
on Econometrics 
By:  Yu Ren (Queen's University); Katsumi Shimotsu (Queen's University) 
Abstract:  Jagannathan and Wang (1996) derive the asymptotic distribution of the HansenJagannathan distance (HJdistance) proposed by Hansen and Jagannathan (1997), and develop a specification test of asset pricing models based on the HJdistance. While the HJdistance has several desirable properties, Ahn and Gadarowski (2004) find that the specification test based on the HJdistance overrejects correct models too severely in commonly used sample size to provide a valid test. This paper proposes to improve the finite sample properties of the HJdistance test by applying the shrinkage method (Ledoit and Wolf, 2003) to compute its weighting matrix. The proposed method improves the finite sample performance of the HJdistance test significantly. 
Keywords:  Covariance matrix estimation, Factor models, Finite sample properties, HansenJagannathan distance, Shrinkage method 
JEL:  C13 C52 G12 
Date:  2007–06 
URL:  http://d.repec.org/n?u=RePEc:qed:wpaper:1126&r=ecm 
By:  James G. MacKinnon (Queen's University) 
Abstract:  This paper surveys bootstrap and Monte Carlo methods for testing hypotheses in econometrics. Several different ways of computing bootstrap P values are discussed, including the double bootstrap and the fast double bootstrap. It is emphasized that there are many different procedures for generating bootstrap samples for regression models and other types of model. As an illustration, a simulation experiment examines the performance of several methods of bootstrapping the supF test for structural change with an unknown break point. 
Keywords:  bootstrap test, supF test, wild bootstrap, pairs bootstrap, moving block bootstrap, residual bootstrap, bootstrap P value 
JEL:  C12 C15 
Date:  2007–06 
URL:  http://d.repec.org/n?u=RePEc:qed:wpaper:1127&r=ecm 
By:  Antonio E. Noriega; Daniel VentosaSantaulària 
Abstract:  This paper analyses the asymptotic behavior of the EngleGranger ttest for cointegration when the data include structural breaks, instead of being pure I(1) processes. We find that the test does not possess a limiting distribution, but diverges as the sample size tends to infinity. Calculations involving the asymptotic expression of the ttest , as well as Monte Carlo simulations, reveal that the test can diverge in either direction, making it unreliable as a test for cointegration, when there are neglected breaks in the trend function of the data. Using real data on car sales and murders in the US, we present an empirical illustration of the theoretical results. 
Keywords:  Spurious cointegration, structural breaks, integrated processes 
JEL:  C12 C13 C22 
Date:  2006–12 
URL:  http://d.repec.org/n?u=RePEc:bdm:wpaper:200612&r=ecm 
By:  Jhon James Mora; Juan Muro 
Abstract:  Sample selection bias is commonly used in economic models based on micro data. Despite the continuous generalization of panel data surveys, most countries still collect microeconomic information on the behavior of economic agents by means of repeated independent and representative crosssections. This paper discusses a simple testing procedure for sample selection bias in pseudo panels. In the context of conditional mean independence panel data models we describe a pseudo panel model in which under convenient expansion of the original specification with a selectivity bias correction term the method allows us to use a Wald test of H0: ρ=0 as a test of the null hypothesis of absence of sample selection bias. We show that the proposed selection bias correction term is proportional to Inverse Mills ratio with an argument equal to the “normit” of a consistent estimation of the observed proportion of individuals in each cohort. This finding can be considered a cohort counterpart of Heckman’s selectivity bias correction for the individual case and generalizes to some extent previous existing results in the empirical labour literature. Monte Carlo analysis shows the test does not reject the null for fixed T at a 5% significance level in finite samples and increases its power when utilizing cohort size corrections as suggested by Deaton (1985). As a “side effect” our method enables us to make a consistent estimation of the pseudo panel parameters under rejection of the null. 
Date:  2007–03–01 
URL:  http://d.repec.org/n?u=RePEc:col:001061:002921&r=ecm 
By:  Carlos Capistrán; Allan Timmermann 
Abstract:  Combination of forecasts from survey data is complicated by the frequent entry and exit in real time of individual forecasters which renders conventional least squares regression approaches to estimation of the combination weights infeasible. We explore the consequences of this for a variety of forecast combination methods in common use and propose a new method that projects actual outcomes on the equalweighted forecast as a means of adjusting for biases and noise in the underlying forecasts. Through simulations and an empirical application to inflation forecasts we show that the entry and exit of individual forecasters can have a large effect on the real time performance of conventional forecast combination methods. We also find that the proposed projection on the equalweighted forecast works well in practice. 
