Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

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Econometric Analysis of Panel Data

This article considers estimation of a stochastic frontier production function-the type introduced by Aigner, Lovell, and Schmidt (1977) and Meeusen and van den Broeck (1977). Such a production frontier model consists of a production function of the usual regression type but with an error term equal to the sum of two parts. The first part is typically assumed to be normally distributed and represents the usual statistical noise, such as luck, weather, machine breakdown, and other events beyond the control of the firm. The second part is nonpositive and represents technical inefficiencythat is, failure to produce maximal output, given the set of inputs used. Realized output is bounded from above by a frontier that includes the deterministic part of the regression, plus the part of the error representing noise; so the frontier is stochastic. There also exist socalled deterministic frontier models, whose error term contains only the nonpositive component, but we will not consider them here (e.g., see Greene 1980). Frontier models arise naturally in the problem of efficiency measurement, since one needs a bound on output to measure efficiency. A good survey of such production functions and their relationship to the measurement of productive efficiency was given by F0rsund, Lovell, and Schmidt (1980).

Production Frontiers and Panel Data

This paper gives a brief survey of forecasting with panel data. Starting with a simple error component regression model and surveying best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial auto-regressive models. The paper also surveys how these forecasts have been used in panel data applications, running horse races between heterogeneous and homogeneous panel data models using out of sample forecasts.

Forecasting with Panel Data

This paper gives a brief survey of forecasting with panel data. It begins with a simple error component regression model and surveys the best linear unbiased prediction under various assumptions of the disturbance term. This includes various ARMA models as well as spatial autoregressive models. The paper also surveys how these forecasts have been used in panel data applications, running horse races between heterogeneous and homogeneous panel data models using out-of-sample forecasts. Copyright © 2008 John Wiley & Sons, Ltd.

https://surface.syr.edu/cgi/viewcontent.cgi?article=1073&context=cpr

Forecasting with panel data

Of all the major economic and demographic events in the postwar United States period, the trends in fertility and female labour supply stand out as among the most dramatic. Fertility rates, after their Depression lows, rose unexpectedly after World War II far more than was anticipated as an adjustment to the War, and continued to rise until the late 1950's and early 1960's, when they again took an unexpected turn downward all the way to today's historical lows. Female labour-force-participation rates, continuing their historical climb, grew throughout the postwar period at an increasing pace, by 4 percentage points during the 1950's, by 5 percentage points during the 1960's, and by 8 percentage points during the 1970's. These two trends are of course closely related, for most married women have traditionally spent at least some minimal time out of the labour force while caring for their own children. Thus, for example, the slower growth rate of labour-force-participation rates in the 1950's is probably connected with the higher fertility rates in that period. In this paper a complete model of female labour supply and fertility choice is constructed and estimated. The model is more complete than previous models in several respects. First, labour supply and fertility decisions are modelled as completely joint in the same sense as the consumption of two goods is joint. Second, both are modelled as life-cycle decisions, that is, as decisions regarding sequences of labour-supply and fertility events rather than levels of the same. Hence the timing of both is important. Third, the life-cycle path of wage rates is made endogenous to the model-given a wage function showing the effect of work experience on future wages, the path of wages is determined automatically by the chosen path of labour supply, which in turn is related to fertility decisions that require some period of non-work. Econometrically, the three profiles for labour supply, fertility, and wages are estimated with a full-information maximum likelihood technique that solves several econometric problems, such as the problem of heterogeneity of tastes; the selectivity problem of missing wage rates for non-workers; and the problem of simultaneous-equations bias. The value of constructing a complete model is best seen by briefly reviewing past work in this area. The modern literature in the area is based upon the work of Becker

Profiles of fertility, labour supply and wages of married women: a complete life-cycle model.

Prediction in the context of the variance-components model

R. E. Attiyeh, G. L. Bach, and Keith Lumsden, "Factors Affecting Student Learning of Elementary Economics," in Recent Research in Economic Education, Keith Lumsden, Editor. Englewood Cliffs, N.J.: Prentice-Hall, Inc., 1970. G. L. Bach, "Student Learning in Basic Economics: An Evaluated Experimental Course," in New Developments in the Teaching ofEconomics, Keith G. Lumsden, Editor. Englewood Cliffs, N.J.: Prentice-Hall, 1967. Rendigs Fels, "Hard Research on a Soft Subject: Hypothesis Testing in Economic Education," The Southern Economic Journal, 35, (July 1969), 1-9. S. V. Whitney, "Measuring Success of Elementary Courses," The American Economic Review, 50 (March 1960), 159-169. Manual Test of Understanding in College Economics. New York: The Psychological Corporation, 1968.

