nep-ecm New Economics Papers
on Econometrics
Issue of 2007‒05‒26
seventeen papers chosen by
Sune Karlsson
Orebro University

  1. Spurious Regression and Trending Variables By Antonio E. Noriega; Daniel Ventosa-Santaularia
  2. Consistent Estimation with Weak Instruments in Panel Data By Chihwa Kao; Long Liu
  3. Comparison of Panel Cointegration Tests By Deniz Dilan Karaman Örsal
  4. Trend Extraction From Time Series With Missing Observations By Schlicht, Ekkehart
  5. Trend Extraction From Time Series With Structural Breaks By Schlicht, Ekkehart
  6. Forecasting Exchange Rates of Major Currencies with Long Maturity Forward Rates By Zsolt Darvas; Zoltán Schepp
  7. Testing for breaks in cointegrated panels By Di Iorio, Francesca; Fachin, Stefano
  8. Filtered Extreme Value Theory for Value-At-Risk Estimation By Ozun, Alper; Cifter, Atilla; Yilmazer, Sait
  9. Long Memory Persistence in the Factor of Implied Volatility Dynamics By Wolfgang Härdle; Julius Mungo
  10. Estimating Systematic Continuous-time Trends in Recidivism using a Non-Gaussian Panel Data Model By Siem Jan Koopman; André Lucas; Marius Ooms; Kees van Montfort; Victor van der Geest
  11. Parameter estimation from multinomial trees to jump diffusions with k means clustering By Lee, Kiseop; Xu, Mingxin
  12. Conditional Stochastic Kernel Estimation by Nonparametric Methods By Laurini, Márcio P. & Valls Pereira, Pedro L.
  13. Multivariate contemporaneous threshold autoregressive models By Michael J. Dueker; Zacharias Psaradakis; Martin Sola; Fabio Spagnolo
  14. Estimation risk effects on backtesting for parametric value-at-risk models By J. Carlos Escanciano; Jose Olmo
  15. Unpacking social interactions By Ethan Cohen-Cole; Giulio Zanella
  16. The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts By Christian Huurman; Francesco Ravazzolo; Chen Zhou
  17. Unit Roots in Inflation and Aggregation Bias By Joseph Byrne; Alexandros Kontonikas; Alberto Montagnoli

  1. By: Antonio E. Noriega (School of Economics, Universidad de Guanajuato); Daniel Ventosa-Santaularia (School of Economics, Universidad de Guanajuato)
    Abstract: This paper analyses the asymptotic and finite sample implications of different types of nonstationary behavior among the dependent and explanatory variables in a linear spurious regression model. We study cases when the nonstationarity in the dependent and explanatory variables is deterministic as well as stochastic. In particular, we derive the order in probability of the t-statistic in a linear regression equation under a variety of empirically relevant data generation processes, and show that he spurious regression phenomenon is present in all cases considered, when at least one of the variables behaves in a nonstationary way. Simulation experiments confirm our asymptotic results.
    Keywords: Trend Stationarity, Structural Breaks, Spurious Regression, Unit Roots, Trends.
    JEL: C12 C13 C22
  2. By: Chihwa Kao (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020); Long Liu (Department of Economics, Maxwell School, Syracuse University, Syracuse, NY 13244-1020)
    Abstract: This note analyzes the asymptotic distribution for instrumental variables regression for panel data when the available instruments are weak. We show that consistency can be established in panel data.
    Keywords: Weak instrument; Two stage least squares; Panel data; Concentration parameter.
    JEL: C33
    Date: 2007–05
  3. By: Deniz Dilan Karaman Örsal
    Abstract: The main aim of this paper is to compare the size and size-adjusted power properties of four residual-based and one maximum-likelihood-based panel cointegration tests with the help of Monte Carlo simulations. In this study the panel-p, the group-p, the panel-t, the group-t statistics of Pedroni (1999) and the standardized LR-bar statistic of Larsson et al. (2001) are considered. The simulation results indicate that the panel-t and standardized LR-bar statistic have the best size and power properties a mong the five panel cointegration test statistics evaluated. Finally, the Fisher Hypothesis is tested with two different data sets for OECD countries. The results point out the existence of the Fisher relation.
    Keywords: Panel Cointegration tests, Monte Carlo Study, Fisher Hypothesis.
    JEL: C23 C33 C15
    Date: 2007–05
  4. By: Schlicht, Ekkehart
    Abstract: Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. A practical problem arises, however, when some data points are missing. This note proposes a method for coping with this problem.
    Keywords: Trend extraction; missing observations; gaps; Hodrick-Prescott filter; Leser filter; spline; time-series; smoothing; interpolation.
    JEL: C22 C32 C63 C14
    Date: 2007–05
  5. By: Schlicht, Ekkehart
    Abstract: Trend extraction from time series is often performed by using the filter proposed by Leser (1961), also known as the Hodrick-Prescott filter. A practical problem arises, however, when the time series contains structural breaks (such as produced by German unification for German time series, for instance). This note proposes a method for coping with this problem.
