nep-ecm New Economics Papers
on Econometrics
Issue of 2017‒04‒09
sixteen papers chosen by
Sune Karlsson
Örebro universitet

  1. Bayesian estimation based on summary statistics: Double asymptotics and practice By Tingting Cheng; Jiti Gao; Peter CB Phillips
  2. Balanced bootstrap joint confidence bands for structural impulse response functions By Stefan Bruder; Michael Wolf
  3. Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series By Cizek, Pavel; Koo, Chao
  4. On the asymptotic normality of the R-estimators of the slope parameters of simple linear regression models with positively dependent errors By Sana Louhichi; Ryozo Miura; Dalibor Volny
  5. Poorly Measured Confounders are More Useful on the Left Than on the Right By Zhuan Pei; Jörn-Steffen Pischke; Hannes Schwandt
  6. Conditional forecasting with DSGE models - A conditional copula approach By Kenneth Sæterhagen Paulsen
  7. Shock Restricted Structural Vector-Autoregressions By Sydney C. Ludvigson; Sai Ma; Serena Ng
  8. Modelling and forecasting WIG20 daily returns By Cristina Amado; Annastiina Silvennoinen; Timo Teräsvirta
  9. An empirical model of dyadic link formation in a network with unobserved heterogeneity By Dzemski, Andreas
  10. Dissecting Characteristics Nonparametrically By Joachim Freyberger; Andreas Neuhierl; Michael Weber
  11. Testing for multiple level shifts in I(0) and I(1) stochastic processes By Josep Lluís Carrion-i-Silvestre; Maria Dolores Gadea
  12. Multivariate Geometric Expectiles By Klaus Herrmann; Marius Hofert; Melina Mailhot
  13. Travel Time Prediction for Taxi-GPS Data Streams By Laha, A. K.; Putatunda, Sayan
  14. Real Time Location Prediction with Taxi-GPS Data Streams By Laha, A. K.; Putatunda, Sayan
  15. A New Approach to Estimation of the R&D-Innovation-Productivity Relationship By Andreas Stephan; Christopher BAUM,; Pardis NABAVI; Hans LÖÖF,
  16. Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity By Jalal Etesami; Ali Habibnia; Negar Kiyavash

  1. By: Tingting Cheng; Jiti Gao; Peter CB Phillips
    Abstract: Ergodic theorem shows that ergodic averages of the posterior draws converge in probability to the posterior mean under the stationarity assumption. The literature also shows that the posterior distribution is asymptotically normal when the sample size of the original data considered goes to infinity. To the best of our knowledge, there is little discussion on the large sample behaviour of the posterior mean. In this paper, we aim to fill this gap. In particular, we extend the posterior mean idea to the conditional mean case, which is conditioning on given vector of summary statistics of the original data. We establish a new asymptotic theory for the conditional mean estimator when both the sample size of the original data concerned and the number of Markov chain Monte Carlo iterations go to infinity. Simulation studies show that this conditional mean estimator has very good finite sample performance. In addition, we employ the conditional mean estimator to estimate a GARCH(1,1) model for S&P 500 stock returns and find that the conditional mean estimator performs better than quasi{maximum likelihood estimation in terms of out-of-sample forecasting.
    Keywords: Bayesian average, conditional mean estimation, ergodic theorem, summary statistic.
    JEL: C11 C15 C21
    Date: 2017
  2. By: Stefan Bruder; Michael Wolf
    Abstract: Constructing joint confidence bands for structural impulse response functions based on a VAR model is a difficult task because of the non-linear nature of such functions. We propose new joint confidence bands that cover the entire true structural impulse response function up to a chosen maximum horizon with a prespecified probability (1 - α), at least asymptotically. Such bands are based on a certain bootstrap procedure from the multiple-testing literature. We compare the finite-sample properties of our method with those of existing methods via extensive Monte Carlo simulations. We also investigate the effect of endogenizing the lag order in our bootstrap procedure on the finite-sample properties. Furthermore, an empirical application to a real data set is provided.
