CEIS: Econometrics & Empirical Economics (Topic)最新文献

{"title":"Robust Tests for Convergence Clubs","authors":"L. Corrado, T. Stengos, M. Weeks, M. Yazgan","doi":"10.2139/ssrn.3333113","DOIUrl":"https://doi.org/10.2139/ssrn.3333113","url":null,"abstract":"In many applications common in testing for convergence the number of cross-sectional units is large and the number of time periods are few. In these situations asymptotic tests based on an omnibus null hypothesis are characterised by a number of problems. In this paper we propose a multiple pairwise comparisons method based on an a recursive bootstrap to test for convergence with no prior information on the composition of convergence clubs. Monte Carlo simulations suggest that our bootstrap-based test performs well to correctly identify convergence clubs when compared with other similar tests that rely on asymptotic arguments. Across a potentially large number of regions, using both cross-country and regional data for the European Union we find that the size distortion which afflicts standard tests and results in a bias towards finnding less convergence, is ameliorated when we utilise our bootstrap test.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130011883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Spatial Diffusion Model with Common Factors and an Application to Cigarette Consumption","authors":"Carlo Ciccarelli, J. Elhorst","doi":"10.2139/ssrn.2787095","DOIUrl":"https://doi.org/10.2139/ssrn.2787095","url":null,"abstract":"This paper adopts a dynamic spatial panel data model with common factors to explain the non-stationary diffusion process of cigarette consumption across 69 Italian provinces over the period 1877-1913. The Pesaran (2015) CD-test and the exponent a-test of Bailey et al. (2015) are used to show that both weak and strong cross-sectional dependence are important drivers of the propagation of cigarette demand over this period. Stability tests on the coefficients and the CD-test on the residuals of the model are used to verify whether the data and both forms of cross-sectional dependence are modeled adequately. Cigarettes are found to be a normal good with an income elasticity of 0.4 and a price elasticity -0.4 in the long term. The price elasticity can be decomposed into a direct effect of -0.54 in the own region and a spillover effect to other regions of 0.15. This positive spillover effect is in line with previous spatial econometric studies which investigated cigarette demand in the U.S. states over a more recent period.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130331963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalised Partial Autocorrelations and the Mutual Information between Past and Future","authors":"A. Luati, Tommaso Proietti","doi":"10.2139/ssrn.2615064","DOIUrl":"https://doi.org/10.2139/ssrn.2615064","url":null,"abstract":"The paper introduces the generalised partial autocorrelation (GPAC) coefficients of a stationary stochastic process. The latter are related to the generalised autocovariances, the inverse Fourier transform coefficients of a power transformation of the spectral density function. By interpreting the generalized partial autocorrelations as the partial autocorrelation coefficients of an auxiliary process, we derive their properties and relate them to essential features of the original process. Based on a parameterisation suggested by Barndorff-Nielsen and Schou (1973) and on Whittle likelihood, we develop an estimation strategy for the GPAC coefficients. We further prove that the GPAC coefficients can be used to estimate the mutual information between the past and the future of a time series.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114463140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Selection of Common Factors for Macroeconomic Forecasting","authors":"A. Giovannelli, Tommaso Proietti","doi":"10.2139/ssrn.2577358","DOIUrl":"https://doi.org/10.2139/ssrn.2577358","url":null,"abstract":"We address the problem of selecting the common factors that are relevant for forecasting macroeconomic variables. In economic forecasting using diffusion indexes the factors are ordered, according to their importance, in terms of relative variability, and are the same for each variable to predict, i.e. the process of selecting the factors is not supervised by the predictand. We propose a simple and operational supervised method, based on selecting the factors on the basis of their significance in the regression of the predictand on the predictors. Given a potentially large number of predictors, we consider linear transformations obtained by principal components analysis. The orthogonality of the components implies that the standard t-statistics for the inclusion of a particular component are independent, and thus applying a selection procedure that takes into account the multiplicity of the hypotheses tests is both correct and computationally feasible. We focus on three main multiple testing procedures: Holm’s sequential method, controlling the family wise error rate, the Benjamini-Hochberg method, controlling the false discovery rate, and a procedure for incorporating prior information on the ordering of the components, based on weighting the p-values according to the eigenvalues associated to the components. We compare the empirical performances of these methods with the classical diffusion index (DI) approach proposed by Stock and Watson, conducting a pseudo-real time forecasting exercise, assessing the predictions of 8 macroeconomic variables using factors extracted from an U.S. dataset consisting of 121 quarterly time series. The overall conclusion is that nature is tricky, but essentially benign: the information that is relevant for prediction is effectively condensed by the first few factors. However, variable selection, leading to exclude some of the low order principal components, can lead to a sizable improvement in forecasting in specific cases. Only in one instance, real personal income, we were able to detect a significant contribution from high order components.