Search results

Intro -- Preface -- Contents -- Part I Fundamental Theory -- Validating Markov Switching VAR Through Spectral Representations -- 1 Introduction -- 2 Spectra of Markov Switching VAR -- 2.1 The Case of Hidden Markov Process -- 2.2 The Case of MS VAR(p) -- 3 Frequency Variability in Real Data -- 4 Conclusion -- References -- Rapid Optimal Lag Order Detection and Parameter Estimation of Standard Long Memory Time Series -- 1 Introduction -- 2 Preliminaries -- 2.1 Fractionally Differenced Long Memory Processes -- 3 State Space Representation of an ARFIMA Time Series -- 3.1 State Space Representation of ARFIMA Model -- 3.2 KF and Estimation Process -- 4 Simulation Results -- 5 Empirical Evidence -- 6 Concluding Remarks -- References -- Spatial Econometric Analysis: Potential Contribution to the Economic Analysis of Smallholder Development -- 1 Introduction -- 2 Advances in Data to Capture Spatial Heterogeneity -- 2.1 GIS and GPS Mapping -- 2.2 Big Data -- 2.3 Increased Availability of Panel Data Sets -- 3 Review of Existing Literature on Spatial Econometric Analysis of Smallholder Development -- 3.1 Recent Progress in Spatial Econometric Modelling -- 3.2 Use of Spatial Econometric Analysis to Study Spillovers and Spatial Interaction -- 3.3 Environmental and Land Use Applications -- 3.4 Accounting for Space in Analyses of Technology Adoption and Productivity -- 4 Potential Areas for Analysis Using Spatial Econometric Methods: Examples from the Philippines -- 4.1 Spatial Heterogeneity in the Rural Sector of the Philippines: Example of Rice Ecosystems -- 4.2 Assessment of Smallholder Response to Rural Development Interventions -- 4.3 Measuring, Decomposing and Explaining TFP Growth in Smallholder Farming -- 5 Prospects and Conclusions -- References -- Consistent Re-Calibration in Yield Curve Modeling: An Example -- 1 Introduction.

Cover -- Half Title -- Title -- Copyright -- Dedication -- Table of Contents -- Preface -- I: INTRODUCTION -- Causal Thinking, Theory, and Operationalism -- The Concept of Causality -- Some Problems with Causal Thinking -- Causal Models -- Inferences from Experimental and Nonexperimental Designs -- Concluding Remarks -- II: MATHEMATICAL REPRESENTATIONS OF CAUSAL MODELS -- Causality, Attributes, and Necessary Conditions -- Independent and Dependent Variables: the Problem of Asymmetry -- Prediction Versus Causal Inferences -- Outside Influences, Error Terms, and Correlations -- The Use of Simultaneous Equations -- III: EVALUATING CAUSAL MODELS -- Rationale for Testing Models -- Specific Causal Models -- Numerical Applications -- Some Cautions -- Concluding Remarks -- IV: INFERENCES BASED ON CHANGES IN NONEXPERIMENTAL DESIGNS -- Changes in Units of Analysis -- Comparisons with Same Units, Different Variations -- Comparisons Involving Change Data -- Concluding Remarks -- V: COMPLICATING FACTORS -- Reducing the Effects of Confounding Influences -- Measurement Error in Nonexperimental Studies -- Inferences Involving Unmeasured Variables -- Concluding Remarks -- VI: SUMMARY AND CONCLUSIONS -- Summary -- Concluding Remarks -- APPENDIX: SOME RELATED APPROACHES -- INDEX -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- R -- S -- T -- U -- W -- Z

The problem of drawing causal inferences from retrospective case-control studies is considered. A model for causal inference in prospective studies is reviewed and then applied to retrospective studies. The limitations of case-control studies are formulated in terms of the level of causally relevant parameters that can be estimated in such studies. An example using data from a large retrospective study of coffee-drinking and myocardial infarctions is used to illustrate the ideas of the article.

Originating in econometrics and statistics, the counterfactual model provides a natural framework for clarifying the requirements for valid causal inference in the social sciences. This article presents the basic potential outcomes model and discusses the main approaches to identification in social science research. It then addresses approaches to the statistical estimation of treatment effects either under unconfoundedness or in the presence of unmeasured heterogeneity. As an update to Winship & Morgan's (1999) earlier review, the article summarizes the more recent literature that is characterized by a broader range of estimands of interest, a renewed interest in exploiting experimental and quasi-experimental designs, and important progress in the areas of semi- and nonparametric estimation of treatment effects, difference-in-differences estimation, and instrumental variable estimation. The review concludes by highlighting implications of the recent econometric and statistical literature for sociological research practice.

Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interpreted in a way that does not depend on it. Nevertheless, he shows that this more charitable version of the account is still unsuccessful as it stands. Consequently, he advances a proposal for shoring up the account, which is founded on the possibility of acquiring knowledge of social mechanisms by linking together norms or practices found in a society.