nep-ecm New Economics Papers
on Econometrics
Issue of 2014‒03‒15
nineteen papers chosen by
Sune Karlsson
Orebro University

  1. Specification Tests for Nonlinear Dynamic Models By Igor Kheifets
  2. Estimating multivariate GARCH and stochastic correlation models equation by equation By Francq, Christian; Zakoian, Jean-Michel
  3. Specification Testing for Nonlinear Multivariate Cointegrating Regressions By Chaohua Dong; Jiti Gao; Dag Tjostheim; Jiying Yin
  4. Semiparametric Single-Index Panel Data Models with Cross-Sectional Dependence By Bin Peng; Chaohua Dong; Jiti Gao
  5. A Practical Test for Misspecification in Regression: Functional Form, Separability and Distribution By Juan M. Rodríguez-Póo; Stefan Sperlich; Philippe Vieu
  6. Parameter estimation for subcritical Heston models based on discrete time observations By Matyas Barczy; Gyula Pap; Tamas T. Szabo
  7. Model Averaging in Predictive Regressions By Liu, Chu-An; Kuo, Biing-Shen
  8. On Variance Estimation for a Gini Coefficient Estimator Obtained from Complex Survey Data By Judith A. Clarke; Ahmed A. Hoque
  9. Discussion of “Principal Volatility Component Analysis” by Yu-Pin Hu and Ruey Tsay By Michael McAleer
  10. A Combined Nonparametric Test for Seasonal Unit Roots By Kunst, Robert M.
  11. A non parametric ACD model By Cosma, Antonio; Galli, Fausto
  12. Stochastic Model Specification Search for Time-Varying Parameter VARs By Eric Eisenstat; Joshua C.C. Chan; Rodney W. Strachan
  13. Stochastic conditonal range, a latent variable model for financial volatility By Galli, Fausto
  14. Estimation for Single-index and Partially Linear Single-index Nonstationary Time Series Models By Chaohua Dong; Jiti Gao; Dag Tjostheim
  15. Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices By Jakub Nowotarski; Rafal Weron
  16. The uncertainty of conditional returns, volatilities and correlations in DCC models By Diego Fresoli; Esther Ruiz
  17. Generalised Density Forecast Combinations By N. Fawcett; G. Kapetanios; J. Mitchell; S. Price
  18. Designing Experiments to Measure Spillover Effects By Sarah Baird; J. Aislinn Bohren; Craig McIntosh; Berk Ozler
  19. Fuzzy Changes-in Changes By de Chaisemartin, Clement; D'Haultfoeuille, Xavier

  1. By: Igor Kheifets (New Economic School, Moscow)
    Abstract: We propose a new adequacy test and a graphical evaluation tool for nonlinear dynamic models. The proposed techniques can be applied in any setup where parametric conditional distribution of the data is specified, in particular to models involving conditional volatility, conditional higher moments, conditional quantiles, asymmetry, Value at Risk models, duration models, diffusion models, etc. Compared to other tests, the new test properly controls the nonlinear dynamic behavior in conditional distribution and does not rely on smoothing techniques which require a choice of several tuning parameters. The test is based on a new kind of multivariate empirical process of contemporaneous and lagged probability integral transforms. We establish weak convergence of the process under parameter uncertainty and local alternatives. We justify a parametric bootstrap approximation that accounts for parameter estimation effects often ignored in practice. Monte Carlo experiments show that the test has good finite-sample size and power properties. Using the new test and graphical tools we check adequacy of various popular heteroscedastic models for stock exchange index data.
    Keywords: Conditional distribution, Time series, Goodness-of-fit, Empirical process, Weak convergence, Parameter uncertainty, Probability integral transform
    JEL: C12 C22 C52
    Date: 2014–03
  2. By: Francq, Christian; Zakoian, Jean-Michel
    Abstract: A new approach is proposed to estimate a large class of multivariate volatility models. The method is based on estimating equation-by-equation the volatility parameters of the individual returns by quasi-maximum likelihood in a first step, and estimating the correlations based on volatility-standardized returns in a second step. Instead of estimating a $d$-multivariate volatility model we thus estimate $d$ univariate GARCH-type equations plus a correlation matrix, which is generally much simpler and numerically efficient. The strong consistency and asymptotic normality of the first-step estimator is established in a very general framework. For generalized constant conditional correlation models, and also for some time-varying conditional correlation models, we obtain the asymptotic properties of the two-step estimator. Our estimator can also be used to test the restrictions imposed by a particular MGARCH specification. An application to financial series illustrates the interest of the approach.
