nep-ecm New Economics Papers
on Econometrics
Issue of 2017‒06‒11
seventeen papers chosen by
Sune Karlsson
Örebro universitet

  1. Quasi-Bayesian Inference for Latent Variable Models with External Information: Application to generalized linear mixed models for biased data By Takahiro Hoshino; Ryosuke Igari
  2. Wavelet decomposition of the financial cycle : An early warning system for financial tsunamis By Voutilainen, Ville
  3. Industry Interdependency Dynamics in a Network Context By Ya Qian; Wolfgang Karl Härdle; Cathy Yi-Hsuan Chen
  4. Two-Stage Least Squares as Minimum Distance By Frank Windmeijer
  5. "Asymptotic Properties of the Maximum Likelihood Estimator in Regime Switching Econometric Models" By Hiroyuki Kasahara; Katsumi Shimotsu
  6. Spatial Differencing: Estimation and Inference By Federico Belotti; Edoardo Di Porto; Gianluca Santoni
  7. Bayesian Data Combination Approach for Repeated Durations under Unobserved Missing Indicators: Application to Interpurchase-Timing in Marketing By Ryosuke Igari; Takahiro Hoshino
  8. Joining the Incompatible: Exploiting Floristic Lists for the Sample-based Estimation of Species Richness By Alessandro Chiarucci; Rosa Maria Di Biase; Lorenzo Fattorini; Marzia Marcheselli; Caterina Pisani
  9. Robust Bayesian regression with the forward search: theory and data analysis By Anthony C. Atkinson; Aldo Corbellini; Marco Riani
  10. Bootstrapping high-frequency jump tests By Dovonon, Prosper; Goncalves, Silvia; Hounyo, Ulrich; Meddahi, Nour
  11. Forecasting the distributions of hourly electricity spot prices By Christian Pape; Arne Vogler; Oliver Woll; Christoph Weber
  12. Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling By Yaxiong Zeng; Diego Klabjan
  13. Statistical Matching for Combining Time-Use Surveys with Consumer Expenditure Surveys: An Evaluation on Real Data By Anil Alpman; François Gardes; Noël Thiombiano
  14. Testing the impossible: identifying exclusion restrictions By Jan F. Kiviet
  15. On some properties of elliptical distributions By Armerin, Fredrik
  16. Some Remarks on the Causal Inference for Historical Persistence By KOGURE, Katsuo
  17. LATE with Mismeasured or Misspecified Treatment: An application to Women's Empowerment in India By Denni Tommasi; Arthur Lewbel; Rossella Calvi

  1. By: Takahiro Hoshino (Faculty of Economics, Keio University); Ryosuke Igari (Graduate School of Economics, Keio University)
    Abstract: There is a vast literature proposing non-Bayesian methods for making inferences incorporating auxiliary information such as population-level marginal moments. However, it is not feasible to apply these methods directly to latent variable models because the data augmentation approach, in which latent variables are treated as incidental parameters and then generated, is not developed. In this paper, we propose a Markov Chain Monte Carlo (MCMC) algorithm with data augmentation for latent variable models for cases in which we have both a sampled dataset and additional information such as population level moments. The resulting quasi-Bayesian inference with auxiliary information is very straightforwaed to implement, and consistency and asymptotic variance of the quasi- Bayesian posterior mean estimators from the MCMC outputs are shown in this paper. The proposed method is especially useful when the dataset is biased but we have an unbiased large sample for some variables or population marginal moments in which it is difficult to correctly specify the sample selection model. For illustrative purposes, we apply the proposed estimation method to generalized linear mixed models for biased data both in simulation studies and in real data analysis. The proposed method can be used to make inferences in non/semi-parametric latent variable models by incorporating the existing semi-parametric Bayesian algorithms such as the Blocked Gibbs sampler in the MCMC iteration.
    Keywords: Latent Variable Modeling, Quasi-Bayes, Population-Level Information, Markov chain Monte Carlo, Data Augmentation
    JEL: C11 C15 C35
    Date: 2017–04–28
  2. By: Voutilainen, Ville
    Abstract: We propose a wavelet-based approach for construction of a financial cycle proxy. Specifically, we decompose three key macro-financial variables – private credit, house prices, and stock prices – on a frequency-scale basis using wavelet multiresolution analysis. The resulting “wavelet-based” sub-series are aggregated into a composite index representing our cycle proxy. Selection of the sub-series deemed most relevant is done by emphasizing early warning properties. The wavelet-based financial cycle proxy is shown to perform well in detecting banking crises in out-of-sample exercises, outperforming the credit-to-GDP gap and a financial cycle proxy derived using the approach of Schüler et al. (2015).
