nep-ecm New Economics Papers
on Econometrics
Issue of 2005‒10‒22
nineteen papers chosen by
Sune Karlsson
Orebro University

  1. Panel Cointegration Tests with Deterministic Trends and Structural Breaks By Westerlund, Joakim; Edgerton , David
  2. "Goodness-of-Fit Tests for Symmetric Stable Distributions - Empirical Characteristic Function Approach" By Muneya Matsui; Akimichi Takemura
  3. Joint change point estimation in regression coeffcients and variances of the errors of a linear model By Oleg Glouchakov
  4. Forecast Combination and Model Averaging using Predictive Measures By Eklund, Jana; Karlsson, Sune
  5. A Bayesian Approach to Model Uncertainty By Charalambos G. Tsangarides
  6. Forecasting Thailand's Core Inflation By Tao Sun
  7. Global and Partial Errors in Stratified and Clustering Sampling By Giovanna Nicolini; Anna Lo Presti
  8. A Single-Frame Multiplicity Estimator for Multiple Frame Survey By Fulvia Mecatti
  9. Empirical Modeling of Contagion: A Review of Methodologies By Mardi Dungey; Renee Fry; Vance Martin; Brenda González-Hermosillo
  10. Estimation of Economic Growth in France Using Business Survey Data By Alain N. Kabundi
  11. Autocorrelation-Corrected Standard Errors in Panel Probits: An Application to Currency Crisis Prediction By Rebecca N. Coke; Andrew Berg
  12. A Proposal for Setting-up Indicators in the Presence of Missing Data: the Case of Vulnerability Indicators By Pieralda Ferrari; Paola Annoni; Sergio Urbisci
  13. How Stable is the Forecasting Performance of the Yield Curve for Outpot Growth? By Rossi, Barbara; Giacomini, Raffaella
  14. Oriented stochastic data envelopment models: Ranking comparison to stochastic frontier approach By Frantisek Brazdik
  15. Dynamic Discrete Choice and Dynamic Treatment Effects By James J. Heckman; Salvador Navarro
  17. Blinder-Oaxaca Decomposition for Tobit Models By Thomas K. Bauer; Mathias Sinning
  18. Nelson-Plosser Revisited: the ACF Approach By Karim Abadir, Giovanni Caggiano and Gabriel Talmain
  19. Best Nonparametric Bounds on Demand Responses By Richard Blundell; Martin Browning; Ian Crawford

  1. By: Westerlund, Joakim (Department of Economics, Lund University); Edgerton , David (Department of Economics, Lund University)
    Abstract: This paper proposes Lagrange multiplier (LM) based tests for the null hypothesis of no cointegration in panel data. The tests are general enough to allow for heteroskedastic and serially correlated errors, individual specific time trends, and a single structural break in both the intercept and slope of each regression, which may be located different dates for different individuals. The limiting distributions of the test statistics are derived, and are found to be standard normal and free of nuisance parameters under the null. In particular, the distributions are found to be invariant not only with respect to trend and structural break, but also with respect to the presence of stochastic regressors. A small Monte Carlo study is also conducted to investigate the small-sample properties of the tests. The results reveal that the tests have small size distortions and good power even in very small samples.
    Keywords: Panel Cointegration; Residual-Based Cointegration Test; Structural Break; Deterministic Trend; LM Principle
    JEL: C12 C32 C33
    Date: 2005–10–11
  2. By: Muneya Matsui (Graduate School of Economics, University of Tokyo); Akimichi Takemura (Graduate School of Information Science and Technology, University of Tokyo)
    Abstract: We consider goodness-of fit tests of symmetric stable distributions based on weighted integrals of the squared distance between the empirical characteristic function of the standardized data and the characteristic function of the standard symmetric stable distribution with the characteristic exponentƒ¿ estimated from the data. We treat ƒ¿ as an unknown parameter, but for theoretical simplicity we also consider the case that ƒ¿ is fixed. For estimation of parameters and the standardization of data we use maximum likelihood estimator (MLE) and an equivariant integrated squared error estimator (EISE) which minimizes the weighted integral. We derive the asymptotic covariance function of the characteristic function process with parameters estimated by MLE and EISE. For the case of MLE, the eigenvalues of the covariance function are numerically evaluated and asymptotic distribution of the test statistic is obtained using complex integration. Simulation studies show that the asymptotic distribution of the test statistics is very accurate. We also present a formula of the asymptotic covariance function of the characteristic function process with parameters estimated by an efficient estimator for general distributions.