    Keywords: Bootstrap, impulse response functions, joint confidence bands, vector autoregressive process
    JEL: C12 C32
    Date: 2017–03
  3. By: Cizek, Pavel (Tilburg University, Center For Economic Research); Koo, Chao (Tilburg University, Center For Economic Research)
    Abstract: An important and widely used class of semiparametric models is formed by the varyingcoefficient models. Although the varying coefficients are traditionally assumed to be smooth functions, the varying-coefficient model is considered here with the coefficient functions containing a finite set of discontinuities. Contrary to the existing nonparametric and varying-coefficient estimation of piecewise smooth functions, the varying-coefficient models are considered here under dependence and are applicable in time series with heteroscedastic and serially correlated errors. Additionally, the conditional error variance is allowed to exhibit discontinuities at a finite set of points too. The (uniform) consistency and asymptotic normality of the proposed estimators are established and the finite-sample performance is tested via a simulation study.
    Keywords: change point; Heteroscedasticity; local linear fitting; nonlinear time series; varying-coefficient models
    JEL: C13 C14 C22
    Date: 2017
  4. By: Sana Louhichi; Ryozo Miura; Dalibor Volny
    Abstract: The purpose of this paper is to prove the asymptotic normality of the rank estimator of the slope parameter of a simple linear regression model with stationary associated errors. This result follows from a uniform linearity property for a linear rank statistics that we establish under general conditions on the dependence of the errors. We prove also a tightness criterion for weighted empirical process constructed from associated triangular arrays. This criterion is needed for the proofs which are based on that of Koul (1977) and of Louhichi (2000).
    Date: 2015–10–23
  5. By: Zhuan Pei; Jörn-Steffen Pischke; Hannes Schwandt
    Abstract: Researchers frequently test identifying assumptions in regression based research designs (which include instrumental variables or difference-in-differences models) by adding additional control variables on the right hand side of the regression. If such additions do not affect the coefficient of interest (much) a study is presumed to be reliable. We caution that such invariance may result from the fact that the observed variables used in such robustness checks are often poor measures of the potential underlying confounders. In this case, a more powerful test of the identifying assumption is to put the variable on the left hand side of the candidate regression. We provide derivations for the estimators and test statistics involved, as well as power calculations, which can help applied researchers interpret their findings. We illustrate these results in the context of various strategies which have been suggested to identify the returns to schooling.
    JEL: C31 C52
    Date: 2017–03
  6. By: Kenneth Sæterhagen Paulsen (Norges Bank (Central Bank of Norway))
    Abstract: DSGE models may be misspecified in many dimensions, which can affect their forecasting performance. To correct for these misspecifications we can apply conditional information from other models or judgment. Conditional information is not accurate, and can be provided as a probability distribution over different outcomes. These probability distributions are often provided by a set of marginal distributions. To be able to condition on this information in a structural model we must construct the multivariate distribution of the conditional information, i.e. we need to draw multivariate paths from this distribution. One way to do this is to draw from the marginal distributions given a correlation structure between the different marginal distributions. In this paper we use the theoretical correlation structure of the model and a copula to solve this problem. The copula approach makes it possible to take into account more flexible assumption on the conditional information, such as skewness and/or fat tails in the marginal density functions. This method may not only improve density forecasts from the DSGE model, but can also be used to interpret the conditional information in terms of structural shocks/innovations.
    Keywords: DSGE model, conditional forecast, copula
    JEL: C53 E37 E47 E52
    Date: 2017–04–07
  7. By: Sydney C. Ludvigson; Sai Ma; Serena Ng
    Abstract: Identifying assumptions need to be imposed on autoregressive models before they can be used to analyze the dynamic effects of economically interesting shocks. Often, the assumptions are only rich enough to identify a set of solutions. This paper considers two types of restrictions on the structural shocks that can help reduce the number of plausible solutions. The first is imposed on the sign and magnitude of the shocks during unusual episodes in history. The second restricts the correlation between the shocks and components of variables external to the autoregressive model. These non-linear inequality constraints can be used in conjunction with zero and sign restrictions that are already widely used in the literature. The effectiveness of our constraints are illustrated using two applications of the oil market and Monte Carlo experiments calibrated to study the role of uncertainty shocks in economic fluctuations.
