nep-ecm New Economics Papers
on Econometrics
Issue of 2006‒07‒21
thirteen papers chosen by
Sune Karlsson
Orebro University

  1. Testing Dependence among Serially Correlated Multi-Category Variables By M. Hashem Pesaran; Allan Timmermann
  2. Stochastic population forecasts using functional data models for mortality, fertility and migration By Rob J Hyndman; Heather Booth
  3. Skills, On determining the importance of a regressor with small and undersized samples By Peter Sandholt Jensen; Allan H. Würtz
  4. Half-Life Estimation based on the Bias-Corrected Bootstrap: A Highest Density Region Approach By Jae Kim; Param Silvapulle; Rob J. Hyndman
  5. Functional Central Limit Theorems for Dependent, Heterogeneous Tail Arrays with Applications By Jonathan Hill
  6. Asymptotically Nuisance-Parameter-Free Consistent Tests of Lp-Functional Form By Jonathan Hill
  7. Properties of the Sieve Bootstrap for Fractionally Integrated and Non-Invertible Processes By D. S. Poskitt
  8. Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions By Heather Booth; Rob J Hyndman; Leonie Tickle; Piet de Jong
  9. The Finite-Sample Properties of Autoregressive Approximations of Fractionally-Integrated and Non-Invertible Processes By S. D. Grose; D. S. Poskitt
  10. Fixed and Mixed Effects Models in Meta-Analysis By Spyros Konstantopoulos
  11. On Compilation of Long Term Series of GDP for the Former USSR Republics By Youri N. Ivanov
  12. Forecasting Stock Price Changes: Is it Possible? By Pedro N. Rodríguez,; Simón Sosvilla-Rivero
  13. Forecasting inflation with an uncertain output gap By Bjørnland, Hilde C.; Brubakk, Leif; Jore, Anne Sofie

  1. By: M. Hashem Pesaran (Cambridge University and IZA Bonn); Allan Timmermann (University of California, San Diego)
    Abstract: The contingency table literature on tests for dependence among discrete multi-category variables is extensive. Existing tests assume, however, that draws are independent, and there are no tests that account for serial dependencies - a problem that is particularly important in economics and finance. This paper proposes a new test of independence based on the maximum canonical correlation between pairs of discrete variables. We also propose a trace canonical correlation test using dynamically augmented reduced rank regressions or an iterated weighting method in order to account for serial dependence. Such tests are useful, for example, when testing for predictability of one sequence of discrete random variables by means of another sequence of discrete random variables as in tests of market timing skills or business cycle analysis. The proposed tests allow for an arbitrary number of categories, are robust in the presence of serial dependencies and are simple to implement using multivariate regression methods. Monte Carlo experiments show that the proposed tests have good finite sample properties. An empirical application to survey data on forecasts of GDP growth demonstrates the importance of correcting for serial dependencies in predictability tests.
    Keywords: contingency tables, canonical correlations, serial dependence, tests of predictability
    JEL: C12 C22 C42 C52
    Date: 2006–07
  2. By: Rob J Hyndman; Heather Booth
    Abstract: Age-sex-specific population forecasts are derived through stochastic population renewal using forecasts of mortality, fertility and net migration. Functional data models with time series coefficients are used to model age-specific mortality and fertility rates. As detailed migration data are lacking, net migration by age and sex is estimated as the difference between historic annual population data and successive populations one year ahead derived from a projection using fertility and mortality data. This estimate, which includes error, is also modeled using a functional data model. The three models involve different strengths of the general Box-Cox transformation chosen to minimise out-of-sample forecast error. Uncertainty is estimated from the model, with an adjustment to ensure the one-step-forecast variances are equal to those obtained with historical data. The three models are then used in the Monte Carlo simulation of future fertility, mortality and net migration, which are combined using the cohort-component method to obtain age-specific forecasts of the population by sex. The distribution of forecasts provides probabilistic prediction intervals. The method is demonstrated by making 20-year forecasts using Australian data for the period 1921-2003.
    Keywords: Fertility forecasting, functional data, mortality forecasting, net migration, nonparametric smoothing, population forecasting, principal components, simulation.
