Measurement Error in Imputed Consumption

Baker, Scott R.; Kueng, Lorenz; Meyer, Steffen; Pagel, Michaela

doi:10.3386/w25078

Cited by 23 publications

(13 citation statements)

References 28 publications

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“…Furthermore, as we will explain in section 7.1, measurement error in consumption is not a concern unless it is correlated with changes in income. However, Baker, Kueng, Pagel, and Meyer (2018) show, in a German dataset, that the relation between income and imputation error is economically small.…”

Section: Imputed Expenditurementioning

confidence: 91%

“…Given that dierent households will have their own idiosyncratic portfolios, this methodology will result in signicant measurement error. Baker, Kueng, Pagel, and Meyer (2018) show that the size of this measurement error is not only correlated with income and wealth, but also with the business cycle. Furthermore, Fagereng, Guiso, Malacrino, and Pistaferri (2019) show that some groups of investors consistently outperform the market, which would lead us to consistently underestimate their expenditure.…”

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confidence: 94%

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Consumption Heterogeneity: Micro Drivers and Macro Implications

Crawley

Küchler

2020

FEDS

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This paper explores the microfoundations of consumption models and quanties the macro implications of consumption heterogeneity. We propose a new empirical method to estimate the response of consumption to permanent and transitory income shocks for dierent groups of households. We then apply this method to administrative data from Denmark. The large sample size, along with detailed household balance sheet information, allows us to nely divide the population along relevant dimensions. We nd that households that stand to lose from an interest rate hike are signicantly more responsive to income shocks than those that stand to gain. Following a 1-percentage-point interest rate increase, we estimate that consumption growth decreases by a 1/4 percentage point through this interest rate exposure channel alone, making this channel substantially larger than the intertemporal substitution channel that is at the core of representative agent New Keynesian models.

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Section: Imputed Expenditurementioning

confidence: 91%

mentioning

confidence: 94%

Consumption Heterogeneity: Micro Drivers and Macro Implications

Crawley

Küchler

2020

FEDS

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“…We show that the SCF micro data generally line up very well with comparable National Income and Product Account (NIPA) and Financial Account (FA) income and wealth aggregates, so for most income and wealth components we can simply use proportional scaling to reproduce the aggregate intertemporal budget constraints precisely. There are three wealth components- 3 Baker et al (2018) consider how measurement error in balance sheet components flows through to error in consumption (or saving) using the intertemporal budget constraint approach. Those sorts of errors are relevant for both the registry papers and our pseudo-panel approach.…”

Section: Introductionmentioning

confidence: 99%

Lifecycle Patterns of Saving and Wealth Accumulation

Feiveson¹,

Sabelhaus²

2019

FEDS

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Empirical analysis of U.S. income, saving and wealth dynamics is constrained by a lack of highquality and comprehensive household-level panel data. This paper uses a pseudo-panel approach, tracking types of agents by birth cohort and across time through a series of cross-section snapshots synthesized with macro aggregates. The key micro source data is the Survey of Consumer Finances (SCF), which captures the top of the wealth distribution by sampling from administrative records. The SCF has the detailed balance sheet components, incomes, and interfamily transfers needed to use both sides of the intertemporal budget constraint and thus solve for saving and consumption. The results here are consistent with recent papers based on individual panel data from countries with administrative registries, and highlights the different roles of saving, capital gains, and interfamily transfers in wealth change over the lifecycle and across permanent income groups.

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“…Second, in the case of our motivating example, this procedure results in income or wealth being on both the right and left-hand side of the equation so that any measurement error in income or wealth can cause quite serious problems (Browning et al, 2014). Baker et al (2018) show that even with administrative data on income and wealth there can be significant measurement error in implied spending.…”

Section: Introductionmentioning

confidence: 99%

Regression with an Imputed Dependent Variable

2019

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Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching-based imputation procedures lead to inconsistent estimates. We offer an easily-implemented correction and correct asymptotic standard errors. We illustrate these with Monte Carlo experiments and empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).

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Measurement Error in Imputed Consumption

Cited by 23 publications

References 28 publications

Consumption Heterogeneity: Micro Drivers and Macro Implications

Consumption Heterogeneity: Micro Drivers and Macro Implications

Lifecycle Patterns of Saving and Wealth Accumulation

Regression with an Imputed Dependent Variable

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