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130019880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EuroMInd-C: A Disaggregate Monthly Indicator of Economic Activity for the Euro Area and Member Countries","authors":"C. Frale, S. Grassi, Massimiliano Marcellino, G. Mazzi, Tommaso Proietti","doi":"10.2139/ssrn.2334364","DOIUrl":"https://doi.org/10.2139/ssrn.2334364","url":null,"abstract":"This paper deals with the estimation of monthly indicators of economic activity for the Euro area and its largest member countries that possess the following attributes: relevance, representativeness and timeliness. Relevance is determined by comparing our monthly indicators to the gross domestic product at chained volumes, as the most important measure of the level of economic activity. Representativeness is achieved by considering a very large number of (timely) time series of monthly indicators relating to the level of economic activity, providing a more or less complete coverage. The indicators are modelled using a large-scale parametric factor model. We discuss its specification and provide details of the statistical treatment. Computational efficiency is crucial for the estimation of large-scale parametric factor models of the dimension used in our application (considering about 170 series). To achieve it, we apply state-of-the-art state space methods that can handle temporal aggregation, and any pattern of missing values.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132684917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regression with Imputed Covariates: A Generalized Missing Indicator Approach","authors":"Franco Peracchi, Valentino Dardanoni, S. Modica","doi":"10.2139/ssrn.1485547","DOIUrl":"https://doi.org/10.2139/ssrn.1485547","url":null,"abstract":"A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper, we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may then be tackled by either model reduction procedures or model averaging methods. We illustrate our approach by considering the problem of estimating the relation between income and the body mass index (BMI) using survey data affected by item non-response, where the missing values on the main covariates are filled in by imputations.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128094995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bayesian Estimation of a DSGE Model with Financial Frictions","authors":"Rossana Merola","doi":"10.2139/ssrn.1481308","DOIUrl":"https://doi.org/10.2139/ssrn.1481308","url":null,"abstract":"Episodes of crises that have recently plagued many emerging market economies have lead to a wide-spread questioning of the two traditional generations of models of currency crises. Distressed banking system and adverse credit-markets conditions have been pointed as sources of serious macroeconomics contractions, so introducing these imperfections into standard economic models can help to explain the more recent crises. This paper introduces financial frictions a la Bernanke Gertler and Gilchrist in a two-sector small open economy, suited to analyze an emerging country. The model is estimated on simulated data applying both Bayesian techniques and maximum likelihood method and comparing the results under the two di¤erent estimation procedures. First, I analyze the influence of the prior on the estimation outcomes. Results seems to confirm that one of the main advantages of Bayesian approach is the ability of providing a framework for evaluating fundamentally mis-specified models. Second, I test the sensitivity of estimation outcomes to the sample size, showing how, for large samples, results under Bayesian estimation converges asymptotically to those obtained applying maximum likelihood. A further extension would be to perform the estimation on historical data for an emerging economy that have recently experienced a financial crisis.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114307193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing for Cointegration in High-Dimensional Systems","authors":"G. Cubadda, J. Breitung","doi":"10.2139/ssrn.1473886","DOIUrl":"https://doi.org/10.2139/ssrn.1473886","url":null,"abstract":"This paper considers cointegration tests for dynamic systems where the number of variables is large relative to the sample size. Typical examples include tests for unit roots in panels, where the units are linked by complicated dynamic relationships. It is well known that conventional cointegration tests based on a parametric (vector autoregressive) representation of the system break down if the number of variables approaches the number of time periods. To sidestep this difficulty we propose nonparametric cointegration tests based on eigenvalue problems that are asymptotically free of nuisance parameters. Furthermore, a nonparametric panel unit root test is suggested. It turns out that if the number of variables is large, the nonparametric tests outperform their parametric (likelihood-ratio based) counterparts by a clear margin.","PeriodicalId":365494,"journal":{"name":"CEIS: Econometrics & Empirical Economics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129122503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}