nep-ecm New Economics Papers
on Econometrics
Issue of 2006‒11‒18
23 papers chosen by
Sune Karlsson
Orebro University

  1. Forecast Encompassing Tests and Probability Forecasts By Clements, Michael P; Harvey, David I
  2. Exact Elliptical Distributions for Models of Conditionally Random Financial Volatility By George A. Christodoulakis; Stephen E Satchell
  3. Real-time forecasting of GDP based on a large factor model with monthly and quarterly data By Schumacher, Christian; Breitung, Jörg
  4. On Joint Modelling and Testing for Local and Global Spatial Externalities By Zhenlin Yang
  5. Uniform Convergence Rate of the SNP Density Estimator and Testing for Similarity of Two Unknown Densities By Kyoo il Kim
  6. Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation. By Clements, Michael P; Galvão, Ana Beatriz
  7. Testing for unit roots in three-dimensional heterogeneous panels in the presence of cross-sectional dependence By Giulietti, Monica; Otero, Jesús; Smith, Jeremy
  8. Higher Order Bias Correcting Moment Equation for M-Estimation and its Higher Order Efficiency By Kyoo il Kim
  9. Set Inference for Semiparametric Discrete Games By Kyoo il Kim
  10. Semiparametric Estimation of Signaling Games By Kyoo il Kim
  11. Testing Models of Low-Frequency Variability By Ulrich Mueller; Mark W. Watson
  12. Frequency Dependence in Regression Model Coefficients: An Alternative Approach for Modeling Nonlinear Dynamic Relationships in Time Series By Richard A. Ashley.; Randall J. Verbrugge
  13. Beyond Optimal Forecasting By Richard A. Ashley.
  14. Accurate Value-at-Risk Forecast with the (good old) Normal-GARCH Model By Christoph Hartz; Stefan Mittnik; Marc S. Paolella
  15. Nonparametric estimation betas in the Market Model. By Mª Victoria Esteban González; Susan Orbe Mandaluniz
  16. Regime Switching and Artificial Neural Network Forecasting By Eleni Constantinou; Robert Georgiades; Avo Kazandjian; George Kouretas
  17. Extreme value theory approach to simultaneous monitoring and tresholding of multiple risk indicators By Einmahl,John H.J.; Li,Jun; Liu,Regina Y.
  18. Tail Probabilities for Registration Estimators By Thomas Mikosch; Casper G. de Vries
  19. A New Bispectral Test for Nonlinear Serial Dependence By Elena Rusticelli; Richard A. Ashley; Estela Bee Dagum; Douglas M. Patterson
  20. Assessing the Credibility of Instrumental Variables Inference With Imperfect Instruments Via Sensitivity Analysis By Richard A. Ashley.
  21. Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression By Wayne E. Ferson; Sergei Sarkissian; Timothy Simin
  22. Forecasting Euro-Area Variables with German Pre-EMU Data By Ralf Brueggemann; Helmut Luetkepohl; Massimiliano Marcellino
  23. Jointness in Bayesian variable selection with applications to growth regression By Ley, Eduardo; Steel, Mark F. J.

  1. By: Clements, Michael P (Department of Economics, University of Warwick); Harvey, David I (School of Economics, University of Nottingham)
    Abstract: We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecasting models’ parameters on these distributions. The small-sample performance of the various statistics is investigated, both in terms of small numbers of forecasts and model estimation sample sizes. Two empirical applications show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables, and for evaluating survey-based probability forecasts. Probability forecasts ; encompassing tests ; recession probabilities
    JEL: C12 C15 C53
    Date: 2006
  2. By: George A. Christodoulakis (Bank of Greece and Manchester Business School); Stephen E Satchell (Trinity College, University of Cambridge and Bank of Greece)
    Abstract: Assuming the time series of random returns to be jointly elliptical, we derive a relationship between its conditional variance and the probability density function of the conditioning set. In the case that such a relationship is linear in a quadratic form for of the conditioning variables, we show that the probability density function of the conditioning variables is multivariate t. This result is then applied to models of conditionally random volatility and used to derive exact results for the GARCH(p,q) class of processes previously thought to be intractable.
    Keywords: Elliptical Distributions, Financial Asset Returns, Conditional Volatility, GARCH
    JEL: C22 G11 G12
    Date: 2006–01
  3. By: Schumacher, Christian; Breitung, Jörg
    Abstract: This paper discusses a factor model for estimating monthly GDP using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm combined with a principal components estimator. We discuss the in-sample properties of the estimator in real-time environments and methods for out-of-sample forecasting. As an empirical application, we estimate monthly German GDP in real-time, discuss the nowcast and forecast accuracy of the model and the role of revisions. Furthermore, we assess the contribution of timely monthly data to the forecast performance.