    JEL: J11 C53 C14 C32
    Date: 2006–05
  3. By: Peter Sandholt Jensen; Allan H. Würtz (Department of Economics, University of Aarhus, Denmark)
    Abstract: A problem encountered in, for instance, growth empirics is that the number of explanatory variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to determine the importance of a variable of interest. We prove identifying assumptions under which the problem is not ill-posed. Under these assumptions, we derive properties of the most commonly used methods: Extreme bounds analysis, Sala-i-Martin’s method, BACE, generalto- specific, minimum t-statistics, BIC and AIC. We propose a new method and show that it has good finite sample properties.
    Keywords: AIC, BACE, BIC, extreme bounds analysis, general-to-specific, identification, ill-posed inverse problem, robustness, sensitivity analysis
    JEL: C12 C51 C52
    Date: 2006–07–14
  4. By: Jae Kim; Param Silvapulle; Rob J. Hyndman
    Abstract: The half-life is defined as the number of periods required for the impulse response to a unit shock to a time series to dissipate by half. It is widely used as a measure of persistence, especially in international economics to quantify the degree of mean reversion of the deviation from an international parity condition. Several studies have proposed bias-corrected point and interval estimation methods. However, they have found that the confidence intervals are rather uninformative with their upper bound being either extremely large or infinite. This is largely due to the distribution of the half-life estimator being heavily skewed and multi-modal. In this paper, we propose a bias-corrected bootstrap procedure for the estimation of half-life, adopting the highest density region (HDR) approach to point and interval estimation. Our Monte Carlo simulation results reveal that the bias-corrected bootstrap HDR method provides an accurate point estimator, as well as tight confidence intervals with superior coverage properties to those of its alternatives. As an application, the proposed method is employed for half-life estimation of the real exchange rates of seventeen industrialized countries. The results indicate much faster rates of mean-reversion than those reported in previous studies.
    Keywords: Autoregressive Model, Bias-correction, Bootstrapping, Confidence interval, Half-life, Highest density region.
    JEL: C15 C22 F37
    Date: 2006–06
  5. By: Jonathan Hill (Department of Economics, Florida International University)
    Abstract: In this paper we establish functional central limit theorems for a broad class of dependent, heterogeneous processes that includes popularly studied tail arrays {X_(n,t)} in the extreme-value literature. We assume sup_t(E|X_(n,t)|^r)^(1/r)=O(n^(-a(r))) for some r>=2 and some function 0<a(r)<=1/2 capturing extremal exceedances, tail empirical processes and tail empirical quantile processes. We trim dependence assumptions down to a minimum by constructing an extremal version of the near-epoch-dependence property to hold exclusively in the extreme support of the distribution. Our theory can be used to characterize the functional limit distributions of a popular tail index estimator, the tail quantile function, and multivariate extremal dependence measures under substantially general conditions.
    Keywords: Funct ional central limit theorem, extremal processes, tail empirical process, cadlag space, mixingale, near-epoch-dependence, regular variation, Hill estimator, tail dependence
    JEL: C19 C22
    Date: 2006–07
  6. By: Jonathan Hill (Department of Economics, Florida International University)
    Abstract: We develop a consistent conditional moment test of Lp-best predictor functional form, 1<p<=2. Our main result is a reduction of the nuisance parameter space to the set of integers which greatly simplifies asymptotic theory, and allows for removal of the nuisance parameter in a mechanical fashion. Our results provide a fresh vantage into why Bierens’ (1990) moment condition works, and uncovers a new class of weights which sharply contrasts with Stinchcombe and White’s (1997) weight classification (real analytic and non-polynomial). The computation of a weighted-Average CM statistic is easy and asymptotically nuisance parameter free be cause it incorporate s all possible nuisance paramete r values. Our test serves as a consistent model check in Lp-regression environments. Finally, we provide a simple nuisance parameter free series expansion of the best Lp-predictor.
    Keywords: nonlinear regression models, consistent conditional moment test, nuisance parameter-free test, Lp-best predictor
    JEL: C12 C45 C52
    Date: 2006–07
  7. By: D. S. Poskitt
    Abstract: In this paper we will investigate the consequences of applying the sieve bootstrap under regularity conditions that are sufficiently general to encompass both fractionally integrated and non-invertible processes. The sieve bootstrap is obtained by approximating the data generating process by an autoregression whose order h increases with the sample size T. The sieve bootstrap may be particularly useful in the analysis of fractionally integrated processes since the statistics of interest can often be non-pivotal with distributions that depend on the fractional index d. The validity of the sieve bootstrap is established and it is shown that when the sieve bootstrap is used to approximate the distribution of a general class of statistics admitting an Edgeworth expansion then the error rate achieved is of order <em>O</em>(<em>T</em><sup> β+d-1</sup>), for any β > 0. Practical implementation of the sieve bootstrap is considered and the results are illustrated using a canonical example.