    JEL: C49 E32 E44
    Date: 2017–05–31
  3. By: Ya Qian; Wolfgang Karl Härdle; Cathy Yi-Hsuan Chen
    Abstract: This paper contributes to model the industry interconnecting structure in a network context. General predictive model (Rapach et al. 2016) is extended to quantile LASSO regression so as to incorporate tail risks in the construction of industry interdependency networks. Empirical results show a denser network with heterogeneous central industries in tail cases. Network dynamics demonstrate the variety of interdependency across time. Lower tail interdependency structure gives the most accurate out-of-sample forecast of portfolio returns and network centrality-based trading strategies seem to outperform market portfolios, leading to the possible ’too central to fail’ argument.
    Keywords: Dynamic network, interdependency, general predictive model, quantile LASSO, connectedness, centrality, prediction accuracy, network-based trading strategy
    JEL: C32 C55 C58 G11 G17
    Date: 2017–04
  4. By: Frank Windmeijer
    Abstract: The Two-Stage Least Squares instrumental variables (IV) estimator for the parameters in linear models with a single endogenous variable is shown to be identical to an optimal Minimum Distance (MD) estimator. This MD estimator is a weighted average of the individual instrument-specific IV estimates, with the weights determined by the variances and covariances of the individual estimators under conditional homoskedasticity. It is further shown that the Sargan test statistic for overidentifying restrictions is the same as the MD criterion test statistic. This provides an intuitive interpretation of the Sargan test. The equivalence results also apply to the efficient two-step GMM and robust optimal MD estimators and criterion functions, allowing for general forms of heteroskedasticity. It is further shown how these results extend to the linear overidentified IV model with multiple endogenous variables.
    Keywords: Instrumental Variables, Two-Stage Least Squares, Minimum Distance, Overidentification Test.
    JEL: C26 C13 C12
    Date: 2017–06–07
  5. By: Hiroyuki Kasahara (Vancouver School of Economics, University of British Columbia); Katsumi Shimotsu (Faculty of Economics, The University of Tokyo)
    Abstract: Markov regime switching models have been widely used in numerous empirical applications in economics and finance. However, the asymptotic distribution of the maximum likelihood estimator (MLE) has not been proven for some empirically popular Markov regime switching models. In particular, the asymptotic distribution of the MLE has been unknown for models in which the regime-specific density depends on both the current and the lagged regimes, which include the seminal model of Hamilton (1989) and the switching ARCH model of Hamilton and Susmel (1994). This paper shows the asymptotic normality of the MLE and the consistency of the asymptotic covariance matrix estimate of these models.
    Date: 2017–05
  6. By: Federico Belotti (University of Rome Tor Vergata); Edoardo Di Porto (Università di Napoli Federico II, CSEF and UCFS, Uppsala University); Gianluca Santoni (CEPII)
    Abstract: Spatial differencing is a spatial data transformation pioneered by Holmes (1998) increasingly used to estimate casual effects with non-experimental data. Recently, this transformation has been widely used to deal with omitted variable bias generated by local or site-specific unobservables in a “boundary-discontinuity” design setting. However, as well known in this literature, spatial differencing makes inference problematic. Indeed, given a specific distance threshold, a sample unit may be the neighbor of a number of units on the opposite side of a specific boundary inducing correlation between all differenced observations that share a common sample unit. By recognizing that the spatial differencing transformation produces a special form of dyadic data, we show that the dyadic-robust variance matrix estimator proposed by Cameron and Miller (2014) is, in general, a better solution compared to the most commonly used estimators.
    Keywords: Spatial differencing, Boundary discontinuity, robust inference, dyadic data.