    JEL: C01 C5 C51 E17
    Date: 2017–03
  8. By: Cristina Amado (Department of Economics/NIPE, University of Minho; CREATES, Aarhus University); Annastiina Silvennoinen (School of Economics and Finance, Queensland University of Technology, Brisbane); Timo Teräsvirta (C.A.S.E., Humboldt-Universität zu Berlin)
    Abstract: The purpose of this paper is to model daily returns of the WIG20 index. The idea is to consider a model that explicitly takes changes in the amplitude of the clusters of volatility into account. This variation is modelled by a positive-valued deterministic component. A novelty in specification of the model is that the deterministic component is specified before estimating the multiplicative conditional variance component. The resulting model is subjected to misspecification tests and its forecasting performance is compared with that of commonly applied models of conditional heteroskedasticity.
    Date: 2017
  9. By: Dzemski, Andreas (Department of Economics, School of Business, Economics and Law, Göteborg University)
    Abstract: In this paper I study a fixed effects model of dyadic link formation for directed networks. I discuss inference on structural parameters as well as a test of model specification. In the model, an agent's linking decisions depend on perceived similarity to potential linking partners (homophily). Agents are endowed with potentially unobserved characteristics that govern their ability to establish links (productivity) and to receive links (popularity). Heterogeneity in productivity and popularity is a structural driver of degree heterogeneity. The unobserved heterogeneity is captured by a fixed effects approach. This allows for arbitrary correlation between an observed homophily component and latent sources of degree heterogeneity.The linking model accounts for link reciprocity by allowing linking decisions within each pair of agents to be correlated. Estimates of structural parameters related to homophily preferences and reciprocity can be obtained by ML but inference is non-standard due to the incidental parameter problem (Neyman and Scott 1948). I study t-statistics constructed from ML estimates via a naive plug-in approach. For these statistics it is not appropriate to compute critical values from a standard normal distribution because of the incidental parameter problem. I suggest modified t-statistics that are justified by an asymptotic approximation that sends the number of agents to infinity. For a t-test based on the modified statistics, critical values can be computed from a standard normal distribution. My model specification test compares observed transitivity to the transitivity predicted by the dyadic linking model. The test statistic corrects for incidental parameter bias that is due to ML estimation of the null model. The implementation of my procedures is illustrated by an application to favor networks in Indian villages.
    Keywords: Network formation; fixed effects; incidental parameter problem; transitive structure; favor networks
    JEL: C33 C35
    Date: 2017–03
  10. By: Joachim Freyberger; Andreas Neuhierl; Michael Weber
    Abstract: We propose a nonparametric method to test which characteristics provide independent information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a flexible functional form, and is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. Our proposed method has higher out-of-sample explanatory power compared to linear panel regressions, and increases Sharpe ratios by 50%.
    JEL: C14 C52 C58 G12
    Date: 2017–03
  11. By: Josep Lluís Carrion-i-Silvestre; Maria Dolores Gadea
    Abstract: The paper analyzes the detection and estimation of multiple level shifts regardless of the order of integration of the time series. We show that it is possible to extend the sequential testing procedure of Bai and Perron (1998) to the I(1) non-stationary case so that a unified framework based on this approach can be applied. The performance of the test statistic is carried out, establishing a comparison with other existing proposals in the literature.Developing of a sup test statistic for detecting multiple leve shifts for I(1) processes.Simulations are reported on the finite sample performance of the statistic. Further details in the uploaded paper.
