The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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Econometric Analysis of Cross Section and Panel Data

Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the observations. Many economic variables, such as GNP and its components, price indices, sales, and stock returns are observed over time. In addition to being interested in the contemporaneous relationships among such variables, we are often concerned with relationships between their current and past values, that is, relationships over time.

Time Series Analysis

Recently, Perron has carried out tests of the unit-root hypothesis against the alternative hypothesis of trend stationarity with a break in the trend occurring at the Great Crash of 1929 or at the 1973 oil-price shock. His analysis covers the Nelson–Plosser macroeconomic data series as well as a postwar quarterly real gross national product (GNP) series. His tests reject the unit-root null hypothesis for most of the series. This article takes issue with the assumption used by Perron that the Great Crash and the oil-price shock can be treated as exogenous events. A variation of Perron's test is considered in which the breakpoint is estimated rather than fixed. We argue that this test is more appropriate than Perron's because it circumvents the problem of data-mining. The asymptotic distribution of the estimated breakpoint test statistic is determined. The data series considered by Perron are reanalyzed using this test statistic. The empirical results make use of the asymptotics developed for the test stati...

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Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis

SEQUELS ARE RARELY AS good as the originals, so I approach this review of the market efficiency literature with trepidation. The task is thornier than it was 20 years ago, when work on efficiency was rather new. The literature is now so large that a full review is impossible, and is not attempted here. Instead, I discuss the work that I find most interesting, and I offer my views on what we have learned from the research on market efficiency.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.1991.tb04636.x

Efficient Capital Markets: II

This reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.

New Introduction to Multiple Time Series Analysis

Individuals' preferences underlying most economic behavior are likely to display substantial heterogeneity. This paper reports on direct measures of preference parameters relating to risk tolerance, time preference, and intertemporal substitution. These experimental measures are based on survey respondents' choices in hypothetical situations. The questions are constructed with as little departure from the theorist's concept of the underlying parameter as possible. The individual measures of preference parameters display substantial heterogeneity. The majority of respondents fall into the least risk-tolerant group, but a substantial minority display higher risk tolerance. The individual measures of intertemporal substitution and time preference also display substantial heterogeneity. The mean risk tolerance is 0.25; the mean elasticity of intertemporal substitution is 0.2. Estimated risk tolerance and the elasticity of intertemporal substitution are essentially uncorrelated across individuals. Because the risk tolerance measure is obtained as part of the main questionnaire of a large survey, it can be related to a number of economic behaviors. Measured risk tolerance is positively related to a number of risky behaviors, including smoking, drinking, failing to have insurance, and holding stocks rather than Treasury bills. Although measured risk tolerance explains only a small fraction of the variation of the studied behaviors, these estimates provide evidence about the validity and usefulness of the measures of preference parameters.

Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Survey

This paper reports measures of preference parameters relating to risk tolerance, time preference, and intertemporal substitution. These measures are based on survey responses to hypothetical situations constructed using an economic theorist's concept of the underlying parameters. The individual measures of preference parameters display heterogeneity. Estimated risk tolerance and the elasticity of intertemporal substitution are essentially uncorrelated across individuals. Measured risk tolerance is positively related to risky behaviors, including smoking, drinking, failing to have insurance, and holding stocks rather than Treasury bills. These relationships are both statistically and quantitatively significant, although measured risk tolerance explains only a small fraction of the variation of the studied behaviors.

/pdf/preference-parameters-and-behavioral-heterogeneity-an-45i36u0mka.pdf

Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Study

Changes in government spending often lead to significant shifts in demand across sectors. This paper analyzes the effects of sector-specific changes in government spending in a two-sector dynamic general equilibrium model in which the reallocation of capital across sectors is costly. The two-sector model leads to a richer array of possible responses of aggregate variables than the one-sector model. The empirical part of the paper estimates the effects of military buildups on a variety of macroeconomic variables using a new measure of military shocks. The behavior of macroeconomic aggregates is consistent with the predictions of a multi-sector neoclassical model. In particular, consumption, real product wages and manufacturing productivity fall in response to exogenous military buildups in the post-World War II United States.

Costly Capital Reallocation and the Effects of Government Spending

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What shocks account for the business cycle frequency and long run movements of output and prices? This paper addresses this question using the identifying assumption that only supply shocks, such as shocks to technology, oil prices, and labor supply affect output in the long run Real and monetary aggregate demand shocks can affect output, but only in the short run This assumption sufficiently restricts the reduced form of key macroeconomic variables to allow estimation of the shocks and their effect on output and price at all frequencies Aggregate demand shocks account for about twenty to thirty percent of output fluctuations at business cycle frequencies Technological shocks account for about one-quarter of cyclical fluctuations, and about one-third of output's variance at low frequencies Shocks to oil prices are important in explaining episodes in the 1970's and 1980's Shocks that permanently affect labor input account for the balance of fluctuations in output, namely, about half of its variance at all frequencies

https://papers.ssrn.com/sol3/Delivery.cfm/nber_w2589.pdf?abstractid=1651431

Matthew D. Shapiro

Papers

Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Survey

Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Study

Costly Capital Reallocation and the Effects of Government Spending

Costly Capital Reallocation and the Effects of Government Spending

Sources of Business Cycle Fluctuations