    Keywords: monthly GDP, EM algorithm, principal components, factor models
    JEL: C53 E37
    Date: 2006
  4. By: Zhenlin Yang (School of Economics and Social Sciences, Singapore Management University)
    Abstract: This paper concerns the joint modeling, estimation and testing for local and global spatial externalities. Spatial externalities have become in recent years a standard notion of economic research activities in relation to social interactions, spatial spillovers and dependence, etc., and have received an increasing attention by econometricians and applied researchers. While conceptually the principle underlying the spatial dependence is straightforward, the precise way in which this dependence should be included in a regression model is complex. Following the taxonomy of Anselin (2003, International Regional Science Review 26, 153-166), a general model is proposed, which takes into account jointly local and global externalities in both modelled and unmodelled effects. The proposed model encompasses all the models discussed in Anselin (2003). Robust methods of estimation and testing are developed based on Gaussian quasi-likelihood. Large and small sample properties of the proposed methods are investigated.
    Keywords: Asymptotic property, Finite sample property, Quasi-likelihood, Spatial regression models, Robustness, Tests of spatial externalities.
    JEL: C1 C2 C5
    Date: 2006–10
  5. By: Kyoo il Kim (School of Economics and Social Sciences, Singapore Management University)
    Abstract: This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.
    Keywords: SNP Density Estimator, Uniform Convergence Rate, Comparison of Two Densities
    JEL: C12 C14 C16
    Date: 2006–09
  6. By: Clements, Michael P (Department of Economics, University of Warwick); Galvão, Ana Beatriz (Bank of Portugal)
    Abstract: Although many macroeconomic series such as US real output growth are sampled quarterly, many potentially useful predictors are observed at a higher frequency. We look at whether a recently developed mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth and inflation. We carry out a number of related real-time forecast comparisons using various indicators as explanatory variables. We find that MIDAS model forecasts of output growth are more accurate at horizons less than one quarter using coincident indicators ; that MIDAS models are an effective way of combining information from multiple indicators ; and that the forecast accuracy of the unemployment-rate Phillips curve for inflation is enhanced using the MIDAS approach.
    Keywords: Data frequency ; multiple predictors ; combination ; real-time forecasting
    JEL: C51 C53
    Date: 2006
  7. By: Giulietti, Monica (Aston Business School, University of Aston); Otero, Jesús (Facultad de Economía, Universidad del Rosario,); Smith, Jeremy (Department of Economics, University of Warwick)
    Abstract: This paper extends the cross-sectionally augmented IPS (CIPS) test of Pesaran (2006) to a three-dimensional (3D) panel. This 3D-CIPS test is correctly sized in the presence of cross-sectional dependency. Comparing its power performance to that of a bootstrapped IPS (BIPS) test, we find that the BIPS test invariably dominates, although for high levels of cross-sectional dependency the 3D-CIPS test can out-perform the BIPS test.
    Keywords: Heterogeneous dynamic panels ; Monte Carlo ; unit roots ; cross-sectional dependence
    JEL: C12 C15 C22 C23
    Date: 2006
  8. By: Kyoo il Kim (School of Economics and Social Sciences, Singapore Management University)
    Abstract: This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment equation in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically bias-corrected estimator and the alternative estimator and finds that the third-order stochastic expansions of these two estimators are identical. This implies that at least in terms of the third order stochastic expansion, we cannot improve on the simple one-step bias correction by using the bias correction of moment equations. Though the result in this paper is for a fixed number of parameters, our intuition may extend to the analytical bias correction of the panel data models with individual specific effects. Noting the M-estimation can nest many kinds of estimators including IV, 2SLS, MLE, GMM, and GEL, our finding is a rather strong result.
    Keywords: Third-order Stochastic Expansion, Bias Correction, M-estimation
    JEL: C10
    Date: 2006–09
  9. By: Kyoo il Kim (School of Economics and Social Sciences, Singapore Management University)
    Abstract: We consider estimation and inference of parameters in discrete games allowing for multiple equilibria, without using an equilibrium selection rule. We do a set inference while a game model can contain infinite dimensional parameters. Examples can include signaling games with discrete types where the type distribution is nonparametrically specified and entry-exit games with partially linear payoffs functions. A consistent set estimator and a confidence interval of a function of parameters are provided in this paper. We note that achieving a consistent point estimation often requires an information reduction. Due to this less use of information, we may end up a point estimator with a larger variance and have a wider confidence interval than those of the set estimator using the full information in the model. This finding justifies the use of the set inference even though we can achieve a consistent point estimation. It is an interesting future research to compare these two alternatives: CI from the point estimation with the usage of less information vs. CI from the set estimation with the usage of the full information.