    Date: 2005–10
  3. By: Oleg Glouchakov (Department of Economics, York University)
    Abstract: This paper describes how to estimate the alternative model that admits a one-time break in coeffcients of a linear regression function and variances of the errors. Bai and Perron (1998) introduced an estimation and testing procedure for multiple breaks in regression coeffcients. We limit the number of breaks to one but extend the estimation to the alternative model that allows for variances of the errors to break too. The method is based on application of specific objective functions in conjunction with the tests ofstructural change. In particular, sup-Wald test of Andrews (1993) can be used to detect structural breaks. Andrews and Ploberger (1994) introduce optimal tests of constancy of model parameters when the change point is unknown. However, these tests lose their power optimality properties when the break happens in both the mean and the variance of a process. For such an alternative we introduce a statistic with a modified measure of a distance between model parameters before and after the break. In a Monte-Carlo experiment we show that the power of the corresponding sup-test dominates that of the sup-Wald test. If a change point is known, then the test based on this statistic is uniformly more powerful than the Wald test.
    Keywords: Change point, asymptotic distribution, Brownian bridge, Brownian motion, Wald statistic, parameter instability, structural change, estimation of the alternative model
    Date: 2005–05
  4. By: Eklund, Jana (Department of Economic Statistics); Karlsson, Sune (Department of Economics, Statistics and Informatics)
    Abstract: We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting and improves forecast performance. For the predictive likelihood we show analytically that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and an application to forecasts of the Swedish inflation rate where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.
    Keywords: Bayesian model averaging; Predictive likelihood; Partial Bayes factor; Training sample; Inflation rate
    JEL: C11 C51 C52
    Date: 2005–09–01
  5. By: Charalambos G. Tsangarides
    Abstract: This paper develops the theoretical background for the Limited Information Bayesian Model Averaging (LIBMA). The proposed approach accounts for model uncertainty by averaging over all possible combinations of predictors when making inferences about the variables of interest, and it simultaneously addresses the biases associated with endogenous and omitted variables by incorporating a panel data systems Generalized Method of Moments estimator. Practical applications of the developed methodology are discussed, including testing for the robustness of explanatory variables in the analyses of the determinants of economic growth and poverty.
    Keywords: Forecasting models , Economic models ,
    Date: 2004–05–11
  6. By: Tao Sun
    Abstract: This paper develops an approach for forecasting in Thailand core inflation. The key innovation is to anchor the projections derived from the short-term time-series properties of core inflation to its longer-run evolution. This involves combining a short-term model, which attempts to distill the forecasting power of a variety of monthly indicators purely on goodness-of-fit criteria, with an equilibrium-correction model that pins down the convergence of core inflation to its longer-run structural determinants. The result is a promising model for forecasting Thai core inflation over horizons up to 10, 24, and 55 months, based on a root mean-squared error criterion as well as a mean absolute error criterion.
    Keywords: Forecasting models , Thailand , Inflation ,
    Date: 2004–06–14
  7. By: Giovanna Nicolini (Department of Economics, Business and Statistics); Anna Lo Presti (Dipartimento di Statistica e Matematica Applicata alle Scienze Umane, Università di Torino)
    Abstract: In this paper we split up the sampling error occurred in stratified and clustering sampling, called global error and measured by the variance of estimator, in many partial errors each one referred to a single stratum or cluster. In particular, we study, for clustering sampling, the empirical distribution of the homogeneity coefficient that is very important for settlement of partial errors.
    Keywords: Global Sampling Error, Partial Sampling Errors, Homogeneity Coefficient, Stratified Sampling, Clustering Sampling,
    Date: 2005–06–28
  8. By: Fulvia Mecatti (University of Milan-Bicocca)
    Abstract: Multiple Frame Survey has been originally proposed, according to an optimality approach, in order to persecute survey cost savings, especially in the case of a complete list available but expensive to sample. In the modern sampling practice it is frequent the case where a total and up-to-date list of units, to be used as sampling frame, is not available or it may not be built unless expensive or unfeasible screening of the target population. Instead, a set of lists singularly partial, usually overlapping, with union offering an adequate coverage of the target population, can be available. Thus the collection of the partial lists can be used as Multiple Frame. Literature about Multiple Frame estimation theory mainly concentrates over the Dual Frame case and it is only rarely concerned with the important practical issue of the variance estimation. By using a multiplicity approach a Single Frame estimator is proposed. The new estimator naturally applies to any number of frame and it is very simple so that its variance is given exactly and easily estimated.