    Keywords: Constant conditional correlation; Dynamic conditional correlation; Markov switching models; Multivariate GARCH; Quasi maximum likelihood estimation
    JEL: C01 C13 C32
    Date: 2014
  3. By: Chaohua Dong; Jiti Gao; Dag Tjostheim; Jiying Yin
    Abstract: This paper considers a general model specification test for nonlinear multivariate cointegrating regressions where the regressor consists of a univariate integrated time series and a vector of stationary time series. The regressors and the errors are generated from the same innovations, so that the model accommodates endogeniety. A new and simple test is proposed and the resulting asymptotic theory is established. The test statistic is constructed based on a natural distance function between a nonparametric estimate and a smoothed parametric counterpart. The asymptotic distribution of the test statistic under the parametric specification is proportional to that of a local-time random variable with a known distribution. In addition, the finite sample performance of the proposed test is evaluated through using both simulated and real data examples.
    Keywords: ointegration, endogeneity, nonparametric kernel estimation, parametric model speci-fication, time series.
    Date: 2014
  4. By: Bin Peng; Chaohua Dong; Jiti Gao
    Abstract: In this paper, we consider a semiparametric single index panel data mode with cross-sectional dependence, high-dimensionality and stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then establish some consistent closed-form estimates for both the unknown parameters and a link function for the case where both N and T go to ∞. Rates of convergence and asymptotic normality consistencies are established for the proposed estimates. Our experience suggests that the proposed estimation method is simple and thus attractive for finite-sample studies and empirical implementations. Moreover, both the finite-sample performance and the empirical applications show that the proposed estimation method works well when the cross-sectional dependence exists in the data set.
    Keywords: symptotic theory; closed-form estimate; nonlinear panel data model; orthogonal series method
    Date: 2014
  5. By: Juan M. Rodríguez-Póo; Stefan Sperlich; Philippe Vieu
    Abstract: This paper discusses the problem of testing misspecifications in semiparametric regression models for a large family of econometric models under rather general conditions on not further specified semiparametric estimators. We focus on mainly three issues that typically arise in econometrics. First, many econometric models are estimated through maximum likelihood like limited dependent variable models. Second, often the null hypothesis is only partly modeled parametrically. Third, under the null often some structure like separability is imposed on the nonparametric part. In order to address these points we introduce an adaptive omnibus test. Special emphasis is given on practical issues like bias reduction, adaptive bandwidth choice, rather general but simple requirements on the estimates, and finite sample performance, including the resampling approximations.
    Keywords: Specification test, semiparametric econometrics, adaptive testing, limited dependent variables, separability
    Date: 2012–09
  6. By: Matyas Barczy; Gyula Pap; Tamas T. Szabo
    Abstract: We study asymptotic properties of some parameter estimators for subcritical Heston models based on discrete time observations derived from conditional least squares estimators of some modified parameters.
    Date: 2014–03
  7. By: Liu, Chu-An; Kuo, Biing-Shen
    Abstract: This paper considers forecast combination in a predictive regression. We construct the point forecast by combining predictions from all possible linear regression models given a set of potentially relevant predictors. We propose a frequentist model averaging criterion, an asymptotically unbiased estimator of the mean squared forecast error (MSFE), to select forecast weights. In contrast to the existing literature, we derive the MSFE in a local asymptotic framework without the i.i.d. normal assumption. This result allows us to decompose the MSFE into the bias and variance components and also to account for the correlations between candidate models. Monte Carlo simulations show that our averaging estimator has much lower MSFE than alternative methods such as weighted AIC, weighted BIC, Mallows model averaging, and jackknife model averaging. We apply the proposed method to stock return predictions.
    Keywords: Forecast combination, Local asymptotic theory, Plug-in estimators.
    JEL: C52 C53
    Date: 2014–03–07
  8. By: Judith A. Clarke (Department of Economics, University of Victoria); Ahmed A. Hoque
    Abstract: Obtaining variances for the plug-in estimator of the Gini coefficient for inequality has preoccupied researchers for decades with proposed analytic formulae often cumbersome to apply, in addition to being obtained assuming an iid structure. Bhattacharya (2007, Journal of Econometrics) provides an (asymptotic) variance when data arise from a complex survey, a sampling design common with data frequently used in inequality studies. Under a complex survey sampling design, we prove that Bhattacharya’s variance estimator is equivalent to an asymptotic version of the estimator derived by Binder and Kovacevic (1995, Survey Methodology) more than a decade earlier. In addition, we show that Davidson’s (2009, Journal of Econometrics) derived variance, for the iid case, is a simplification of that provided by Binder and Kovacevic. These results are computationally useful, as the Binder and Kovacevic variance estimator is straightforward to calculate in practice. To aid applied researchers, we show how auxiliary regressions can be used to generate the plug-in Gini estimator and its asymptotic variance, irrespective of the sampling design. Health data on the body mass index for Bangladeshi women is employed in an illustration.
