https://www.sfu.ca/~wainwrig/Econ400/jensen-meckling.pdf

Theory of the firm: Managerial behavior, agency costs and ownership structure

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.

/pdf/econometric-analysis-of-cross-section-and-panel-data-3jzr8emmtf.pdf

Econometric Analysis of Cross Section and Panel Data

https://www.ucl.ac.uk/~uctp39a/Blundell-Bond-1998.pdf

Initial conditions and moment restrictions in dynamic panel data models

Existing strategies for econometric analysis related to macroeconomics are subject to a number of serious objections, some recently formulated, some old. These objections are summarized in this paper, and it is argued that taken together they make it unlikely that macroeconomic models are in fact over identified, as the existing statistical theory usually assumes. The implications of this conclusion are explored, and an example of econometric work in a non-standard style, taking account of the objections to the standard style, is presented. THE STUDY OF THE BUSINESS cycle, fluctuations in aggregate measures of economic activity and prices over periods from one to ten years or so, constitutes or motivates a large part of what we call macroeconomics. Most economists would agree that there are many macroeconomic variables whose cyclical fluctuations are of interest, and would agree further that fluctuations in these series are interrelated. It would seem to follow almost tautologically that statistical models involving large numbers of macroeconomic variables ought to be the arena within which macroeconomic theories confront reality and thereby each other. Instead, though large-scale statistical macroeconomic models exist and are by some criteria successful, a deep vein of skepticism about the value of these models runs through that part of the economics profession not actively engaged in constructing or using them. It is still rare for empirical research in macroeconomics to be planned and executed within the framework of one of the large models. In this lecture I intend to discuss some aspects of this situation, attempting both to offer some explanations and to suggest some means for improvement. I will argue that the style in which their builders construct claims for a connection between these models and reality-the style in which "identification" is achieved for these models-is inappropriate, to the point at which claims for identification in these models cannot be taken seriously. This is a venerable assertion; and there are some good old reasons for believing it;2 but there are also some reasons which have been more recently put forth. After developing the conclusion that the identification claimed for existing large-scale models is incredible, I will discuss what ought to be done in consequence. The line of argument is: large-scale models do perform useful forecasting and policy-analysis functions despite their incredible identification; the restrictions imposed in the usual style of identification are neither essential to constructing a model which can perform these functions nor innocuous; an alternative style of identification is available and practical. Finally we will look at some empirical work based on an alternative style of macroeconometrics. A six-variable dynamic system is estimated without using 1 Research for this paper was supported by NSF Grant Soc-76-02482. Lars Hansen executed the computations. The paper has benefited from comments by many people, especially Thomas J. Sargent

/pdf/macroeconomics-and-reality-3v6kkpl8jz.pdf

Macroeconomics and reality

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.

/pdf/econometric-analysis-of-panel-data-46ld72a5p1.pdf

Econometric Analysis of Panel Data

Creeping bentgrass (Agrostis stolonifera L.) is the most common cool-season grass used for putting surfaces in Kansas and other temperate regions of the world. Fine-textured and high-density cultivars (‘Penn A-1’, ‘Penn A-2’, and ‘Penn A-4’) released in the late 1990s are widely accepted throughout the transition zone. However, new cultivars with attractive characteristics continue to be selected, evaluated, and introduced to determine their long-term suitability for Kansas.

2021 Creeping Bentgrass Putting Green National Turfgrass Evaluation Program Trial

Comments on "Measuring disease prevalence in surveys: A comparison of diabetes self-reports,

Abstract When a thatch layer accumulates on turfgrass it can be detrimental to the stand. A field experiment was initiated to investigate aerification treatments and their influence on thatch (organic matter level), quality, and color of ‘Innovation’ zoysiagrass that was sodded within the past year. Turfgrass that was intensely aerified had less organic matter content in the surface inch of the profile compared to turfgrass that was not aerified. Color was also enhanced in treatments receiving aerification compared to non-aerified turf, which may have been attributed to trending of higher nitrate content in aerified plots. Summary When a thatch layer accumulates on turfgrass it can be detrimental to the stand. A field experiment was initiated to investigate aerification treatments and their influence on thatch (organic matter level), quality, and color of ‘Innovation’ zoysiagrass that was sodded within the past year. Turfgrass that was intensely aerified had less organic matter content in the surface inch of the profile compared to turfgrass that was not aerified. Color was also enhanced in treatments receiving aerification compared to non-aerified turf, which may have been attributed to trending of higher nitrate content in aerified plots. The of this research to determine the influence of aerification on thatch levels and rooting of Innovation zoysiagrass.

https://newprairiepress.org/cgi/viewcontent.cgi?article=8325&context=kaesrr

Aerification Effects on ‘Innovation’ Zoysiagrass in 2020-2021

Administrative data such as insurance claims offer a potentially powerful data source to examine the relative benefits and costs of competing drug treatments. Motivated by a 2011 FDA warning about possible side effects of angiotensin-II receptor blockers (ARBs), we analyze the benefits and risks of ARBs compared to other classes of hypertension drugs using Medicare Parts A, B, and D data between 2006 and 2009. We study treatment adherence and crossover as well as non-random treatment assignment in detail and illustrate how different approaches to handling these issues impact our results. We find little evidence that ARBs increase cancer rates and weak evidence that they increase stroke rates, but falsification tests raise doubts that any associations are causal. Overall, our results suggest comprehensive, robust analyses are needed in using observational data for comparative effectiveness analysis.

A Cautionary Tale in Comparative Effectiveness Research: Pitfalls and Perils of Observational Data Analysis

This paper considers discrete choice, with choice probabilities coming from maximization of preferences from a random utility field perturbed by additive location shifters (ARUM). Any ARUM can be characterized by a choice-probability generating function (CPGF) whose gradient gives the choice probabilities, and every CPGF is consistent with an ARUM. We relate CPGF to multivariate extreme value distributions, and review and extend methods for constructing CPGF for applications.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2035316

Daniel McFadden

Papers

2021 Creeping Bentgrass Putting Green National Turfgrass Evaluation Program Trial

Comments on "Measuring disease prevalence in surveys: A comparison of diabetes self-reports,

Aerification Effects on ‘Innovation’ Zoysiagrass in 2020-2021

A Cautionary Tale in Comparative Effectiveness Research: Pitfalls and Perils of Observational Data Analysis

Choice Probability Generating Functions