Can cohort data be treated as genuine panel data?

If repeated observations on the same individuals are not available it is not possible to capture unobserved individual characteristics in a linear model by using the standard fixed effects estimator. If large numbers of observations are available in each period one can use cohorts of individuals with common characteristics to achieve the same goal, as shown by Deaton (1985). It is tempting to analyze the observations on cohort averages as if they are observations on individuals which are observed in consecutive time periods. In this paper we analyze under which conditions this is a valid approach. Moreover, we consider the impact of the construction of the cohorts on the bias in the standard fixed effects estimator. Our results show that the effects of ignoring the fact that only a synthetic panel is available will be small if the cohort sizes are sufficiently large (100, 200 individuals) and if the true means within each cohort exhibit sufficient time variation.The authors thank Bertrand Melenberg, Robert Moffitt, Guglielmo Weber, seminar participants at Texas A & M University, Rice University and the Conference on The Econometrics of Panels and Pseudo Panels (Venice, October 1990) and two anonymous referees for helpful comments. Rob Alessie and Pim Adang kindly provided the data. Financial support by the Royal Netherlands Academy of Arts and Sciences (K.N.A.W.) and the Netherlands Organization for Scientific Research (N.W.O.) is gratefully acknowledged.     

Analyses on web-based product data management

This paper is concerned with the development of product data management （PDM） systems--WPDM systems based on web technologies. As a tool to integrate information, traditional PDM system has many benefits for the companies in such aspects as improving design productivity, better control over projects and so on. With the maturing of web technologies, the advantages of WPDM system are obvious. We will show these advantages in detail in Part 3. WPDM system is built on three-tier application model to provide security and flexibility, they are back-end, middle layer and front-end. The basic designs in each layer will be briefly introduced in Part 4. In the future, WPDM will be extended to integrate with other applications to provide a complete web-based engineering environment.     

Does the labour supply of wives respond to husbands' wages? Canadian evidence from micro data and grouped data

Abstract. Using micro data and grouped data, we assess the extent to which Canadian wives adjusted their labour supply in response to changes in husbands' wages during the period 1980‐2000. Grouped data parameters based on weighted least squares and the unbiased‐error‐in‐variables estimator developed by Devereux (2004, 2007a,b) yield cross‐wage elasticities that are substantially higher (in absolute value) than those derived from OLS regressions run on micro data. Both grouping estimators indicate that the labour supply of Canadian wives responded strongly to changes in husbands' wages during the 1980s. For the 1990s, our estimates of wives' cross‐wage elasticity display greater dispersion.     

中国城镇居民消费的动态Panel data模型

文章利用中宏数据库(MCDB)的我国内地29个省级行政单位1986—2004年的"家庭平均每人全年可支配收入"、"居民家庭平均每人全年消费性支出"数据,建立固定影响的动态Panel Data的消费模型,对地区间城镇居民消费结构进行对比分析,结果发现,东北地区的边际消费倾向最高,中部地区的自发性消费最高;各个地区的消费惯性的影响都不大。     

A panel data approach to price–value correlations

Resorting to stationary and nonstationary panel data econometrics, we offer tests for “Ricardo’s 93% theory of value” for ten OECD countries over different time ranges. The theory does not find empirical support.     

What is an oil shock? Panel data evidence

This article characterizes the nonlinear relation between oil price change and GDP growth, focusing on the panel data of various industrialized countries. Toward this end, the article extends a flexible nonlinear inference to the panel data analysis where the random error components are incorporated into the flexible approach. The article reports clear evidence of nonlinearity in the panel and confirms earlier claims in the literature??oil price increases are statistically and economically significant while oil price decreases are not and previous upheaval in oil prices causes the marginal effect of any given oil price change to be reduced. Our result suggests that the nonlinear oil?Cmacroeconomy relation is generally observable over different industrialized countries and it is desirable for one to use the nonlinear function of oil price change for GDP forecast.     

Testing Okun’s law with Swiss industry data

Okun’s law postulates a stable relationship between quarterly output growth and changes in (un)employment. This proposition has so far been tested with macroeconomic data at the highest level of aggregation. The article goes beyond that in extending the analysis to industry data from Switzerland, applying a method suggested by the International Monetary Fund. Another focus is on whether expansions in production have become more ‘jobless’ over the most recent business cycle compared to earlier ones. This does not seem to be the case in Switzerland, except in the construction industry.     

Peaks or tails – What distinguishes financial data?