    Keywords: Inequality; Asymptotic inference; Gini index; Complex survey
    Date: 2014–03–04
  9. By: Michael McAleer (University of Canterbury)
    Abstract: This note discusses some aspects of the paper by Hu and Tsay (2014), “Principal Volatility Component Analysis”. The key issues are considered, and are also related to existing conditional covariance and correlation models. Some caveats are given about multivariate models of time-varying conditional covariance and correlation models.
    Keywords: Principal Component Analysis, Principal Volatility Component Analysis, Vector time-varying conditional heteroskedasticity, BEKK, DCC, asymptotic properties
    JEL: C32 C58 F37
    Date: 2014–02–23
  10. By: Kunst, Robert M. (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and University of Vienna)
    Abstract: Nonparametric unit-root tests are a useful addendum to the tool-box of time-series analysis. They tend to trade off power for enhanced robustness features. We consider combinations of the RURS (seasonal range unit roots) test statistic and a variant of the level-crossings count. This combination exploits two main characteristics of seasonal unit-root models, the range expansion typical of integrated processes and the low frequency of changes among main seasonal shapes. The combination succeeds in achieving power gains over the component tests. Simulations explore the finite-sample behavior relative to traditional parametric tests.
    Keywords: Seasonality, nonparametric tests, visualization, time series
    JEL: C12 C14 C22
    Date: 2014–03
  11. By: Cosma, Antonio; Galli, Fausto
    Abstract: We carry out a non parametric analysis of financial durations. We make use of an existing algorithm to describe non parametrically the dynamics of the process in terms of its lagged realizations and of a latent variable, its conditional mean. The devices needed to effectively apply the algorithm to our dataset are presented. On simulated data, the non parametric procedure yields better estimates than the ones delivered by an incorrectly specified parametric method. On a real dataset, the non parametric estimator seems to mildly overperform with respect to its parametric counterpart. Moreover the non parametric analysis can convey information on the nature of the data generating process that may not be captured by the parametric specification. In particular, once intraday seasonality is directly used as an explana- tory variable, the non parametric approach provides insights about the time-varying nature of the dynamics in the model that the standard procedures of deseasonaliza- tion may lead to overlook.
    Keywords: non parametric, ACD, trade durations, local-linear
    JEL: C14 C58 G10
    Date: 2014–02–27
  12. By: Eric Eisenstat; Joshua C.C. Chan; Rodney W. Strachan
    Abstract: This article develops a new econometric methodology for performing stochastic model specification search (SMSS) in the vast model space of time-varying parameter VARs with stochastic volatility and correlated state transitions. This is motivated by the concern of over-fitting and the typically imprecise inference in these highly parameterized models. For each VAR coefficient, this new method automatically decides whether it is constant or time-varying. Moreover, it can be used to shrink an otherwise unrestricted timevarying parameter VAR to a stationary VAR, thus providing an easy way to (probabilistically) impose stationarity in time-varying parameter models. We demonstrate the effectiveness of the approach with a topical application, where we investigate the dynamic effects of structural shocks in government spending on U.S. taxes and GDP during a period of very low interest rates.
    Keywords: Bayesian Lasso, shrinkage, fiscal policy
    JEL: C11 C52 E37 E47
    Date: 2014–03
  13. By: Galli, Fausto
    Abstract: In this paper I introduce a latent variable augmented version of the conditional autoregressive range (CARR) model. The new model, called stochastic conditional- range (SCR) can be estimated by Kalman filter or by efficient importance sampling depending on the hypotheses on the distributional form of the innovations. A predic- tive accuracy comparison with the CARR model shows that the new approach can provide an interesting alternative.
    Keywords: Financial econometrics, range, volatility, importance sampling
    JEL: C15 C5 C58
    Date: 2014–02–28
  14. By: Chaohua Dong; Jiti Gao; Dag Tjostheim
    Abstract: Estimation in two classes of popular models, single-index models and partially linear single-index models, is studied in this paper. Such models feature nonstationarity. Orthogonal series expansion is used to approximate the unknown integrable link function in the models and a profile approach is used to derive the estimators. The findings include dual convergence rates of the estimators for the single-index models and a trio of convergence rates for the partially linear single-index models. More precisely, the estimators for single-index model converge along the direction of the true parameter vector at rate of n^(-1/4), while at rate of n^(-3/4) along all directions orthogonal to the true parameter vector; on the other hand, the estimators of the index vector for the partially single-index model retain the dual convergence rates as in the single-index model but the estimators of the coefficients in the linear part of the model possess rate n^(-1). Monte Carlo simulation verifies these theoretical results. An empirical study on the dataset of aggregate disposable income, consumption, investment and real interest rate in the United States between 1960:1-2009:3 furnishes an application of the proposed estimation procedures in practice.
