In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

We examine the frequency of price changes for 350 categories of goods and services covering about 70 percent of consumer spending, on the basis of unpublished data from the Bureau of Labor Statisti...

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Some evidence on the importance of sticky prices

Teacher training, teacher quality and student achievement

This chapter summarizes what has been learned from recent research on intergenerational transmission of earnings status. The chapter begins by using a simple theoretical model to highlight several key concepts. Then it reviews (and discusses the connections among) three related empirical literatures: on sibling correlations in earnings, on the intergenerational elasticity of offspring’s earnings with respect to parents’ earnings or income, and on neighborhood effects. © 1999 Elsevier Science B.V. All rights reserved.

Intergenerational Mobility in the Labor Market

My focus is on the degree to which increasing inequality in the high-income countries, particularly in the United States, is likely to limit economic mobility for the next generation of young adults. I discuss the underlying drivers of opportunity that generate the relationship between inequality and intergenerational mobility. The goal is to explain why America differs from other countries, how intergenerational mobility will change in an era of higher inequality, and how the process is different for the top 1 percent. I begin by presenting evidence that countries with more inequality at one point in time also experience less earnings mobility across the generations, a relationship that has been called “The Great Gatsby Curve.” The interaction between families, labor markets, and public policies all structure a child's opportunities and determine the extent to which adult earnings are related to family background—but they do so in different ways across national contexts. Both cross-country compa...

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Income Inequality, Equality of Opportunity, and Intergenerational Mobility

We estimate the importance of teachers in Chicago public high schools using matched student‐teacher administrative data. A one standard deviation, one semester improvement in math teacher quality raises student math scores by 0.13 grade equivalents or, over 1 year, roughly one‐fifth of average yearly gains. Estimates are relatively stable over time, reasonably impervious to a variety of conditioning variables, and do not appear to be driven by classroom sorting or selective score reporting. Also, teacher quality is particularly important for lower‐ability students. Finally, traditional human capital measures—including those determining compensation—explain little of the variation in estimated quality.

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Teachers and Student Achievement in the Chicago Public High Schools

Studies that attempt to measure the impact of neighborhoods on children's outcomes are susceptible to bias because families choose where to live. As a result, the effect of family unobservables, such as the importance parents place on their children's welfare, and other unobservables that are common to geographically clustered households, may be mistakenly attributed to neighborhood influences. Previous studies that attempt to correct for this selection bias have used questionable instrumental variables. This paper introduces an approach based on the observation that the latent factors associated with neighborhood choice do not vary across siblings. Therefore, family residential changes provide a source of neighborhood background variation that is free of the family-specific heterogeneity biases associated with neighborhood selection. Using a sample of multichild families whose children are separated in age by at least three years, I estimate family fixed effect equations of children's educational outcomes. The fixed effect results suggest that the impact of neighborhoods may exist even when family-specific unobservables are controlled. This finding is robust to many changes to estimation techniques, outcome measures, variable definitions, and samples but is sensitive to the exact formulation of the neighborhood measure. Daniel Aaronson is a researcher at the Federal Reserve Bank of Chicago. He thanks Joe Altonji, Becky Blank, Greg Duncan, Judy Hellerstein, Sandy Jencks, Bruce Meyer, John Karl Scholz, Lauren Sinai, and the reviewers for helpful suggestions. He takes responsibility for all errors and omissions. The views expressed in this paper are those of the author and are not necessarily those of the Federal Re

Using sibling data to estimate the impact of neighborhoods on children's educational outcomes

A Note on the Benefits of Homeownership

We estimate trends in intergenerational economic mobility by matching men in the Census to synthetic parents in the prior generation. We find that mobility increased from 1950 to 1980 but has declined sharply since 1980. While our estimator places greater weight on location effects than the standard intergenerational coefficient, the size of the bias appears to be small. Our preferred results suggest that earnings are regressing to the mean more slowly now than at any time since World War II, causing economic differences between families to become more persistent. However, current rates of positional mobility appear historically normal.

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Intergenerational Economic Mobility in the United States, 1940 to 2000

Immediately following a minimum wage hike, household income rises on average by about $250 per quarter and spending by roughly $700 per quarter for households with minimum wage workers. Most of the spending response is caused by a small number of households who purchase vehicles. Furthermore, we find that the high spending levels are financed through increases in collateralized debt. Our results are consistent with a model where households can borrow against durables and face costs of adjusting their durables stock. (JEL D12, D14, D91, J38)

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Daniel Aaronson

Papers

Teachers and Student Achievement in the Chicago Public High Schools

Using sibling data to estimate the impact of neighborhoods on children's educational outcomes

A Note on the Benefits of Homeownership

Intergenerational Economic Mobility in the United States, 1940 to 2000

The Spending and Debt Response to Minimum Wage Hikes