We argue against the view that it is mostly the peaks of the empirical densities of stock returns (and of other risky returns as well) that set such data aside from “normal” variables. We show that peaks depend on sample size and on the way returns are standardized, and that for given data sets of stock returns, both higher peaks and lower peaks than in a standard normal case can be obtained. First version received: March 1998/Final version received: April 2000     

Testing the Uzawa–Lucas model with OECD data

The paper tests the Uzawa–Lucas model of endogenous growth using a modified Granger-causality test and panel data for 20 OECD countries. The results favour the human capital augmented endogenous growth model against both the exogenous growth model à la Solow and endogenous growth models of the AK type.     

DOES NEOCLASSICAL THEORY ACCOUNT FOR THE EFFECTS OF BIG FISCAL SHOCKS? EVIDENCE FROM WORLD WAR II*

Much debate surrounds the usefulness of the neoclassical growth model for assessing the macroeconomic impact of fiscal shocks. We test the theory using data from World War II, which is by far the largest fiscal shock in the history of the United States. We take observed changes in fiscal policy during the war as inputs into a parameterized, dynamic general equilibrium model and compare the values of all variables in the model to the actual values of these variables in the data. Our main finding is that the theory quantitatively accounts for macroeconomic activity during this big fiscal shock.     

How should we collect data on the nursing workforce?

R esearchers who study the nursing workforce use data about nurses and data about nursing jobs. Diifferent approaches - sample surveys and relicensure surveys - used to collect data on RNs, and the benefits and weaknesses of these approaches, are reviewed. Predicting the future supply of nurses is more complex than estimating the current supply, as factors that affect future supply must be considered. Simple forecasts can be created with relatively few variables, which could be collected through relicensure surveys. However, more detailed assessments of potential retirement patterns, changing family norms regarding childcare and household work, and movements into higher education require the rich data more often found in sample surveys.     

Testing the Bhaduri–Marglin model with OECD panel data

The Bhaduri–Marglin model is a post-Kaleckian model that allows one to study the impact of a functional income distribution on the growth in demand. Over recent years, a number of empirical studies based on this model have aimed at determining whether a redistribution towards profits harms or fosters demand growth. The focus so far has been on a very limited number of countries. This paper is the first to test the Bhaduri–Marglin model with panel data. It finds that demand growth is reduced by a redistribution towards profits in the average OECD country. Productivity growth is also impaired.     

Price convergence in the EMU? Evidence from micro data

The establishment of the European Monetary Union (EMU) was widely expected to cause price convergence among member states. In an investigation of this claim, the present study avoids problems of comparability and representativeness by using an extremely detailed and comprehensive scanner database on washing machine prices and sales volumes for 17 European countries. A hedonic regression yields country-specific time series for quality-adjusted price differentials. Statistically and economically significant deviations from the Law of One Price emerge. Log t tests firmly reject price convergence among EMU countries. Small convergence clusters can be identified but they are unrelated to EMU membership.     

Do dynamics and heterogeneity in panel data models matter?

This article investigates empirically the determinants of central banks’ reserve holdings for a large panel data set of developing and transition countries covering the period from 1975 to 2003. It estimates both a static and a dynamic relationship and applies estimators for homogeneous and heterogeneous panel data. Thereby, it examines the extent to which conclusions of panel data studies on the determinants of international reserve holdings are robust to the inclusion of dynamics as well as to the consideration of heterogeneity across countries. The results show that the neglect of dynamics and heterogeneity in country behaviour may lead to misleading inferences. Independently of the chosen estimation method, the findings suggest that trade openness and external debt are robust determinants of the level of reserves. Central banks take precautionary measures against the downside of the increasing international economic integration.     

Trade and unemployment: What do the data say?

This paper documents a robust empirical regularity: in the long-run, higher trade openness is associated with a lower structural rate of unemployment. We establish this fact using: (i) panel data from 20 OECD countries, (ii) cross-sectional data on a larger set of countries. The time structure of the panel data allows us to control for unobserved heterogeneity, whereas cross-sectional data make it possible to instrument openness by its geographical component. In both setups, we purge the data of business cycle effects, include a host of institutional and geographical variables, and control for within-country trade. Our main finding is robust to various definitions of unemployment rates and openness measures. Our benchmark specification suggests that a 10 percentage point increase in total trade openness reduces aggregate unemployment by about three quarters of one percentage point.     

Ambiguity,data and preferences for information – A case-based approach

We model decision making under ambiguity based on available data. Decision makers express preferences over actions and data sets. We derive an α-max–min representation of preferences, in which beliefs combine objective characteristics of the data (number and frequency of observations) with subjective features of the decision maker (similarity of observations and perceived ambiguity). We identify the subjectively perceived ambiguity and separate it into ambiguity due to a limited number of observations and ambiguity due to data heterogeneity. The special case of no ambiguity provides a behavioral foundation for beliefs as similarity-weighted frequencies as in Billot et al. (2005) [3].     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.