    Keywords: onstationarity, orthogonal series expansion, single-index models, partially linear single-index models, dual convergence rates, a trio of convergence rates.
    Date: 2014
  15. By: Jakub Nowotarski; Rafal Weron
    Abstract: We evaluate a recently proposed method for constructing prediction intervals, which utilizes the concept of quantile regression (QR) and a pool of point forecasts of different time series models.We find that in terms of interval forecasting of Nord Pool day-ahead prices the new QR-based approach significantly outperforms prediction intervals obtained from standard, as well as, semi-parametric autoregressive time series models.
    Keywords: Electricity spot price; Prediction interval; Quantile regression; Forecasts combination
    JEL: C22 C24 C53 Q47
    Date: 2014–03–10
  16. By: Diego Fresoli; Esther Ruiz
    Abstract: When forecasting conditional correlations that evolve according to a Dynamic Conditional Correlation (DCC) model, only point forecasts can be obtained at each moment of time. In this paper, we analyze the finite sample properties of a bootstrap procedure to approximate the density of these forecasts that also allows obtaining conditional densities for future returns and volatilities. The procedure is illustrated by obtaining conditional forecast intervals and regions of returns, volatilities andcorrelations in the context of a system of daily exchange rates returns of the Euro, Japanese Yen and Australian Dollar against the US Dollar
    Keywords: Bootstrap forecast intervals, Forecast regions, Dynamic Conditional Correlation, Exchange rates, Realized correlation, Resampling methods
    Date: 2014–02
  17. By: N. Fawcett; G. Kapetanios; J. Mitchell; S. Price
    Abstract: Density forecast combinations are becoming increasingly popular as a means of improving forecast `accuracy’, as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score of the generalised density combination where the combination weights depend on the variable one is trying to forecast. Specific attention is paid to the use of piecewise linear weight functions that let the weights vary by region of the density. We analyse these schemes theoretically, in Monte Carlo experiments and in an empirical study. Our results show that the generalised combinations outperform their linear counterparts.
    Keywords: Density Forecasting, Model Combination, Scoring Rules
    JEL: C53
    Date: 2014–03
  18. By: Sarah Baird (George Washington University and University of Otago); J. Aislinn Bohren (Department of Economics, University of Pennsylvania); Craig McIntosh (University of California, San Diego); Berk Ozler (World Bank and University of Otago)
    Abstract: This paper formalizes the design of experiments intended specifically to study spillover effects. By first randomizing the intensity of treatment within clusters and then randomly assigning individual treatment conditional on this cluster-level intensity, a novel set of treatment effects can be identified. We develop a formal framework for consistent estimation of these effects, and provide explicit expressions for power calculations. We show that the power to detect average treatment effects declines precisely with the quantity that identifies the novel treatment effects. A demonstration of the technique is provided using a cash transfer program in Malawi.
    Keywords: Experimental Design, Networks, Cash Transfers
    JEL: C93 O22 I25
    Date: 2014–02–07
  19. By: de Chaisemartin, Clement (University of Warwick); D'Haultfoeuille, Xavier (CREST)
    Abstract: The changes-in-changes model extends the widely used difference-in-differences to situations where outcomes may evolve heterogeneously. Contrary to difference-in-differences, this model is invariant to the scaling of the outcome. This paper develops an instrumental variable changes-in-changes model, to allow for situations in which perfect control and treatment groups cannot be defined, so that some units may be treated in the "control group", while some units may remain untreated in the "treatment group". This is the case for instance with repeated cross sections, if the treatment is not tied to a strict rule. Under a mild strengthening of the changes-in-changes model, treatment effects in a population of compliers are point identied when the treatment rate does not change in the control group, and partially identied otherwise. Simple plug-in estimators of treatment effects are proposed. We show that they are asymptotically normal, and that the bootstrap is valid. Finally, we use our results to reanalyze findings in Field (2007) and Duo (2001).
    Keywords: differences-in-differences, changes-in-changes, imperfect compliance, instrumental variables, quantile treatment effects, partial identication.
    Date: 2014

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