    Keywords: Semiparametric Estimation, Set Inference, Infinite Dimensional Parameters, Inequality Moment Conditions, Signaling Game with Discrete Types
    JEL: C13 C14 C35 C62 C73
    Date: 2006–09
  10. By: Kyoo il Kim (School of Economics and Social Sciences, Singapore Management University)
    Abstract: This paper studies an econometric modeling of a signaling game with two players where one player has one of two types. In particular, we develop an estimation strategy that identifies the payoffs structure and the distribution of types from data of observed actions. We can achieve uniqueness of equilibrium using a refinement, which enables us to identify the parameters of interest. In the game, we consider non-strategic public signals about the types. Because the mixing distribution of these signals is nonparametrically specified, we propose to estimate the model using a sieve conditional MLE. We achieve the consistency and the asymptotic normality of the structural parameters estimates. As an alternative, we allow for the possibility of multiple equilibria, without using an equilibrium selection rule. As a consequence, we adopt a set inference allowing for multiplicity of equilibria.
    Keywords: Semiparametric Estimation, Signaling Game, Set Inference, Infinite Dimensional Parame- ters, Sieve Simultaneous Conditional MLE
    JEL: C13 C14 C35 C62 C73
    Date: 2006–09
  11. By: Ulrich Mueller; Mark W. Watson
    Abstract: We develop a framework to assess how successfully standard times eries models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric series. The properties of these weighted averages are then compared to the asymptotic implications of a number of common time series models. We apply the framework to twenty U.S. macroeconomic and financial time series using frequencies lower than the business cycle.
    JEL: C22 E32
    Date: 2006–11
  12. By: Richard A. Ashley.; Randall J. Verbrugge
    Keywords: Phillips Curve, spectral regression, time series analysis
    Date: 2006
  13. By: Richard A. Ashley.
    Keywords: forecasting,forecast loss functions,stochastic dominance.
    Date: 2006
  14. By: Christoph Hartz (University of Munich); Stefan Mittnik (University of Munich, Center for Financial Studies and ifo); Marc S. Paolella (University of Zurich)
    Abstract: A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal-GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and, except for having to choose a window length L for the bias-correction step, fully data driven. The results for several different financial asset returns over a long out-of-sample forecasting period, as well as use of simulated data, strongly support use of the new method, and the performance is not sensitive to the choice of L.
    Keywords: Bootstrap, GARCH, Value-at-Risk
    JEL: C22 C53 C63 G12
    Date: 2006–11–03
  15. By: Mª Victoria Esteban González (F. C.C. Económicas y Empresariales. UPV/EHU); Susan Orbe Mandaluniz (F. C.C. Económicas y Empresariales. UPV/EHU)
    Abstract: In this study an alternative nonparametric estimator to the Fama and MacBeth approach for the CAPM estimation is proposed. Betas and risk premiums are estimated simultaneously in order to increase the explanatory power of the proxy for betas. A data driven method is proposed for selecting the smoothness degrees, which are directly related to the subsample sizes. Based on this relation, the traditional estimator is obtained as a particular case. Contrary to the results obtained in other studies our empirical evidence for Spanish market data is favorable to the CAPM.
    Keywords: smoothed rolling, betas, CAPM
    JEL: C14 G12
    Date: 2006–11–10
  16. By: Eleni Constantinou (Department of Accounting and Finance, The Philips College, 4-6 Lamias Street, CY-2100, Nicosia,); Robert Georgiades (Department of Accounting and Finance, The Philips College, 4-6 Lamias Street, CY-2100, Nicosia,); Avo Kazandjian (Department of Business Studies, The Philips College, 4-6 Lamias Street, CY-2100, Nicosia, Cyprus.); George Kouretas (Department of Economics, University of Crete, Greece)
    Abstract: This paper provides an analysis of regime switching in volatility and out-of-sample forecasting of the Cyprus Stock Exchange using daily data for the period 1996-2002. We first model volatility regime switching within a univariate Markov-Switching framework. Modelling stock returns within this context can be motivated by the fact that the change in regime should be considered as a random event and not predictable. The results show that linearity is rejected in favour of a MS specification, which forms statistically an adequate representation of the data. Two regimes are implied by the model; the high volatility regime and the low volatility one and they provide quite accurately the state of volatility associated with the presence of a rational bubble in the capital market of Cyprus. Another implication is that there is evidence of regime clustering. We then provide out-of-sample forecasts of the CSE daily returns using two competing non-linear models, the univariate Markov Switching model and the Artificial Neural Network Model. The comparison of the out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano (1995) test and forecast encompassing, using the Clements and Hendry (1998) test. The results suggest that both non-linear models equivalent in forecasting accuracy and forecasting encompassing and therefore on forecasting performance.