Keywords:  Forecasting, forecast combination, inflation, surveys 
JEL:  C53 E37 
Date:  2006–09 
URL:  http://d.repec.org/n?u=RePEc:bdm:wpaper:200608&r=ecm 
By:  Jennifer Chan (University of Sydney); Boris Choy (Department of Mathematical Sciences, University of Technology, Sydney); Udi Makov (University of Haifa) 
Abstract:  This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalizedt (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavytailed distributions including the Studentt and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the logANOVA, logANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC). 
Keywords:  Bayesian approach; state space model; threshold model; scale mixtures of uniform distribution; device information criterion 
Date:  2007–05–01 
URL:  http://d.repec.org/n?u=RePEc:uts:rpaper:196&r=ecm 
By:  Carla Ysusi 
Abstract:  When the logprice process incorporates a jump component, realised variance will no longer estimate the integrated variance since its probability limit will be determined by the continuous and jump components. Instead realised bipower variation, tripower variation and quadpower variation are consistent estimators of integrated variance even in the presence of jumps. In this paper we derive the limit distributions of realised tripower and quadpower variation, allowing us to compare these three estimators of integrated variance. Using the limit theories for the differences of the errors, tests for jumps are proposed for each estimator. Using simulated data, the performance of each of these tests is compared. The tests are also applied to empirical data but results need to be taken carefully as market microstructure effects may contaminate real data. 
Keywords:  Quadratic variation, Multipower variation, Stochastic volatility models, Jump process, Semimartingale, Highfrequency data 
JEL:  C12 C51 G19 
Date:  2006–09 
URL:  http://d.repec.org/n?u=RePEc:bdm:wpaper:200610&r=ecm 
By:  Carla Ysusi 
Abstract:  When highfrequency data is available, in the context of a stochastic volatility model, realised absolute variation can estimate integrated spot volatility. A central limit theory enables us to do filtering and smoothing using modelbased and modelfree approaches in order to improve the precision of these estimators. Although the absolute values are empirically attractive as they are less sensitive to possible large movements in highfrequency data, realised absolute variation does not estimate integrated variance. Some problems arise when using a finite number of intraday observations, as explained here. 
Keywords:  Quadratic variation, Absolute variation, Stochastic volatility models, Semimartingale, Highfrequency data 
JEL:  C13 C51 G19 
Date:  2006–12 
URL:  http://d.repec.org/n?u=RePEc:bdm:wpaper:200613&r=ecm 
By:  Banbura, Marta; Giannone, Domenico; Reichlin, Lucrezia 
Abstract:  This paper assesses the performance of Bayesian Vector Autoregression (BVAR) for models of different size. We consider standard specifications in the macroeconomic literature based on, respectively, three and eight variables and compare results with those obtained by larger models containing twenty or over one hundred conjunctural indicators. We first study forecasting accuracy and then perform a structural exercise focused on the effect of a monetary policy shock on the macroeconomy. Results show that BVARs estimated on the basis of hundred variables perform well in forecasting and are suitable for structural analysis. 
Keywords:  Bayesian VAR; forecasting; large crosssections; monetary VAR 
JEL:  C11 C13 C33 C53 
Date:  2007–06 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:6326&r=ecm 
By:  Christophe Hurlin (LEO  Laboratoire d'économie d'Orleans  [CNRS : UMR6221]  [Université d'Orléans]) 
Abstract:  In this study, we systemically apply nine recent panel unit root tests to the same fourteen macroeconomic and financial series as those considered in the seminal paper by Nelson and Plosser (1982). The data cover OECD countries from 1950 to 2003. Our results clearly point out the difficulty that applied econometricians would face when they want to get a simple and clearcut diagnosis with panel unit root tests. We confirm the fact that panel methods must be very carefully used for testing unit roots in macroeconomic or financial panels. More precisely, we find mitigated results under the crosssectional independence assumption, since the unit root hypothesis is rejected for many macroeconomic variables. When international crosscorrelations are taken into account, conclusions depend on the specification of these crosssectional dependencies. Two groups of tests can be distinguished. The first group tests are based on a dynamic factor structure or an error component model. In this case, the non stationarity of common factors (international business cycles or growth trends) is not rejected, but the results are less clear with respect to idiosyncratic components. The second group tests are based on more general specifications. Their results are globally more favourable to the unit root assumption. 
Keywords:  Panel Unit Root Tests 
Date:  2007–06–22 
URL:  http://d.repec.org/n?u=RePEc:hal:papers:halshs00156685_v1&r=ecm 
By:  John M Maheu; Thomas H McCurdy 
Abstract:  We provide an approach to forecasting the longrun (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on outofsample density forecasts. Forecasts use a probabilityweighted average of submodels, each of which is estimated over a different history of data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higherorder moments of excess returns when forecasting the return distribution and its moments. The shape of the longrun distribution and the dynamics of the higherorder moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixedlength moving window. These differences in longrun forecasts have implications for many financial decisions, particularly for risk management and longrun investment decisions. 
