nep-ecm New Economics Papers
on Econometrics
Issue of 2008‒08‒14
eleven papers chosen by
Sune Karlsson
Orebro University

  1. Small-area estimation with spatial similarity By Nicholas Longford
  2. Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails By Millimet, Daniel L.; Tchernis, Rusty
  3. Bayesian Demographic Modeling and Forecasting: An Application to U.S. Mortality By Wolfgang Reichmuth; Samad Sarferaz
  4. Inference with the lognormal distribution By Nicholas Longford
  5. Comparison of methods in the analysis of dependent ordered catagorical data By Högberg, Hans; Svensson, Elisabeth
  6. Wild-Bootstrapped Variance Ratio Test for Autocorrelation in the Presence of Heteroskedasticity By Jeong, Jinook; Kang, Byunguk
  7. A review of nonfundamentalness and identification in structural VAR models By Lucia Alessi; Matteo Barigozzi; Marco Capasso
  8. An Overview of Methods in the Analysis of Dependent ordered catagorical Data: Assumptions and Implications By Högberg, Hans; Svensson, Elisabeth
  9. A Copula Test Space Model: How To Avoid the Wrong Copula Choice By Michiels F.; De Schepper A.
  10. Yield Curve Factors, Term Structure Volatility, and Bond Risk Premia By Nikolaus Hautsch; Yangguoyi Ou
  11. Solow Residuals without Capital Stocks By Michael C. Burda; Battista Severgnini

