The Econometric Analysis of Recurrent Events in Macroeconomics and Finance

Description

The global financial crisis highlighted the impact on macroeconomic outcomes of recurrent events like business and financial cycles, highs and lows in volatility, and crashes and recessions. At the most basic level, such recurrent events can be summarized using binary indicators showing if the event will occur or not. These indicators are constructed either directly from data or indirectly through models. Because they are constructed, they have different properties than those arising in microeconometrics, and how one is to use them depends a lot on the method of construction. This book presents the econometric methods necessary for the successful modeling of recurrent events, providing valuable insights for policymakers, empirical researchers, and theorists. It explains why it is inherently difficult to forecast the onset of a recession in a way that provides useful guidance for active stabilization policy, with the consequence that policymakers should place more emphasis on making the economy robust to recessions. The book offers a range of econometric tools and techniques that researchers can use to measure recurrent events, summarize their properties, and evaluate how effectively economic and statistical models capture them. These methods also offer insights for developing models that are consistent with observed financial and real cycles. This book is an essential resource for students, academics, and researchers at central banks and institutions such as the International Monetary Fund. Cover Title Copyright Contents Series Editors’ Introduction Preface 1 Overview 1.1 Introduction 1.2 Describing the Events 1.3 Using the Event Indicators (“States”) 1.4 Prediction of Recurrent Events 1.5 Conclusion 2 Methods for Describing Oscillations, Fluctuations, and Cycles in Univariate Series 2.1 Introduction 2.2 Types of Movements in Real and Financial Series 2.3 Prescribed Rules for Dating Business Cycles 2.4 Prescribed Rules for Dating Other Types of Real Cycles 2.5 Prescribed Rules for Dating Financial Cycles 2.6 Relations between Cycles and Oscillations 2.7 The Nature of St and Its Modeling 2.8 Conclusion 3 Constructing Reference Cycles with Multivariate Information 3.1 Introduction 3.2 Determining the Reference Cycle via Phases 3.3 Combining Specific Cycle Turning Points 3.4 Finding Turning Points by Series Aggregation 3.5 Conclusion 4 Model-Based Rules for Describing Recurrent Events 4.1 Introduction 4.2 Dating Cycles with Univariate Series 4.3 Model-Based Rules for Dating Events with Multivariate Series 4.4 Conclusion 5 Measuring Recurrent Event Features in Univariate Data 5.1 Introduction 5.2 The Fraction of Time Spent in Expansions 5.3 Representing the Features of Phases 5.4 Amplitudes and Durations of Phases 5.5 The Shapes of Phases 5.6 The Diversity of Phases 5.7 Plucking Effects and Recovery Times 5.8 Duration Dependence in Phases 5.9 Conclusion 6 Measuring Synchronization of Recurrent Events in Multivariate Data 6.1 Introduction 6.2 Moment-Based Measures 6.3 Other Approaches to Measuring Synchronization 6.4 Synchronization and Model-Based Rules 6.5 Application to Synchronization of Industrial Production Cycles 6.6 Multivariate Synchronization 6.7 Comovement of Cycles 6.8 Conclusion 7 Accounting for Observed Cycle Features with a Range of Statistical Models 7.1 Introduction 7.2 U.S. Cycles as a Benchmark 7.3 The Business Cycle in a Range of Countries 7.4 Can U.S. Business Cycles Be Generated by Linear Models? 7.5 What Do Non-Linear Models Add? 7.6 Two Markov Switching Models 7.7 Using the Binary Indicators in Multivariate Systems 7.8 Conclusion 8 Using the Recurrent Event Binary States to Examine Economic Modeling Issues 8.1 Introduction 8.2 Estimating Univariate Models with Constructed Binary Data 8.3 What Do Variance Decompositions Tell Us About the Cycle? 8.4 The Role of Structural Shocks in Determining Cycle Features 8.5 Financial Effects and the Business Cycle 8.6 Conclusion 9 Predicting Turning Points and Recessions 9.1 Introduction 9.2 Bounding the Probability of the Occurrence of a Peak 9.3 Predicting Recessions with a Range of Variables 9.4 Changing the Event Defining Recessions and Turning Points 9.5 Conclusion References Index