Keywords:  density forecasts, structural change, model risk, parameter uncertainty, Bayesian learning, market returns 
JEL:  C51 C53 C11 
Date:  2007–06–28 
URL:  http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa293&r=ecm 
By:  Ruist, Erik (Dept. of Economic Statistics, Stockholm School of Economics) 
Abstract:  Most standard hypothesis tests have high power only against a limited space of alternative hypotheses. With the advent of new tests for the same hypothesis, claimed to have higher power for some alternatives, but lower for other, the practitioner has to make a choice between alternative tests. This paper recommends the use of a pretest to guide this choice, or the combined use of both tests. 
Keywords:  sign test; t test; twodimensional criterion 
JEL:  C12 
Date:  2007–06–14 
URL:  http://d.repec.org/n?u=RePEc:hhs:hastef:0667&r=ecm 
By:  Desai Tejas A. 
Abstract:  We present a new algorithm for continuous, nonlinear or linear, and constrained or unconstrained optimization. After proving its convergence, we apply it to unconstrained and constrained maximum likelihood estimation, and compare its performance to that of the NewtonRaphson algorithm. 
Keywords:  Nonlinear programming; Linear programming; Constraints satisfaction; Multivariate statistics; Simulation 
Date:  2006–08–14 
URL:  http://d.repec.org/n?u=RePEc:iim:iimawp:20060804&r=ecm 
By:  Liesenfeld, Roman; Moura, Guilherme V; Richard, JeanFrançois 
Abstract:  We use panel probit models with unobserved heterogeneity and serially correlated errors in order to analyze the determinants and the dynamics of currentaccount reversals for a panel of developing and emerging countries. The likelihood evaluation of these models requires highdimensional integration for which we use a generic procedure known as Efficient Importance Sampling (EIS). Our empirical results suggest that current account balance, terms of trades, foreign reserves and concessional debt are important determinants of the probability of currentaccount reversal. Furthermore we find under all specifications evidence for serially correlated error components and weak evidence for state dependence. 
Keywords:  Panel data, Dynamic discrete choice, Current account reversals, Importance Sampling, Monte Carlo integration, State dependence 
JEL:  C15 C23 C25 F32 
Date:  2007 
URL:  http://d.repec.org/n?u=RePEc:zbw:cauewp:5584&r=ecm 
By:  van den Berg, Gerard J; van der Klaauw, Bas 
Abstract:  This paper provides a structural empirical analysis of Dutch auctions of houseplants at the flower auction in Aalsmeer, the Netherlands. The data set is unique for Dutch auctions in the sense that it includes observations of all losing bids in an interval adjacent to the winning bid. The size of this interval is determined by the speed of reaction of the auction participants, and as such these data are collectible due to neurological constraints on information processing. The data on losing bids are shown to be informative on the structural model determinants. The models are estimated using the Gibbs sampler with data augmentation. We take account of data limitations concerning the number of bidders. The estimation results are used to investigate whether actual reserve prices are optimal, and to determine the effects of reserve price changes. 
Keywords:  data augmentation; firstprice auction; Gibbs sampling; Markov Chain Monte Carlo; observing losing bids; private value; reserve price; speed of reaction 
JEL:  C15 C51 C81 D44 L11 L15 Q13 
Date:  2007–06 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:6323&r=ecm 
By:  Carlos Capistrán; Allan Timmermann 
Abstract:  Recent empirical work documents substantial disagreement in inflation expectations obtained from survey data. Furthermore, the extent of such disagreement varies systematically over time in a way that reflects the level and variance of current inflation. This paper offers a simple explanation for these facts based on asymmetries in the forecaster's costs of over and underpredicting inflation. Our model implies biased forecasts with positive serial correlation in forecast errors and a crosssectional dispersion that rises with the level and the variance of the inflation rate. It also implies that biases in forecaster's ranks should be preserved over time and that forecast errors at different horizons can be predicted through the spread between the short and longterm variance of inflation. We find empirically that these patterns are present in inflation forecasts from the Survey of Professional Forecasters. 
Keywords:  Inflation, Expectations, Forecasting, Asymmetric loss, Inflation dynamics 
JEL:  C53 E31 E37 
Date:  2006–06 
URL:  http://d.repec.org/n?u=RePEc:bdm:wpaper:200607&r=ecm 