    Keywords: Center Sampling, Difficult-to-Sample Populations, Estimation Theory, Finite Population Sampling, Variance Estimation,
    Date: 2005–09–09
  9. By: Mardi Dungey; Renee Fry; Vance Martin; Brenda González-Hermosillo
    Abstract: The existing literature suggests a number of alternative methods to test for the presence of contagion during financial market crises. This paper reviews those methods and shows how they are related in a unified framework. A number of extensions are also suggested that allow for multivariate testing, endogeneity issues, and structural breaks.
    Keywords: Financial crisis , Economic models ,
    Date: 2004–05–20
  10. By: Alain N. Kabundi
    Abstract: This paper proposes a new way of computing a coincident indicator for economic activity in France using data from business surveys. We use the generalized dynamic factor model à la Forni and others (2000) to extract common components from a large number of survey observations. The results obtained show that the resulting indicator forecasts economic activity with a relatively high degree of accuracy before the release of actual data.
    Keywords: Economic growth , France , Forecasting models , Data collection , Data analysis ,
    Date: 2004–05–12
  11. By: Rebecca N. Coke; Andrew Berg
    Abstract: Many estimates of early-warning-system (EWS) models of currency crisis have reported incorrect standard errors because of serial correlation in the context of panel probit regressions. This paper documents the magnitude of the problem, proposes and tests a solution, and applies it to previously published EWS estimates. We find that (1) the uncorrected probit estimates substantially underestimate the true standard errors, by up to a factor of four; (2) a heteroskedasicity- and autocorrelation-corrected (HAC) procedure produces accurate estimates; and (3) most variables from the original models remain significant, though substantially less so than had been previously thought.
    Keywords: Crisis prevention , Currencies , Economic models ,
    Date: 2004–03–23
  12. By: Pieralda Ferrari (department of economics, business and statistics); Paola Annoni; Sergio Urbisci
    Abstract: A procedure for the construction of an indicator in the presence of structured missing data is proposed. In particular, we face the problem of creating a 'measure' of the damage degree of valuable historical-architectonical buildings on the basis of the observation of several ordinal variables. Our proposal is the jointly use of Nonlinear PCA and an imputation method for missing data treatment. The adopted procedure can be generally applied when an indicator is needed on the basis of the observation of ordinal, but also nominal or numerical, variables, which are deeply interrelated and are affected by systematic missing data. It has the nice feature of treating missing data according to the relevance of variables affected by missing observations and, at the same time, it preserves all the properties of Nonlinear PCA without missing data. Furthermore, the method provides category quantifications and variable loadings that could be used for future inventory of buildings (in general of 'units') not included in the initial survey.
    Keywords: nonlinear MVA, quantification of ordinal variables, optimal scaling, measurement.,
    Date: 2005–04–26
  13. By: Rossi, Barbara; Giacomini, Raffaella
    Abstract: We provide an extensive evaluation of the predictive performance of the U.S. yield curve for U.S. GDP growth by using a new test for forecast breakdown as well as a variety of in-sample and out-of-sample testing procedures. Empirical research over the past decades uncovered a strong predictive relationship between the yield curve and output growth. However, the parameter estimates that describe this empirical relationship were not stable over time. We document the existence of a forecast breakdown in this relationship over the past three decades, and find it relevant especially in the seventies and eighties. We also provide empirical support for the theoretical conjecture that the cause of the forecast failure is closely linked to changes in the monetary policy of the Fed.
    JEL: C22 C52 C53
  14. By: Frantisek Brazdik
    Abstract: Results of data envelopment analysis sensitively respond to stochastic noise in the data. In this paper, by introduction of output augmentation and input reduction I extend additive models for stochastic data envelopment analysis (SDEA), which were developed by Li (1998) to handle the noise in the data. Applying the linearization procedure by Li (1998) the linearized versions of models are derived. In the empirical part of this work, the effi- ciency scores of Indonesian rice farms are computed. The computed scores are compared to the stochastic frontier approach scores by Druska and Horrace (2004) and weak ranking consistency with results of stochastic frontier method is observed.
