Proxy Controls and Panel Data

We present a flexible approach to the identification and estimation of causal objects in nonparametric, non-separable models with confounding. Key to our analysis is the use of `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but are informative proxies for variables that do. Our analysis applies to both cross-sectional and panel models. Our identification results motivate a simple and `well-posed' nonparametric estimator and we analyze its asymptotic properties. In panel settings, our methods provide a novel approach to the difficult problem of identification with non-separable general heterogeneity and fixed $T$. In panels, observations from different periods serve as proxies for unobserved heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure. We apply our methodology to two empirical settings. We estimate causal effects of grade retention on cognitive performance using cross-sectional variation and we estimate a structural Engel curve for food using panel data.Comment: 76 pages, 1 table, 1 figur

An analysis of consumer panel data

In terms of collecting comprehensive panel expenditure data, there are trade-offs to be made in terms of the demands imposed on respondents and the level of detail and spending coverage collected. Existing comprehensive spending data tends to be cross-sectional whilst panel studies include only limited expenditure questions that record spending only as broad aggregates. More recently, economists have begun to use spending information collected by market research companies that records very detailed spending down to the barcode level from a panel of households, usually recorded by in-home barcode scanners, which may provide considerable advantages over existing data more commonly used in social sciences. However, there has not been a comprehensive assessment of the strengths and weaknesses of this kind of data collection method and the potential implications of survey mode on the recorded data. This paper seeks to address this, by an in-depth examination of scanner data from one company, Taylor Nelson Sofres (TNS), on grocery purchases over a five-year period. We assess how far the ongoing demands of participation inherent in this kind of survey lead to 'fatigue' in respondents' recording of their spending and compare the demographic representativeness of the data to the well-established Expenditure and Food Survey (EFS), constructing weights for the TNS that account for observed demographic differences. We also look at demographic transitions, comparing the panel aspect of the TNS to the British Household Panel Study (BHPS). We examine in detail the expenditure data in the TNS and EFS surveys and discuss the implications of this method of data collection for survey attrition. Broadly, we suggest that problems of fatigue and attrition may not be so severe as may be expected, though there are some differences in expenditure levels (and to some extent patterns of spending) that cannot be attributed to demographic or time differences in the two surveys alone and may be suggestive of survey mode effects. Demographic transitions appear to occur less frequently than we might expect which may limit the usefulness of the panel aspect of the data for some applications

Random Coefficient Panel Data Models

This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficient models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provides a review of heterogeneous dynamic panels, testing for homogeneity under weak exogeneity, simultaneous equation random coefficient models, and the more recent developments in the area of cross-sectional dependence in panel data models.random coefficient models, dynamic heterogeneous panels, classical and Bayesian approaches, tests of slope heterogeneity, cross section dependence

Forecasting with Spatial Panel Data

This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects, using Monte Carlo experiments. In addition, we check the performance of these forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.forecasting, BLUP, panel data, spatial dependence, heterogeneity