    Keywords: Trend extraction; structural break; Hodrick-Prescott filter; Leser filter; spline; time-series; smoothing; interpolation.
    JEL: C22 C32 C63 C14
    Date: 2007–05
  6. By: Zsolt Darvas (Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest); Zoltán Schepp (University of Pécs)
    Abstract: This paper shows that error correction models assuming that long-maturity forward rates are stationary outperform the random walk in out of sample forecasting at forecasting horizons mostly above one year, for US dollar exchange rates against nine industrial countries’ currencies, using the 1990-2006 period for evaluating the out of sample forecasts. The improvement in forecast accuracy of our models is economically significant for most of the exchange rate series, and statistically significant according to a bootstrap test. Our results are robust to the specification of the error correction model and to the underlying data frequency.
    Keywords: bootstrap, forecasting performance, out of sample, random walk, VECM
    JEL: E43 F31 F47
    Date: 2007–05–18
  7. By: Di Iorio, Francesca; Fachin, Stefano
    Abstract: Stability tests for cointegrating coefficients are known to have very low power with small to medium sample sizes. In this paper we propose to solve this problem by extending the tests to dependent cointegrated panels through the stationary bootstrap. Simulation evidence shows that the proposed panel tests improve considerably on asymptotic tests applied to individual series. As an empirical illustration we examined investment and saving for a panel of 14 European countries over the 1960-2002 period. While the individual stability tests, contrary to expectations and graphical evidence, in almost all cases do not reject the null of stability, the bootstrap panel tests lead to the more plausible conclusion that the long-run relationship between these two variables is likely to have undergone a break.
    Keywords: Panel cointegration; stationary bootstrap; parameter stability tests
    JEL: C23
    Date: 2006–07
  8. By: Ozun, Alper; Cifter, Atilla; Yilmazer, Sait
    Abstract: Extreme returns in stock returns need to be captured for a successful risk management function to estimate unexpected loss in portfolio. Traditional value-at-risk models based on parametric models are not able to capture the extremes in emerging markets where high volatility and nonlinear behaviors in returns are observed. The Extreme Value Theory (EVT) with conditional quantile proposed by McNeil and Frey (2000) is based on the central limit theorem applied to the extremes rater than mean of the return distribution. It limits the distribution of extreme returns always has the same form without relying on the distribution of the parent variable. This paper uses 8 filtered EVT models created with conditional quantile to estimate value-at-risk for the Istanbul Stock Exchange (ISE). The performances of the filtered expected shortfall models are compared to those of GARCH, GARCH with student-t distribution, GARCH with skewed student-t distribution and FIGARCH by using alternative back-testing algorithms, namely, Kupiec test (1995), Christoffersen test (1998), Lopez test (1999), RMSE (70 days) h-step ahead forecasting RMSE (70 days), number of exception and h-step ahead number of exception. The test results show that the filtered expected shortfall has better performance on capturing fat-tails in the stock returns than parametric value-at-risk models do. Besides increase in conditional quantile decreases h-step ahead number of exceptions and this shows that filtered expected shortfall with higher conditional quantile such as 40 days should be used for forward looking forecasting.
    Keywords: Value at-Risk; Filtered Expected shortfall; Extreme value theory; emerging markets
    JEL: C32 G0 C52
    Date: 2007–05–22
  9. By: Wolfgang Härdle; Julius Mungo
    Abstract: The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied Volatility Surface (IV S). Practical applications require reducing the dimension and characterize its dynamics through a small number of factors. Such dimension reduction is summarized by a Dynamic Semiparametric Factor Model (DSFM) that characterizes the IV S itself and their movements across time by a multivariate time series of factor loadings. This paper focuses on investigating long range dependence in the factor loadings series. Our result reveals that shocks to volatility persist for a very long time, affecting significantly stock prices. For appropriate representation of the series dynamics and the possibility of improved forecasting, we model the long memory in levels and absolute returns using the class of fractional integrated volatility models that provide flexible structure to capture the slow decaying autocorrelation function reasonably well.
    Keywords: Implied Volatility, Dynamic Semiparametric Factor Modeling, Long Memory, Fractional Integrated Volatility Models.
    JEL: C14 C32 C52 C53 G12
    Date: 2007–05
  10. By: Siem Jan Koopman (Vrije Universiteit Amsterdam); André Lucas (Vrije Universiteit Amsterdam); Marius Ooms (Vrije Universiteit Amsterdam); Kees van Montfort (Vrije Universiteit Amsterdam); Victor van der Geest (Netherlands Institute for the Study of Crime and Law Enforcement (NCSR), Leiden)
    Abstract: We model panel data of crime careers of juveniles from a Dutch Judicial Juvenile Institution. The data are decomposed into a systematic and an individual-specific component, of which the systematic component reflects the general time-varying conditions including the criminological climate. Within a model-based analysis, we treat (1) shared effects of each group with the same systematic conditions, (2) strongly non-Gaussian features of the individual time series, (3) unobserved common systematic conditions, (4) changing recidivism probabilities in continuous time, (5) missing observations. We adopt a non-Gaussian multivariate state space model that deals with all of these issues simultaneously. The parameters of the model are estimated by Monte Carlo maximum likelihood methods. This paper illustrates the methods empirically. We compare continuous-time trends and standard discrete-time stochastic trend specifications. We find interesting common time-variation in the recidivism behavior of the juveniles during a period of 13 years, while taking account of significant heterogeneity determined by personality characteristics and initial crime records.
