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 survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

/pdf/tensor-decompositions-and-applications-46vl2ih5gn.pdf

Tensor Decompositions and Applications

https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(12)61729-2.pdf

Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010

Structural Equations with Latent Variables.

Much experimental evidence indicates that choice depends on the status quo or reference level: changes of reference point often lead to reversals of preference. We present a reference-dependent theory of consumer choice, which explains such effects by a deformation of indifference curves about the reference point. The central assumption of the theory is that losses and disadvantages have greater impact on preferences than gains and advantages. Implications of loss aversion for economic behavior are considered. The standard models of decision making assume that preferences do not depend on current assets. This assumption greatly simplifies the analysis of individual choice and the prediction of trades: indifference curves are drawn without reference to current holdings, and the Coase theorem asserts that, except for transaction costs, initial entitlements do not affect final allocations. The facts of the matter are more complex. There is substantial evidence that initial entitlements do matter and that the rate of exchange between goods can be quite different depending on which is acquired and which is given up, even in the absence of transaction costs or income effects. In accord with a psychological analysis of value, reference levels play a large role in determining preferences. In the present paper we review the evidence for this proposition and offer a theory that generalizes the standard model by introducing a reference state. The present analysis of riskless choice extends our treatment of choice under uncertainty [Kahneman and Tversky, 1979, 1984; Tversky and Kahneman, 1991], in which the outcomes of risky prospects are evaluated by a value function that has three essential characteristics. Reference dependence: the carriers of value are gains and losses defined relative to a reference point. Loss aversion: the function is steeper in the negative than in the positive domain; losses loom larger than corresponding gains. Diminishing sensitivity: the marginal value of both gains and losses decreases with their

/pdf/loss-aversion-in-riskless-choice-a-reference-dependent-model-4af6fwvxjx.pdf

Loss Aversion in Riskless Choice: A Reference-Dependent Model

In this paper, a new approach to defining the poverty line is proposed in which family heads are asked what they consider a minimal income level for their own family. It was found that the respondents appeared to specify higher amounts, the greater their actual income and family size, and that the relationship was loglinear. For each family size there is an income level at which a respondent's stated minimum income is equal to his actual income. This level is taken as a definition of the poverty line. The poverty line thus defined varies with family size.

The poverty line: Concept and measurement

/pdf/the-relativity-of-utility-evidence-from-panel-data-2a46r6fw0m.pdf

The relativity of utility : Evidence from panel data

First Results from the Survey of Health, Ageing, and Retirement in Europe (2004-2007) : Starting the longitudinal dimension

In contrast to the believed similarity in their health outcomes, workers in different Western countries report very different rates of work disability. Using new data from the United States and the Netherlands, we offer a partial resolution to this paradox. We find that observed differences in reported work disability largely stem from the fact that Dutch respondents have a lower threshold in reporting whether they have a work disability than American respondents. For those who do not suffer from pain, work disability is similar in both countries once thresholds are the same. For respondents with pain, however, a significant difference remains.

/pdf/vignettes-and-self-reports-of-work-disability-in-the-united-49kn3izro7.pdf

Vignettes and Self-Reports of Work Disability in the United States and the Netherlands

Each week, the Dutch Postcode Lottery (PCL) randomly selects a postal code, and distributes cash and a new BMW to lottery participants in that code. We study the effects of these shocks on lottery winners and their neighbors. Consistent with the life-cycle hypothesis, the effects on winners' consumption are largely confined to cars and other durables. Consistent with the theory of in-kind transfers, the vast majority of BMW winners liquidate their BMWs. We do, however, detect substantial social effects of lottery winnings: PCL nonparticipants who live next door to winners have significantly higher levels of car consumption than other nonparticipants.

/pdf/the-effects-of-lottery-prizes-on-winners-and-their-neighbors-3pxximgv24.pdf

Arie Kapteyn

Papers

The poverty line: Concept and measurement

The relativity of utility : Evidence from panel data

First Results from the Survey of Health, Ageing, and Retirement in Europe (2004-2007) : Starting the longitudinal dimension

Vignettes and Self-Reports of Work Disability in the United States and the Netherlands

The Effects of Lottery Prizes on Winners and Their Neighbors: Evidence from the Dutch Postcode Lottery