    Keywords: Regime switching, artificial neural networks, stock returns, forecast
    JEL: G
    Date: 2005–01
  17. By: Einmahl,John H.J.; Li,Jun; Liu,Regina Y. (Tilburg University, Center for Economic Research)
    Abstract: Risk assessments often encounter extreme settings with very few or no occurrences in reality. Inferences about risk indicators in such settings face the problem of insufficient data. Extreme value theory is particularly well suited for handling this type of problems. This paper uses a multivariate extreme value theory approach to establish thresholds for signaling levels of risk in the context of simultaneous monitoring of multiple risk indicators. The proposed threshold system is well justified in terms of extreme multivariate quantiles, and its sample estimator is shown to be consistent. As an illustration, the proposed approach is applied to developing a threshold system for monitoring airline performance measures. This threshold system assigns different risk levels to observed airline performance measures. In particular, it divides the sample space into regions with increasing levels of risk. Moreover, in the univariate case, such a thresholding technique can be used to determine a suitable cut-off point on a runway for holding short of landing aircrafts. This cut-off point is chosen to ensure a certain required level of safety when allowing simultaneous operations on two intersecting runways in order to ease air traffic congestion.
    Keywords: Extreme value theory;extreme quantile;multiple risk indicators;multivariate quantile;rare event;statistics of extremes;threshold system
    JEL: C13 C14 C15 L93
    Date: 2006
  18. By: Thomas Mikosch (University of Copenhagen); Casper G. de Vries (Erasmus Universiteit Rotterdam)
    Abstract: Estimators of regression coefficients are known to be asymptotically normally distributed, provided certain regularity conditions are satisfied. In small samples and if the noise is not normally distributed, this can be a poor guide to the quality of the estimators. The paper addresses this problem for small and medium sized samples and heavy tailed noise. In particular, we assume that the noise is regularly varying, i.e., the tails of the noise distribution exhibit power law behavior. Then the distributions of the regression estimators are heavy tailed themselves. This is relavant for regressions involving financial data which are typically heavy tailed. In medium sized samples and with some dependency in the noise structure, the regression coefficient estimators can deviate considerably from their true values. The relevance of the theory is demonstrated for the highly variable cross country estimates of the expectations coefficient in yield curve regressions.
    Keywords: heavy tails; regression estimators; expectations hypothesis
    Date: 2006–10–06
  19. By: Elena Rusticelli; Richard A. Ashley; Estela Bee Dagum; Douglas M. Patterson
    Keywords: Bispectrum, nonlinearity, time series analysis
    Date: 2006
  20. By: Richard A. Ashley.
    Keywords: instrumental variables,sensitivity analysis
    Date: 2006
  21. By: Wayne E. Ferson; Sergei Sarkissian; Timothy Simin
    Abstract: This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. We find that the regressions are reasonably well specified for conditional betas, even in settings where simple predictive regressions are severely biased. However, there are biases in estimates of the conditional alphas. When time-varying alphas are suppressed and only time-varying betas are considered, the betas become baised. Previous studies overstate the significance of time-varying alphas.
    JEL: C5 G1
    Date: 2006–10
  22. By: Ralf Brueggemann; Helmut Luetkepohl; Massimiliano Marcellino
    Abstract: It is investigated whether Euro-area variables can be forecast better based on synthetic time series for the pre-Euro period or by using just data from Germany for the pre-Euro period. Our forecast comparison is based on quarterly data for the period 1970Q1 - 2003Q4 for ten macroeconomic variables. The years 2000 - 2003 are used as forecasting period. A range of different univariate forecasting methods is applied. Some of them are based on linear autoregressive models and we also use some nonlinear or time-varying coefficient models. It turns out that most variables which have a similar level for Germany and the Euro-area such as prices can be better predicted based on German data while aggregated European data are preferable for forecasting variables which need considerable adjustments in their levels when joining German and EMU data. These results suggest that for variables which have a similar level for Germany and the Euro-area it may be reasonable to consider the German pre-EMU data for studying economic problems in the Euro-area.
    Keywords: Aggregation, forecasting, European monetary union, constructing EMU data
    JEL: C22 C53
    Date: 2006
  23. By: Ley, Eduardo; Steel, Mark F. J.
    Abstract: The authors present a measure of jointness to explore dependence among regressors in the context of Bayesian model selection. The jointness measure they propose equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. They show its application in cross-country growth regressions using two data-sets from the model-averaging growth literature.
    Keywords: Statistical & Mathematical Sciences,Climate Change,Educational Technology and Distance Education,Economic Theory & Research,Pro-Poor Growth and Inequality
    Date: 2006–11–01

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