http://www.jstor.org/stable/pdfplus/1182036.pdf

A Bias in Scores on Multiple-Form Exams

. A family, in appraising the value of a housewife's services, does not necessarily consider the services of the housewife to be free even though there is no explicit money payment for her services. When the housewife devotes time to housekeeping, either she sacrifices leisure or the family sacrifices labor market income which the wife could earn. Empirical evidence supports the notion that families do not consider the supply price of housekeeping services to be zero. Traditional methods of assessing the aggregate value of household services load to underestimates. The actual market wage rate is a good measure of the value of the marginal housekeeping services of a housewife who also spends time in the labor market. But the potential market wage rate of a full-time housewife understates the value of her marginal product in the home.

https://www.jstor.org/stable/info/3486200

The Value of Household Services

In this paper we employ Consumer Expenditure Survey data from the Bureau of Labor Statistics to estimate the percentage of household income that would have been used to purchase goods and services that would have been consumed by individual adult members of the household if there had been no wrongful death. These data previously have been utilized for the same purpose by Nelson and Patton (1984), Patton and Nelson (1991), Lierman, Patton and Nelson (1998), Ajwa, Martin & Vavoulis (2000) and Ruble, Patton and Nelson (2000). All these studies adopt the methodology originally put forward by Nelson and Patton in 1984 and Patton and Nelson in 1991. One problem with these earlier studies involves a simple and important idea–the determination of the number of adults in the consumer unit. This is important because we and other authors agree that expenditures for some goods and services are consumed only by adults, and expenditures for those goods should be allocated equally among the adults in a consumer unit. For some reason, Patton and Nelson’s 1991 study and the follow-up studies assume that any unit with two or more members always contains exactly two adults. Thus, any consumer unit expenditure allocated to all individual adult members was divided by two and allocated to individual adults accordingly. The Consumer Expenditure Survey data show that consumer units with two or more persons do not always, or on average, have two adults. We use the number of adults as shown in the survey data when we allocate expenditures among adults in a consumer unit. The determination of the number of adult members in the consumer units in the survey is important. The ultimate goal of consumption studies is to determine the percentage of consumer unit income that would have been used to purchase goods for the deceased family member and will now be avoided as a result of the death of that individual. Those expenditures that are assumed to be consumed only by adults in the consumer unit will obviously depend on the number of adults in the unit. A second issue we consider in this paper is the allocation scheme of expenditure categories. Our approach parallels the earlier studies in its allocation of several expenditure categories among various family members. Like the earlier studies, we treat some expenditure items as indivisible and conclude that the loss of a family member will not affect a family’s expenditure level for that item. Other expenditure items are, according to our approach and the approach of others, totally divisible either among all family members or among just the adults in the unit. The death of a family member will allow the family to re-

Adult Consumption Ratios: An Alternative Approach

THIS paper analyzes several problems associated with evaluating lifetime income streams within the context of a rising marginal tax structure. Our analysis was prompted by a recent U.S. Supreme Court decision in which the court held that evidence concerning the effects of federal income taxes on a decedent's future earnings is admissable in assessing damages in cases brought under the Federal Employer's Liability Act.' Income taxes have an impact at two points in the determination of a proper award: (a) they influence the disposable income which would have been available to the decedent had he lived; and (b) they influence the amount of the award required to reproduce this disposable income. Although the award proper is not taxable, the future interest earnings from the award are taxable, and the Court recognized that the amount of the award should be adjusted to reflect the tax on these interest earnings. The typical procedure for determining an award has been to calculate a present value by discounting future gross earnings of the decedent at some appropriate discount rate and using the present value of future earnings as the basis for an appropriate lump-sum award. It turns out, however, that adjusting for taxes makes it difficult to determine the appropriate award by applying standard discounting procedures. Our approach, therefore, is from the point of view of consumption alternatives rather than present values.2 This means that we ask first what consumption patterns (through time) would have been available had the decedent lived. For a given lifetime earnings pattern, the alternative consumption pat-

Allan J. Taub

Papers

Prediction in the context of the variance-components model

A Bias in Scores on Multiple-Form Exams

The Value of Household Services

Adult Consumption Ratios: An Alternative Approach

Taxes and Compensation for Lost Earnings