studied over longer time horizon.
    Keywords: Asset price; Forecasting; Memory; ARFIMA-AEGAS; Leverage effects and jumps; Market Efficiency.
    JEL: C14 C58 C22 G17
    Date: 2019
  11. By: Cannon, Alex J. (Environment and Climate Change Canada)
    Abstract: The goal of quantile regression is to estimate conditional quantiles for specified values of quantile probability using linear or nonlinear regression equations. These estimates are prone to "quantile crossing", where regression predictions for different quantile probabilities do not increase as probability increases. In the context of the environmental sciences, this could, for example, lead to estimates of the magnitude of a 10-yr return period rainstorm that exceed the 20-yr storm, or similar nonphysical results. This problem, as well as the potential for overfitting, is exacerbated for small to moderate sample sizes and for nonlinear quantile regression models. As a remedy, this study introduces a novel nonlinear quantile regression model, the monotone composite quantile regression neural network (MCQRNN), that (1) simultaneously estimates multiple non-crossing, nonlinear conditional quantile functions; (2) allows for optional monotonicity, positivity/non-negativity, and generalized additive model constraints; and (3) can be adapted to estimate standard least-squares regression and non-crossing expectile regression functions. First, the MCQRNN model is evaluated on synthetic data from multiple functions and error distributions using Monte Carlo simulations. MCQRNN outperforms the benchmark models, especially for non-normal error distributions. Next, the MCQRNN model is applied to real-world climate data by estimating rainfall Intensity-Duration-Frequency (IDF) curves at locations in Canada. IDF curves summarize the relationship between the intensity and occurrence frequency of extreme rainfall over storm durations ranging from minutes to a day. Because annual maximum rainfall intensity is a non-negative quantity that should increase monotonically as the occurrence frequency and storm duration decrease, monotonicity and non-negativity constraints are key constraints in IDF curve estimation. In comparison to standard QRNN models, the ability of the MCQRNN model to incorporate these constraints, in addition to non-crossing, leads to more robust and realistic estimates of extreme rainfall.
    Date: 2017–12–05
  12. By: Jan Bierlant; Sven Buitendag; Eustasio Del Barrio; Marc Hallin
    Abstract: All multivariate extensions of the univariate theory of risk measurement run into the same fundamental problem of the absence, in dimension d > 1, of a canonical ordering of Rd. Based on measure transportation ideas, several attempts have been made recently in the statistical literature to overcome that conceptual difficulty. In Hallin (2017), the concepts of center-outward distribution and quantile functions are developed as generalisations of the classical univariate concepts of distribution and quantile functions, along with their empirical versions. The center-outward distribution function F± is a homeomorphic cyclically monotone mapping from Rd \ F−1 ± (0) to the open punctured unit ball Bd \ {0}, while its empirical counterpart F(n) ± is a cyclically monotone mapping from the sample to a regular grid over Bd. In dimension d = 1, F± reduces to 2F − 1, while F(n) ± generates the same sigma-field as traditional univariate ranks. The empirical F(n) ± ,however, involves a large number of ties, which is impractical in the context of risk measurement. We therefore propose a class of smooth approximations Fn,ξ (ξ a smoothness index) of F(n) ± as an alternative to the interpolation developed in del Barrio et al. (2018). This approximation allows for the computation of some new empirical risk measures, based either on the convex potential associated with the proposed transports, or on the volumes of the resulting empirical quantile regions. We also discuss the role of such transports in the evaluation of the risk associated with multivariate regularly varying distributions. Some simulations and applications to case studies illustrate the value of the approach.
    Date: 2019–12
  13. By: Dinghai Xu (Department of Economics, University of Waterloo)
    Abstract: This paper investigates the “spurious almost integration” effect of volatility under a threshold GARCH structure with realized volatility measures. To closely examine the effect, the realized persistence of volatility is proposed to be used as a threshold trigger for volatility regimes. Under the threshold framework, general closed-form solutions of moment conditions are derived, which provide a convenient way to theoretically examine the “spurious almost integration” effect and its associated impacts. We find that introducing the volatility persistence-driven threshold can capture regime-specific characteristics well. It performs better than the traditional GARCH-type models in terms of both in-sample fitting and out-of-sample forecasting. Based on our Monte Carlo and empirical results, in general we find that overlooking the relatively low persistence regime(s) could lead to some misleading conclusions.
    JEL: C01 C58
    Date: 2019–12
  14. By: Hui Chen; Winston Wei Dou; Leonid Kogan
    Abstract: We introduce an information-based fragility measure for GMM models that are potentially misspecified and unstable. A large fragility measure signifies a GMM model's lack of internal refutability (weak power of specification tests) and external validity (poor out-of-sample fit). The fragility of a set of model-implied moment restrictions is tightly linked to the quantity of additional information the econometrician can obtain about the model parameters by imposing these restrictions. Our fragility measure can be computed at little cost even for complex dynamic structural models. We illustrate its applications via two models: a rare-disaster risk model and a long-run risk model.
    JEL: C52 D81 E32 G12
    Date: 2019–11
  15. By: Mark Bognanni; John Zito
    Abstract: We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for the model as an effective mutation kernel for fighting particle degeneracy. We show that, relative to using MCMC alone, our algorithm increases the precision of inference while reducing computing time by an order of magnitude when estimating a medium-scale VAR-SV model.
