{"title":"Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies","authors":"Mwanza Nkusu","doi":"10.5089/9781455297740.001.A001","DOIUrl":"https://doi.org/10.5089/9781455297740.001.A001","url":null,"abstract":"We analyze the link between nonperforming loans (NPL) and macroeconomic performance using two complementary approaches. First, we investigate the macroeconomic determinants of NPL in panel regressions and confirm that adverse macroeconomic developments are associated with rising NPL. Second, we investigate the feedback between NPL and its macroeconomic determinants in a panel vector autoregressive (PVAR) model. The impulse response functions (IRFs) attribute to NPL a central role in the linkages between credit market frictions and macrofinancial vulnerability. They suggest that a sharp increase in NPL triggers long-lived tailwinds that cripple macroeconomic performance from several fronts.","PeriodicalId":213955,"journal":{"name":"ERN: Panel Data Models (Multiple) (Topic)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117148679","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":"Can I Use a Panel? Panel Conditioning and Attrition Bias in Panel Surveys","authors":"M. Das, V. Toepoel, A. van Soest","doi":"10.2139/ssrn.1012252","DOIUrl":"https://doi.org/10.2139/ssrn.1012252","url":null,"abstract":"Over the past decades there has been an increasing use of panel surveys at the household or individual level, instead of using independent cross-sections. Panel data have important advantages, but there are also two potential drawbacks: attrition bias and panel conditioning effects. Attrition bias can arise if respondents drop out of the panel non-randomly, i.e., when attrition is correlated to a variable of interest. Panel conditioning arises if responses in one wave are in°uenced by participation in the previous wave(s). The experience of the previous interview(s) may affect the answers of respondents in a next interview on the same topic, such that their answers differ systematically from the answers of individuals who are interviewed for the first time. The literature has mainly focused on estimating attrition bias; less is known on panel conditioning effects. In this study we discuss how to disentangle the total bias in panel surveys due to attrition and panel conditioning into a panel conditioning and an attrition effect, and develop a test for panel conditioning allowing for non-random attrition. First, we consider a fully nonparametric approach without any assumptions other than those on the sample design, leading to interval identification of the measures for the attrition and panel conditioning effect. Second, we analyze the proposed measures under additional assumptions concerning the attrition process, making it possible to obtain point estimates and standard errors for both the attrition bias and the panel conditioning effect. We illustrate our method on a variety of questions from two-wave surveys conducted in a Dutch household panel. We found a significant bias due to panel conditioning in knowledge questions, but not in other types of questions. The examples show that the bounds can be informative if the attrition rate is not too high. Point estimates of the panel conditioning effect do not vary a lot with the different assumptions on the attrition process.","PeriodicalId":213955,"journal":{"name":"ERN: Panel Data Models (Multiple) (Topic)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115613985","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":"Time-Dependent versus State-Dependent Pricing: A Panel Data Approach to the Determinants of Belgian Consumer Price Changes","authors":"Luc Aucremanne, E. Dhyne","doi":"10.2139/ssrn.1691469","DOIUrl":"https://doi.org/10.2139/ssrn.1691469","url":null,"abstract":"Using Logistic Normal regressions, we model the price-setting behaviour for a large sample of Belgian consumer prices over the January 1989 - January 2001 period. Our results indicate that time-dependent features are very important, particularly an infinite mixture of Calvo pricing rules and truncation at specific horizons. Truncation is mainly a characteristic of pricing in the service sector where it mostly takes the form of annual Taylor contracts typically renewed at the end of December. Several other variables, including some that can be considered as state variables, are also found to be statistically significant. This is particularly so for accumulated sectoral inflation since the last price change. Once heterogeneity and the role of accumulated inflation are acknowledged, hazard functions become mildly upward-sloping, even in a low inflation regime. The contribution of the state-dependent variables to the pseudo-R² of our equations is, however, not particularly important.","PeriodicalId":213955,"journal":{"name":"ERN: Panel Data Models (Multiple) (Topic)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115821245","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":"Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey","authors":"B. Baltagi, C. Kao","doi":"10.2139/ssrn.1808022","DOIUrl":"https://doi.org/10.2139/ssrn.1808022","url":null,"abstract":"This chapter provides an overview of topics in nonstationary panels: panel unit root tests, panel cointegration tests, and estimation of panel cointegration models. In addition it surveys recent developments in dynamic panel data models.","PeriodicalId":213955,"journal":{"name":"ERN: Panel Data Models (Multiple) (Topic)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115586649","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}