    Keywords: non-Gaussian state space modeling; nonlinear panel data model; binomial time series; recidivism behavior; continuous time modelling
    JEL: C15 C32 C33 D63
    Date: 2007–03–08
  11. By: Lee, Kiseop; Xu, Mingxin
    Abstract: Ever since the pioneering work of Cox, Ross and Rubinstein, tree models have been popular among asset pricing methods. On the other hand, statistical estimation of parameters of tree models has not been studied as much. In this paper, we use K Means Clustering method to estimate the parameters of multinomial trees. By the weak convergence property of multinomial trees to continuous-time models, we show that this method can be in turn used to estimate parameters in continuous time models, illustrated by an example of jump-diffusion model.
    Keywords: parameter estimation; multinomial tree; jump model; weak convergence; K means clustering
    JEL: G10 C10
    Date: 2007–04–26
  12. By: Laurini, Márcio P. & Valls Pereira, Pedro L.
    Date: 2007–10
  13. By: Michael J. Dueker; Zacharias Psaradakis; Martin Sola; Fabio Spagnolo
    Abstract: In this paper we propose a contemporaneous threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. The model is a multivariate generalization of the contemporaneous threshold autoregressive model introduced by Dueker et al. (2007). A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. The stability and distributional properties of the proposed model are investigated. The C-MSTAR model is also used to examine the relationship between US stock prices and interest rates.
    Date: 2007
  14. By: J. Carlos Escanciano (Indiana University, Bloomington, IN, USA); Jose Olmo (Department of Economics, City University, London)
    Abstract: One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Thereby the correct specification of parametric VaR models became of crucial importance in order to provide accurate and reliable risk measures. If the underlying risk model is not correctly specified, VaR estimates understate/overstate risk exposure. This can have dramatic consequences on stability and reputation of financial institutions or lead to sub-optimal capital allocation. We show that the use of the standard unconditional backtesting procedures to assess VaR models is completely misleading. These tests do not consider the impact of estimation risk and therefore use wrong critical values to assess market risk. The purpose of this paper is to quantify such estimation risk in a very general class of dynamic parametric VaR models and to correct standard backtesting procedures to provide valid inference in specification analyses. A Monte Carlo study illustrates our theoretical findings in finite-samples. Finally, an application to S&P500 Index shows the importance of this correction and its impact on capital requirements as imposed by Basel Accord, and on the choice of dynamic parametric models for risk management.
    Keywords: Backtesting, Basel Accord, Model Risk, Risk management,Value at Risk, Conditional Quantile
    Date: 2007–05
  15. By: Ethan Cohen-Cole; Giulio Zanella
    Abstract: As empirical work in identifying social effects becomes more prevalent, researchers are beginning to struggle with identifying the composition of social interactions within any given reference group. In this paper, we present a simple econometric methodology for the separate identification of multiple social interactions. The setting under which we achieve separation is special, but is likely to be appropriate in many applications.
    Keywords: Social choice
    Date: 2007
  16. By: Christian Huurman (Financial Engineering Associates); Francesco Ravazzolo (Erasmus Universiteit Rotterdam); Chen Zhou (Erasmus Universiteit Rotterdam)
    Abstract: In the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been deregulated. We introduce the weather factor into well-known forecasting models to study its impact. We find that weather has explanatory power for the day-ahead power spot price. Using weather forecasts improves the forecast accuracy, and in particular new models with power transformations of weather forecast variables are significantly better in term of out-of-sample statistics than popular mean reverting models. For different power markets, such as Norway, Eastern Denmark and the Netherlands, we build specific models. The dissimilarity among these models indicates that weather forecasts influence not only the demand of electricity but also the supply side according to different electricity producing methods.
    Keywords: Electricity prices; forecasting; GARCH models; weather forecasts
    JEL: C53 G15 Q40
    Date: 2007–04–25
  17. By: Joseph Byrne; Alexandros Kontonikas; Alberto Montagnoli
    Abstract: In this paper, we examine whether UK inflation is characterized by aggregation bias using three sets of increasingly disaggregated inflation data and a battery of univariate and panel unit root tests. Our results support the existence of aggregation bias since while the unit root hypothesis cannot be rejected for aggregate inflation, it can be rejected for some of its sectoral components, with the rejection frequencies increasing when we use more disaggregate data. Results from structural break analysis indicate that monetary policy shifts are the main factor behind breaks in UK inflation. The panel results typically indicate that when sectoral inflation rates are pooled the unit root hypothesis can be rejected. Our results have important implications for applied econometric analysis, macroeconomic theory and for the conduct of monetary policy.
    Keywords: Inflation, Unit Root, Disaggregation, Structural Breaks, Panel Data
    JEL: C22 C23 E31
    Date: 2007–05

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