    Keywords: Vector autoregressions; sequential Monte Carlo; Rao-Blackwellization; particle filter; stochastic volatility
    JEL: E17 C11 C51 C32
    Date: 2019–12–16
  16. By: Thomas Crossley (Institute for Fiscal Studies and Institute for Fiscal Studies, University of Essex); Peter Levell (Institute for Fiscal Studies and Institute for Fiscal Studies); Stavros Poupakis (Institute for Fiscal Studies and University of Essex)
    Abstract: Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching-based imputation procedures lead to inconsistent estimates. We o?er an easily-implemented correction and correct asymptotic standard errors. We illustrate these with Monte Carlo experiments and empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).
    Date: 2019–06–24
  17. By: Bryan T. Kelly; Asaf Manela; Alan Moreira
    Abstract: Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected—journalists, speechwriters, and others craft messages to target their audiences’ limited attention. We develop an economically motivated high dimensional selection model that improves learning from text (and from sparse counts data more generally). Our model is especially useful when the choice to include a phrase is more interesting than the choice of how frequently to repeat it. It allows for parallel estimation, making it computationally scalable. A first application revisits the partisanship of US congressional speech. We find that earlier spikes in partisanship manifested in increased repetition of different phrases, whereas the upward trend starting in the 1990s is due to entirely distinct phrase selection. Additional applications show how our model can backcast, nowcast, and forecast macroeconomic indicators using newspaper text, and that it substantially improves out-of-sample fit relative to alternative approaches.
    JEL: C1 C4 C55 C58 E17 G12 G17
    Date: 2019–11
  18. By: Valérie Lechene (Institute for Fiscal Studies and University College London); Krishna Pendakur (Institute for Fiscal Studies and Simon Fraser University); Alexander Wolf (Institute for Fiscal Studies and ECARES)
    Abstract: Individuals may be poor even if their household is not poor, because the intra-household distribution of resources may be unequal. We develop a model wherein the resource share of each person in a collective household - defined as their share of household consumption - may be estimated by simple linear regressions using off-the-shelf consumer expenditure micro-data. The model is a linear approximation of Dunbar, Lewbel and Pendakur (2013), whose nonlinear structural model can be computationally difficult. Our model allows for complex household types, including those with multiple adult men and/or women and single parent households. We also provide a simple linear pre-test to check for model identification. Resource shares are obtained as nonlinear functions of estimated coefficients from OLS regressions. We apply the model to data from 12 countries, and investigate resource shares, gender gaps and individual poverty. We find that equal sharing - the implicit assumption underlying household-level poverty calculations - is always rejected. We also find evidence of large gender gaps in resource shares, and consequently in poverty rates, in a few countries.
    Date: 2019–07–11
  19. By: KAINOU Kazunari
    Abstract: Difference-In-Difference(DID) is frequently used methodology in Policy Impact Assessment, but necessary assumptions to be confirmed for DID or ways to fulfill and ensure them are not clearly identified nor well developed yet. Especially in case of ignoring No Auto-correlations Assumptions(NACA) or Stable Unit Treatment Value Assumption(SUTVA) related problems may cause certain bias in the estimated assessment results. This paper shows that four majpr assumptions need to be confirmed in DID, namely Overalap, Conditional Independence, NACA and SUTVA, based on the inductive survay of academic papers in Economics, Sociology and so on, and identifies existing measures applicable for three major approaches of DID, experimental approach with rendomisation, statistical approach with matching or synthetic control group. Based on the inductive survay above, this paper proposes new methodology applocable for statistical approaches where both NACA and SUTVA related problems may happen. Assuming that secondary effect from treated group to control group are identical and other assumptions holds, checking the significance of constant terms in the result of regression analysis of rate of before-after indicator(BAI) and difference-in-difference indicator(DIDI) with inverse DIDI for each control group samples provides solutions for both NACA and SUTVA related problems. In order to demonstrate the practicality of the new methodology, this paper tries treatment effect evaluation of Fukushima rice price before and after the East Japan Great Earthquake and Fukusima No.1 Plant Nuclear Accident where NACA and SUTVA related problems exist. And this paper quantified possible bias caused by four major assumptions related problems of DID and concluded that SUTVA related problems potentially cause largest bias in the results in this case.
    Date: 2019–11
  20. By: Hirschauer, Norbert; Grüner, Sven; Mußhoff, Oliver; Becker, Claudia
    Abstract: Replication crisis and debates about p-values have raised doubts about what we can statistically infer from research findings, both in experimental and observational studies. With a view to the present debate on inferential errors, this paper systematizes and discusses experimental designs with regard to the inferences that can and - perhaps more important - that cannot be made from particular designs.
    Keywords: economic experiments,ceteris paribus,confounders,control,inference,Internal/external validity,randomization,random sampling,superpopulation
    JEL: B41 C18 C90
    Date: 2019
  21. By: Bartosz Uniejewski; Rafal Weron
    Abstract: Quantile Regression Averaging (QRA) has sparked interest in the electricity price forecasting community after its unprecedented success in the Global Energy Forecasting Competition 2014, where the top two winning teams in the price track used variants of QRA. However, recent studies have reported the method's vulnerability to low quality predictors when the set of regressors is larger than just a few. To address this issue, we consider a regularized variant of QRA, which utilizes the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically select the relevant regressors. We evaluate the introduced technique – dubbed LASSO QRA or LQRA for short – using datasets from the Polish and Nordic power markets, a set of 25 point forecasts obtained for calibration windows of different lengths and 20 different values of the regularization parameter. By comparing against nearly 30 benchmarks, we provide evidence for its superior predictive performance in terms of the Kupiec test, the pinball score and the test for conditional predictive accuracy.
    Keywords: Electricity price forecasting; Probabilistic forecast; Quantile Regression Averaging; LASSO; Kupiec test; Pinball score; Conditional predictive accuracy
    JEL: C22 C32 C51 C52 C53 Q41 Q47
    Date: 2019–11–16

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