    JEL: C12 C21
    Date: 2017–06–01
  7. By: Ryosuke Igari (Graduate School of Economics, Keio University); Takahiro Hoshino (Faculty of Economics, Keio University)
    Abstract: In this study, we focus on intermittent missingness in repeated duration analysis, which is common in applied studies but has not rigorously been considered in statistics. Under intermittent missingness, whether any missing events exist between two observed events is unknown. In other words, the missing indicators are never observed. Thus, if there exist any missing events between two observed events, we observe only the cumulated duration between two or more events. We propose a quasi-Bayes estimation method that utilizes population-level information to identify unobserved intermittent missingness. The proposed model consists of the following: (1) latent variable model, (2) latent missing indicator model which separates true and composite duration, (3) mixtures of duration models and (4) moment restriction from population-level information to deal with nonignorable intermittent missingness. We use a new estimation procedure that combines objective functions of likelihood and GMM simultaneously with latent variables, which we call Bayesian data combination. We apply the proposed model to analyze interpurchase-duration in database marketing using purchase-history data in Japan, which capture purchase incidences and purchase stores.
    Keywords: Intermittent Missingness, Quasi-Bayes, Latent Variable Modeling, Population-Level Information, Dirichlet Process Mixture
    JEL: C11 C41 M31
    Date: 2017–04–28
  8. By: Alessandro Chiarucci; Rosa Maria Di Biase; Lorenzo Fattorini; Marzia Marcheselli; Caterina Pisani
    Abstract: The lists of species obtained by purposive sampling by field ecologists are used to improve the sample-based estimation of species richness. The estimation criterion is a modification of the difference estimator in which the species inclusion probabilities are estimated by means of the species frequencies from incidence data. If the species list used to support the estimation is complete the estimator guesses the true richness without error. Moreover, contrary to the nonparametric estimators, our estimator provides values invariably greater than the number of species detected by the combination of sample-based and purposive surveys. A presumably asymptotically conservative estimator of the mean squared error is also provided. A simulation study based on two artificial populations is carried out in order to check the performance of the proposed estimator with respect to the familiar nonparametric estimators. Finally, the proposed estimator is adopted to estimate species richness in the Maremma Regional Park, Italy.
    Keywords: Difference estimator; Probabilistic sampling; Purposive survey; Supporting list; Simulation.
    JEL: C13 C15 C18
    Date: 2017–05
  9. By: Anthony C. Atkinson; Aldo Corbellini; Marco Riani
    Abstract: The frequentist forward search yields a flexible and informative form of robust regression. The device of fictitious observations provides a natural way to include prior information in the search. However, this extension is not straightforward, requiring weighted regression. Bayesian versions of forward plots are used to exhibit the presence of multiple outliers in a data set from banking with 1903 observations and nine explanatory variables which shows, in this case, the clear advantages from including prior information in the forward search. Use of observation weights from frequentist robust regression is shown to provide a simple general method for robust Bayesian regression.
    Keywords: Consistency factor Fictitious observation Forward search Graphical methods Outliers Weighted regression
    JEL: C1
    Date: 2017–05–14
  10. By: Dovonon, Prosper; Goncalves, Silvia; Hounyo, Ulrich; Meddahi, Nour
    Date: 2017–05
  11. By: Christian Pape; Arne Vogler; Oliver Woll; Christoph Weber (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen (Campus Essen))
    Abstract: We present a stochastic modelling approach to describe the dynamics of hourly electricity prices. The suggested methodology is a stepwise combination of several mathematical operations to adequately characterize the distribution of electricity spot prices. The basic idea is to analyze day-ahead prices as panel of 24 cross-sectional hours and to identify principal components of hourly prices to account for the cross correlation between hours. Moreover, non-normality of residuals is addressed by performing a normal quantile transformation and specifying appropriate stochastic processes for time series before fit. We highlight the importance of adequate distributional forecasts and present a framework to evaluate the distribution forecast accuracy. The application for German electricity prices 2015 reveal that: (i) An autoregressive specification of the stochastic component delivers the best distribution but not always the best point forecasting results. (ii) Only a complete evaluation of point, interval and density forecast, including formal statistical tests, can ensure a correct model choice.
    Keywords: Distribution forecasts, Electricity, Price forecasting, Panel data, Statistical tests
    JEL: Q47 N74
    Date: 2017–05
  12. By: Yaxiong Zeng; Diego Klabjan
    Abstract: In this work, we design a machine learning based method, online adaptive primal support vector regression (SVR), to model the implied volatility surface. The algorithm proposed is the first derivation and implementation of an online primal kernel SVR. It features enhancements that allow online adaptive learning by embedding the idea of local fitness and budget maintenance. To accelerate our algorithm, we implement its most computationally intensive parts in a Field Programmable Gate Arrays hardware. Using intraday tick data from the E-mini S&P 500 options market, we show that our algorithm outperforms two competing methods and the Gaussian kernel is a better choice than the linear kernel. Sensitivity analysis is also presented to demonstrate how hyper parameters affect the error rates and the number of support vectors in our models.