GfK Panel Data Analysis of meat consumption

This short report represents a summary of the analysis conducted so far on the GfK Panel Data. The focus was on the main three types of raw meat: chicken, pork, beef, as well as three categories of processed food products: liver paste, cold cuts and sausages. The aim was to look at the market indicators for each one of the six sub-categories, out of which market share and penetration were considered to be the most important. Besides these two indicators, I also included some extra calculations for each category, which can be analysed further and more in-depth if needed. One of the desired outcomes of the analyses was to identify companies and brands that were successful in each sub-category. Unfortunately, due to the generally low market shares of the organic products, the number of options was limited. Beef and pork were two categories where no brands were registered in the original product and sales Files. In the chicken category there was only one brand registered between 2006 and 2010, however, the market shares were extremely low for this category. There were only one or two households buying organic chicken products. The processed meat sub-categories both had a few companies registered. However, the diversity isn’t large. Still, it is good to notice that in the three sub-categories there were two companies that were most present: Farre Food and Hanegal. Market shares were generally low for all six sub-categories. Organic beef, pork and liver paste were the only categories to reach market shares of over 1%, while organic chicken had the lowest market shares of all. The trends were generally fluctuating over time. The highest market shares for liver paste, pork, chicken and beef were registered in 2007. All these four categories had much lower market shares in 2009, but it is interesting to notice that in the same year, organic cold cuts and sausages registered the highest market shares. Except for chicken, all market shares dropped in 2010 compared to 2009. Penetration levels had a clear descending trend for organic beef and liver paste, whereas for the other subcategories the levels fluctuated. Organic chicken and sausage generally had an ascending trend, while organic pork and cold cuts usually had descending trends in penetration levels. The highest penetration rates were registered in 2006 for liver paste, beef and cold cuts and in 2008 for sausages, chicken and pork. Out of all the six sub-categories that were analysed, organic beef has had the highest market shares and highest penetration rates, even though the figures were lower and lower every year. Regarding the average price paid/100 gr of meat products, we notice that in the organic category there is more fluctuation than in the conventional category, meaning that the price of organic products varies more between years. According to the analysis, the price difference between the organic and the conventional options in a sub-category is clearly notices in the processed meat category, but it is not as well defined for chicken, pork and beef. There are some limitations regarding the analysis of the panel data. On the one hand, these are due to the fact that there are some incompatibilities between the product file and the sales file regarding the identification of products as being organic or not. On the other hand, some of the products were registered as “unknown”, meaning that they are neither analysed as being organic, nor as being conventional, but as being a separate category. It is considered however that due to the fact that the results of the analysis are so small, the correction of these errors would not change the numbers significantly

Portuguese tourism demand:a dynamic panel data analysis

This article considers the determinants of Portuguese tourism demand for the period 2004-2013. The econometric methodology uses a panel unit root test and the dynamic panel data (GMM-system estimator). The different techniques of panel unit root (Levin, Lin and Chu; Im, Pesaran and Shin W-stat and augmented Dickey-Fuller - Fisher Chi-square) show that the variables used in this panel are stationary. The dynamic model proves that tourism demand is a dynamic process. The variables relative prices, income per capita, human capital and government spending encourage international tourism demand for Portugal.info:eu-repo/semantics/publishedVersio

An analysis of consumer panel data

In terms of collecting comprehensive panel expenditure data, there are trade-offs to be made in terms of the demands imposed on respondents and the level of detail and spending coverage collected. Existing comprehensive spending data tends to be cross-sectional whilst panel studies include only limited expenditure questions that record spending only as broad aggregates. More recently, economists have begun to use spending information collected by market research companies that records very detailed spending down to the barcode level from a panel of households, usually recorded by in-home barcode scanners, which may provide considerable advantages over existing data more commonly used in social sciences. However, there has not been a comprehensive assessment of the strengths and weaknesses of this kind of data collection method and the potential implications of survey mode on the recorded data. This paper seeks to address this, by an in-depth examination of scanner data from one company, Taylor Nelson Sofres (TNS), on grocery purchases over a five-year period. We assess how far the ongoing demands of participation inherent in this kind of survey lead to 'fatigue' in respondents' recording of their spending and compare the demographic representativeness of the data to the well-established Expenditure and Food Survey (EFS), constructing weights for the TNS that account for observed demographic differences. We also look at demographic transitions, comparing the panel aspect of the TNS to the British Household Panel Study (BHPS). We examine in detail the expenditure data in the TNS and EFS surveys and discuss the implications of this method of data collection for survey attrition. Broadly, we suggest that problems of fatigue and attrition may not be so severe as may be expected, though there are some differences in expenditure levels (and to some extent patterns of spending) that cannot be attributed to demographic or time differences in the two surveys alone and may be suggestive of survey mode effects. Demographic transitions appear to occur less frequently than we might expect which may limit the usefulness of the panel aspect of the data for some applications.Household panel data, scanner data, expenditure, food, duration models, attrition