    Keywords: Autoregressive approximation, fractional process, non-invertibility, rate of convergence, sieve bootstrap.
    JEL: C15 C22
    Date: 2006–07
  8. By: Heather Booth; Rob J Hyndman; Leonie Tickle; Piet de Jong
    Abstract: We compare the short- to medium-term accuracy of five variants or extensions of the Lee-Carter method for mortality forecasting. These include the original Lee-Carter, the Lee-Miller and Booth-Maindonald-Smith variants, and the more flexible Hyndman-Ullah and De Jong-Tickle extensions. These methods are compared by applying them to sex-specific populations of 10 developed countries using data for 1986-2000 for evaluation. All variants and extensions are more accurate than the original Lee-Carter method for forecasting log death rates, by up to 61%. However, accuracy in log death rates does not necessarily translate into accuracy in life expectancy. There are no significant differences among the five methods in forecast accuracy for life expectancy.
    Keywords: Functional data, Lee-Carter method, mortality forecasting, nonparametric smoothing, principal components, state space.
    JEL: J11 C53 C14 C32
    Date: 2006–05
  9. By: S. D. Grose; D. S. Poskitt
    Abstract: This paper investigates the empirical properties of autoregressive approximations to two classes of process for which the usual regularity conditions do not apply; namely the non-invertible and fractionally integrated processes considered in Poskitt (2006). In that paper the theoretical consequences of fitting long autoregressions under regularity conditions that allow for these two situations was considered, and convergence rates for the sample autocovariances and autoregressive coefficients established. We now consider the finite-sample properties of alternative estimators of the AR parameters of the approximating AR(h) process and corresponding estimates of the optimal approximating order h. The estimators considered include the Yule-Walker, Least Squares, and Burg estimators.
    Keywords: Autoregression, autoregressive approximation, fractional process,
    JEL: C14 C22 C53
    Date: 2006–06
  10. By: Spyros Konstantopoulos (Northwestern University and IZA Bonn)
    Abstract: The last three decades the accumulation of quantitative research evidence has led to the development of systematic methods for combining information across samples of related studies. Although a few methods have been described for accumulating research evidence over time, meta-analysis is widely considered as the most appropriate statistical method for combining evidence across studies. This study reviews fixed and mixed effects models for univariate and multivariate meta-analysis. In addition, the study discusses specialized software that facilitates the statistical analysis of meta-analytic data.
    Keywords: meta-analysis, mixed models, multivariate analysis
    Date: 2006–07
  11. By: Youri N. Ivanov
    Date: 2006–07
  12. By: Pedro N. Rodríguez,; Simón Sosvilla-Rivero
    Abstract: We examine the relation between monthly stock returns and lagged publicly available information. Our primary objective is to determine whether the variables proposed in the literature to predict the equity premium contain incremental information to an investor. We find that certain variables do provide incremental information and may have some practical value. Although this not necessarily imply that return-forecasting models may be used to predict future stock returns, some model specifications may be used to predict future stock movements.
  13. By: Bjørnland, Hilde C. (Dept. of Economics, University of Oslo); Brubakk, Leif (Norges Bank); Jore, Anne Sofie (Norges Bank)
    Abstract: The output gap (measuring the deviation of output from its potential) is a crucial concept in the monetary policy framework, indicating demand pressure that generates inflation. The output gap is also an important variable in itself, as a measure of economic fluctuations. However, its definition and estimation raise a number of theoretical and empirical questions. This paper evaluates a series of univariate and multivariate methods for extracting the output gap, and compares their value added in predicting inflation. The multivariate measures of the output gap have by far the best predictive power. This is in particular interesting, as they use information from data that are not revised in real time. We therefore compare the predictive power of alternative indicators that are less revised in real time, such as the unemployment rate and other business cycle indicators. Some of the alternative indicators do as well, or better, than the multivariate output gaps in predicting inflation. As uncertainties are particularly pronounced at the end of the calculation periods, assessment of pressures in the economy based on the uncertain output gap could benefit from being supplemented with alternative indicators that are less evised in real time.
    Keywords: Output gap; real time indicators; forecasting; Phillips curve
    JEL: C32 E31 E32 E37
    Date: 2006–05–04

This nep-ecm issue is ©2006 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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