    Date: 2017–06
  13. By: Anil Alpman (Centre d'Economie de la Sorbonne - Paris School of Economics); François Gardes (Centre d'Economie de la Sorbonne - Paris School of Economics); Noël Thiombiano (Université Ouaga II - Cèdres)
    Abstract: Performing a statistical match to combine two surveys made over the same population by traditional methods is shown to give biased estimates and variance of the imputed values. A method proposed by Rubin (1986) allows imputing an unobserved variable using observations in another dataset by taking into account the partial correlation between the variables that are jointly unobserved for any unit. We use a dataset where households report their expenditures and time-uses to show that fusioning expenditure and time-use surveys by Rubin's procedure allows to recover the true distribution of the missing variables and to yield minimally biased estimates
    Keywords: Data combination; Data fusion; Missing data; Statistical matching; Time-Use
    JEL: C10 D13
    Date: 2017–05
  14. By: Jan F. Kiviet
    Abstract: Method of moment estimators are generally obtained by adopting orthogonality conditions, in which particular functions in terms of the observed data and unknown parameters are supposed to have zero expectation. For regression models this implies exploiting presumed uncorrelatedness of the model disturbances and identifying instrumental variables. Here, utilizing non-orthogonality conditions is examined for linear cross-section multiple regression models. Employing flexible bounds on the correlations between disturbances and regressors one avoids: (i) adoption of often incredible and unverifiable strictly zero correlation assumptions, and (ii) imprecise inference due to possibly weak or invalid instruments. The asymptotic validity of the suggested alternative form of inference is proved and its finite sample accuracy is demonstrated by simulation. It enables to produce inference on coefficient values that within constraints is endogeneity robust. Also a sensitivity analysis of standard least-squares or instrument-based inference is possible, and even a test of the in the standard approach unavoidable though "non-testable" exclusion restrictions regarding external instruments. The practical relevance is illustrated in a few applications borrowed from the textbook literature.
    Date: 2016–12–29
  15. By: Armerin, Fredrik (Center for Banking and Finance)
    Abstract: We look at a characterization of elliptical distributions in the case when finiteness of moments of the random vector is not assumed. Some additional results regarding elliptical distributions are also presented.
    Keywords: Elliptical distributions; multivariate distributions
    JEL: C10
    Date: 2017–06–02
  16. By: KOGURE, Katsuo
    Abstract: A growing body of literature examines the relationships between historical events and contemporary economic outcomes. Recent studies estimate the causal effects using detailed historical data and contemporary microdata of individuals and/or households. In this paper, we discuss conceptual and econometric issues inherent in the causal inference following the potential outcomes framework. We also discuss a simple alternative approach to avoid these issues that is coherent with the potential outcomes framework. An empirical example based on the approach is then presented.
    Keywords: history, economic development, causal effect, Rubin causal model
    JEL: C01 N01
    Date: 2017–05
  17. By: Denni Tommasi; Arthur Lewbel; Rossella Calvi
    Abstract: We show that a local average treatment effect (LATE) can sometimes be point identified and consistently estimated when treatment is mismeasured, or when treatment is estimated using a possibly misspecified structural model. Our associated estimator, which we call Mismeasurement Robust LATE (MR-LATE), is based on differencing two mismeasures of treatment. In our empirical application, treatment is women’s empowerment: whether a wife has significant control of household resources. Due to measurement difficulties and sharing of goods within a household, this treatment cannot be directly observed without error, and so must be estimated. Our outcomes are health indicators of family members. We first estimate a structural model to obtain the otherwise unobserved treatment indicator. Then, using changes in inheritance laws in India as an instrument, we apply our new MR-LATE estimator. We find that women’s empowerment substantially decreases their probability of being anemic or underweight, and children’s likelihood to suffer from cough, fever or diarrhea. We find no significant positive or negative effects on men’s health.
    Keywords: causality; LATE; structural model; collective model; resource shares; bargaining power; health
    JEL: D13 D11 D12 C31 I32
    Date: 2017–06

This nep-ecm issue is ©2017 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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