    Keywords: No empirical application in the paper., Macroeconometric modeling, Modeling: new developments
    Date: 2015–07–01
  12. By: Klaus Herrmann; Marius Hofert; Melina Mailhot
    Abstract: A generalization of expectiles for d-dimensional multivariate distribution functions is introduced. The resulting geometric expectiles are unique solutions to a convex risk minimization problem and are given by d-dimensional vectors. They are well behaved under common data transformations and the corresponding sample version is shown to be a consistent estimator. We exemplify their usage as risk measures in a number of multivariate settings, highlighting the influence of varying margins and dependence structures.
    Date: 2017–04
  13. By: Laha, A. K.; Putatunda, Sayan
    Abstract: The analysis of data streams offers a great opportunity for development of new methodologies and applications in the area of Intelligent Transportation Systems. In this paper, we propose a new incremental learning approach for the travel time prediction problem for taxi GPS data streams in different scenarios and compare the same with four other existing methods. An extensive performance evaluation using four real life datasets indicate that when the drop-off location is known and the training data sizes are small to moderate the Support Vector Regression method is the best choice considering both prediction accuracy and total computation time. However when the training data size becomes large the Randomized K-Nearest Neighbor Regression with Spherical Distance becomes the method of choice. Even when the drop-off location is unknown then the Support Vector Regression method is the best choice when the training data size is small to moderate while for large training data size the Linear Regression method is a good choice. Finally, when continuous prediction of remaining travel time and continuous updating of total travel time along the trajectory of a trip are considered we find that the Support Vector Regression method has the best predictive accuracy. We also propose a new hybrid method which improves the prediction accuracy of the SVR method in the later part of a trip.
  14. By: Laha, A. K.; Putatunda, Sayan
    Abstract: The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a Damped window model namely, Multivariate multiple regression, spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. The Multivariate multiple regression method and the Ensemble of the three methods are found to be the two best performers. The next pickup location problem is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method and the Ensemble of the three methods gives better performance than the rest.
  15. By: Andreas Stephan; Christopher BAUM,; Pardis NABAVI; Hans LÖÖF,
    Abstract: We evaluate a Generalized Structural Equation Model (GSEM) approach to the estimation of the relationship between R&D, innovation and productivity that focuses on the potentially crucial heterogeneity across technology levels and sectors. see above see above
    Keywords: Sweden, Growth, Macroeconometric modeling
    JEL: C00 L00 O00
    Date: 2015–07–01
  16. By: Jalal Etesami; Ali Habibnia; Negar Kiyavash
    Abstract: Financial instability and its destructive effects on the economy can lead to financial crises due to its contagion or spillover effects to other parts of the economy. Having an accurate measure of systemic risk gives central banks and policy makers the ability to take proper policies in order to stabilize financial markets. Much work is currently being undertaken on the feasibility of identifying and measuring systemic risk. In principle, there are two main schemes to measure interlinkages between financial institutions. One might wish to construct a mathematical model of financial market participant relations as a network/graph by using a combination of information extracted from financial statements like the market value of liabilities of counterparties, or an econometric model to estimate those relations based on financial series. In this paper, we develop a data-driven econometric framework that promotes an understanding of the relationship between financial institutions using a nonlinearly modified Granger-causality network. Unlike existing literature, it is not focused on a linear pairwise estimation. The method allows for nonlinearity and has predictive power over future economic activity through a time-varying network of relationships. Moreover, it can quantify the interlinkages between financial institutions. We also show how the model improve the measurement of systemic risk and explain the link between Granger-causality network and generalized variance decompositions network. We apply the method to the monthly returns of U.S. financial Institutions including banks, broker and insurance companies to identify the level of systemic risk in the financial sector and the contribution of each financial institution.
    Keywords: Systemic risk; Risk Measurement; Financial Linkages and Contagion; Nonlinear Granger Causality; Directed Information Graphs
    JEL: C14 C51 D8 D85 G1 G14 G21 G28 G31
    Date: 2017–03

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