    Keywords: Stochastic data envelopment analysis, linear programming, efficiency, rice farm.
    JEL: C14 C61 L23 Q12
    Date: 2005–08
  15. By: James J. Heckman (University of Chicago and IZA Bonn); Salvador Navarro (University of Wisconsin-Madison)
    Abstract: This paper considers semiparametric identiÞcation of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic treatment model using the structural model as a benchmark. For the structural model we show the gains from using cross equation restrictions connecting choices to associated measurements and outcomes. In the dynamic discrete choice model, we identify both subjective and objective outcomes, distinguishing ex post and ex ante outcomes. We show how to identify agent information sets.
    Keywords: dynamic treatment effects, dynamic discrete choice, semiparametric identification
    JEL: C31
    Date: 2005–10
  16. By: Bharat Barot (National Institute of Economic Research)
    Abstract: This study evaluates the performance of the eight most important Swedish domestic forecasters of real GDP-growth, CPI-inflation and unemployment for the sample period 1993-2001. The evaluation is based on the following measures: mean absolute error, the root mean square error, bias and finally directional accuracy. The forecasts are even compared to naive random walk and random walk with drift models. The results indicate that the current forecasts compared to the year ahead forecasts decline over the forecasting horizons as more information becomes available. The results with respect to the directional accuracy indicate that we are equally good/bad in predicting the directional accuracy for all three macro aggregates. According to the comparisons with the naive random walk model six out of seven Swedish CPI-inflation forecasters were outperformed by the naive random walk model. Tests of bias indicate that the Swedish forecasters underestimate GDP-growth and overestimate CPI-inflation and the unemployment rate for the sample period. All the Swedish forecasters have been successful in predicting the downward trend in CPI-inflation and the unemployment rate. The performance of the Swedish domestic forecasters is better using preliminary GDP-growth outcomes than final. The performance for the current year forecasts is better than the year ahead forecasts for all three macro economic variables. Revisions are positively biased. Key words Mean absolute error, root mean square error, directional accuracy, bias, revisions, final respective preliminary outcomes, Theil index, naïve forecasts
    JEL: E
    Date: 2005–10–19
  17. By: Thomas K. Bauer (RWI Essen, University of Bochum, CEPR London and IZA Bonn); Mathias Sinning (RWI Essen)
    Abstract: In this paper, a decomposition method for Tobit-models is derived, which allows the differences in a censored outcome variable between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part attributable to differences in the estimated coefficients. The method is applied to a decomposition of the gender wage gap using German data.
    Keywords: Blinder-Oaxaca decomposition, Tobit model, wage gap
    JEL: C24 J31
    Date: 2005–10
  18. By: Karim Abadir, Giovanni Caggiano and Gabriel Talmain
    Abstract: We detect a new stylized fact about the common dynamics of macroeconomic and financial aggregates. The rate of decay of the memory (or persistence) of these series is depicted by their autocorrelation functions (ACFs), and they all fit very closely a parsimonious four-parameter functional form that we present. Not only does our formula fit the data better than the ones that arise from autoregressive models, but it also yields the correct shape of the ACF. This can help policymakers understand the lags with which an economy evolves, and its turning points.
    JEL: E32
  19. By: Richard Blundell (University College London); Martin Browning (Department of Economics, University of Copenhagen); Ian Crawford (University of Surrey)
    Abstract: This paper uses revealed preference inequalities to provide tight nonparametric bounds on consumer responses to price changes. Price responses are allowed to vary nonparametrically across the income distribution by exploiting micro data on consumer expenditures and incomes over a finite set of discrete relative price changes. This is achieved by combining the theory of revealed preference with the semiparametric estimation of consumer expansion paths (Engel curves). We label these expansion path based bounds as E-bounds. Deviations from revealed preference restrictions are measured by preference perturbations which are shown to usefully characterise taste change.
    Keywords: demand responses; relative prices; revealed preference; semiparametric regression; changing tastes
    JEL: D12 C14 C43
    Date: 2005–09

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