  1. By: Nicholas Longford
    Abstract: We derive a class of composite estimators of small-area quantities that exploit spatial (distance-related) similarity. They are based on a distribution-free model for the areas, but the estimators are aimed to have optimal design-based properties. Composition is applied also to estimating some of the global parameters on which the small-area estimators depend. We show that the commonly adopted assumption of random effects is not necessary for exploiting the similarity of the districts (borrowing strength across the districts). The methods are applied to estimation of the mean household sizes and the proportions of single-member households in the counties (comarcas) of Catalonia.
    Keywords: Auxiliary information, composite estimation, design-based estimator, exploiting similarity, model-based estimator, multivariate shrinkage, small-area estimation, spatial similarity
    JEL: C1 C13 C14 C15 C4 C42
    Date: 2008–07
  2. By: Millimet, Daniel L. (Southern Methodist University); Tchernis, Rusty (Indiana University)
    Abstract: We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.
    Keywords: treatment effects, propensity score, bias, unconfoundedness, selection on unobservables
    JEL: C21 C52
    Date: 2008–08
  3. By: Wolfgang Reichmuth; Samad Sarferaz
    Abstract: We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extending it in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying mortality of all age classes. In contrast to previous models, a similar development of adjacent age groups is assured allowing for consistent forecasts. We develop an appropriate Markov Chain Monte Carlo algorithm to estimate the parameters and the latent variables in an efficient one-step procedure. Via the Bayesian approach we are able to asses uncertainty intuitively by constructing error bands for the forecasts. We observe that in particular parameter uncertainty is important for long-run forecasts. This implies that hitherto existing forecasting methods, which ignore certain sources of uncertainty, may yield misleadingly sure predictions. To test the forecast ability of our model we perform in-sample and out-of-sample forecasts up to 2050, revealing that covariates can help to improve the forecasts for particular age classes. A structural analysis of the relationship between age-specific mortality and covariates is conducted in a companion paper.
    Keywords: Demography, Age-specific, Mortality, Lee-Carter, Stochastic, Bayesian, State Space Models, Forecasts
    JEL: C11 C32 C53 I10 J11
    Date: 2008–07
  4. By: Nicholas Longford
    Abstract: Several estimators of the expectation, median and mode of the lognormal distribution are derived. They aim to be approximately unbiased, efficient, or have a minimax property in the class of estimators we introduce. The small-sample properties of these estimators are assessed by simulations and, when possible, analytically. Some of these estimators of the expectation are far more efficient than the maximum likelihood or the minimum-variance unbiased estimator, even for substantial samplesizes.
    Keywords: X2 distribution, efficiency, lognormal distribution, minimax estimator, Taylor expansion
    JEL: C13 C42
    Date: 2008–07
  5. By: Högberg, Hans (Centre for Research and Development, Uppsala University and Country,Council of Gävleborg, Sweden); Svensson, Elisabeth (Department of Business, Economics, Statistics and Informatics)
    Abstract: Rating scales for outcome variables produce categorical data which are often ordered and measurements from rating scales are not standardized. The purpose of this study is to apply commonly used and novel methods for paired ordered categorical data to two data sets with different properties and to compare the results and the conditions for use of these models. The two applications consist of a data set of inter-rater reliability and a data set from a follow-up evaluation of patients. Standard measures of agreement and measures of association are used. Various loglinear models for paired categorical data using properties of quasi-independence and quasi-symmetry as well as logit models with a marginal modelling approach are used. A nonparametric method for ranking and analyzing paired ordered categorical data is also used. We show that a deeper insight when it comes to disagreement and change patterns may be reached using the nonparametric method and illustrate some problems with standard measures as well as parametric loglinear and logit models. In addition, the merits of the nonparametric method are illustrated.
    Keywords: Agreement:ordinal data; ranking; reliability.rating scales
    JEL: C14
    Date: 2008–08–08
  6. By: Jeong, Jinook; Kang, Byunguk
    Abstract: The Breusch-Godfrey’s LM test is one of the most popular tests for autocorrelation. However, it has been shown that the LM test may be erroneous when there exist heteroskedastic errors in regression model. Some remedies recently have been proposed by Godfrey and Tremayne (2005) and Shim et al. (2006). This paper suggests wild-bootstrapped variance ratio test for autocorrelation in the presence of heteroskedasticity. We show through a Monte Carlo simulation that our wild-bootstrapped VR test has better small sample properties and is robust to the structure of heteroskedasticity.
    Keywords: variance-ratio test; Breusch-Godfrey’s LM test; autocorrelation; heteroskedasticity; wild bootstrap
    JEL: C12 C15
    Date: 2006–12
  7. By: Lucia Alessi (Laboratory of Economics and Management (LEM), Sant’Anna School of Advanced Studies, Piazza Martiri della Libertà, 33, 56127 Pisa, Italy.); Matteo Barigozzi (Max Planck Institute of Economics, Kahlaische Strasse, 10, 07745 Jena, Germany.); Marco Capasso (Urban & Regional research centre Utrecht (URU), Faculty of Geosciences, Utrecht University, and Tjalling C. Koopmans Institute (TKI), Utrecht School of Economics, Utrecht University, The Netherlands.)
    Abstract: We review, under a historical perspective, the development of the problem of nonfundamentalness of Moving Average (MA) representations of economic models. Nonfundamentalness typically arises when agents’ information space is larger than the econometrician’s one. Therefore it is impossible for the latter to use standard econometric techniques, as Vector AutoRegression (VAR), to estimate economic models. We restate the conditions under which it is possible to invert an MA representation in order to get an ordinary VAR and identify the shocks, which in a VAR are fundamental by construction. By reviewing the work by Lippi and Reichlin [1993] we show that nonfundamental shocks may be very different from fundamental shocks. Therefore, nonfundamental representations should not be ruled out by assumption and indeed methods to detect nonfundamentalness have been recently proposed in the literature. Moreover, Structural VAR (SVAR) can be legitimately used for assessing the validity of Dynamic Stochastic General Equilibrium models only if the representation associated with the economic model is fundamental. Factor models can be an alternative to SVAR for validation purposes as they do not have to deal with the problem of nonfundamentalness. JEL Classification: C32, C51, C52.
    Keywords: Nonfundamentalness, Structural VAR, Dynamic Stochastic General Equilibrium Models, Factor Models.
    Date: 2008–07
  8. By: Högberg, Hans (Centre for Research and Development, Uppsala University and Country,Council of Gävleborg, Sweden); Svensson, Elisabeth (Department of Business, Economics, Statistics and Informatics)
    Abstract: Subjective assessments of pain, quality of life, ability etc. measured by rating scales and questionnaires are common in clinical research. The resulting responses are categorical with an ordered structure and the statistical methods must take account of this type of data structure. In this paper we give an overview of methods for analysis of dependent ordered categorical data and a comparison of standard models and measures with nonparametric augmented rank measures proposed by Svensson. We focus on assumptions and issues behind model specifications and data as well as implications of the methods. First we summarise some fundamental models for categorical data and two main approaches for repeated ordinal data; marginal and cluster-specific models. We then describe models and measures for application in agreement studies and finally give a summary of the approach of Svensson. The paper concludes with a summary of important aspects.
    Keywords: Dependent ordinal data; GEE; GLMM; Logit; modelling
    JEL: C14
    Date: 2008–08–08
  9. By: Michiels F.; De Schepper A.
    Abstract: We introduce and discuss the test space problem as a part of the whole copula fitting process. In particular, we explain how an efficient copula test space can be constructed by taking into account information about the existing dependence. Although our model is developed in abivariate environment it can be used for higher dimensional copula fitting applications. This is shown on the 3 dimensional dependence structure of an illustrative porfolio containing the S&P 500 Composite Index, the JP Morgan Government Bond Index and the NAREIT All index.
    Date: 2007–12
  10. By: Nikolaus Hautsch; Yangguoyi Ou
    Abstract: We introduce a Nelson-Siegel type interest rate term structure model with the underlying yield factors following autoregressive processes revealing time-varying stochastic volatility. The factor volatilities capture risk inherent to the term struc- ture and are associated with the time-varying uncertainty of the yield curve’s level, slope and curvature. Estimating the model based on U.S. government bond yields applying Markov chain Monte Carlo techniques we find that the yield factors and factor volatilities follow highly persistent processes. Using the extracted factors to explain one-year-ahead bond excess returns we observe that the slope and cur- vature yield factors contain the same explanatory power as the return-forecasting factor recently proposed by Cochrane and Piazzesi (2005). Moreover, we identify slope and curvature risk as important additional determinants of future excess returns. Finally, we illustrate that the yield and volatility factors are closely con- nected to variables reflecting macroeconomic activity, inflation, monetary policy and employment growth. It is shown that the extracted yield curve components have long-term prediction power for macroeconomic fundamentals.
    Keywords: Term Structure Modelling; Yield Curve Risk; Stochastic Volatility; Factor Models; Macroeconomic Fundamentals
    JEL: C5 E4 G1
    Date: 2008–07
  11. By: Michael C. Burda; Battista Severgnini
    Abstract: For more than fifty years, the Solow decomposition (Solow 1957) has served as the standard measurement of total factor productivity (TFP) growth in economics and management, yet little is known about its precision, especially when the capital stock is poorly measured. Using synthetic data generated from a prototypical stochastic growth model, we explore the quantitative extent of capital measurement error when the initial condition is unknown to the analyst and when capacity utilization and depreciation are endogenous. We propose two alternative measurements which eliminate capital stocks from the decomposition and significantly outperform the conventional Solow residual, reducing the root mean squared error in simulated data by as much as two-thirds. This improvement is inversely related to the sample size as well as proximity to the steady state. As an application, we compute and compare TFP growth estimates using data from the new and old German federal states.
    Keywords: Total factor productivity, Solow residual, generalized differences, measurement error, Malmquist index
    JEL: D24 E01 E22 O33 O47
    Date: 2008–08

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