The chapter looks at how recurrent events might be dated by using a number of series rather than a single one. There is no one method to do this, but in many cases, the procedures come up with rather similar results. Much depends on how one wants to use the dating information. If it is for judging models or evaluating some macroeconomic propositions, then the ability to automate the selection process easily would be a paramount consideration. Alternatively, if it was desired to establish a definitive dating of cycles in activity or financial series, then it is probably the case that a variety of methods would be used with some judgment applied when determining the weight given to each. Further work is needed on how the methods perform when faced with simulations from estimated models rather than using actual data, as their effectiveness is unclear in such a changed environment.

This chapter presents methods for capturing the synchronization of recurrent events in bivariate and multiple series. The special features of the unconditional densities of binary series recommend the use of moment-based measures of synchronization. It looks at similarity across events in terms of a range of features, such as amplitudes. It then looks at the situation when model-based rules are used to define them, and further gives an application of the methods to studying the synchronization of cycles in industrial production across countries. The question often arises of whether there is synchronization of the events across a number of industries, countries, and so on. This involves multivariate synchronization and this is studied in the chapter. Finally, the chapter examines the relationship between the synchronization of cycles and the comovement in the continuous variables in which those cycles occur.

This chapter looks at using the binary states describing the recurrent events to help in either constructing economic models of time series or evaluating the fit of such models. The chapter provides a general discussion of the issues that come up when using the binary states in regressions. It then turns to the analysis of complete economic models. In these it is very common to see variance decompositions computed and used to draw conclusions about which shocks are responsible for the recurrent events. It is shown that this methodology is flawed when it comes to shedding light on what causes the business cycle. What can be done is investigated in the chapter, which illustrates how to determine which shocks are important to a matching of the business cycle features discussed in Chapter 5. The discussion moves on to some economic models that have been constructed in the wake of the global financial crisis and which aim to highlight the role of financial shocks.

The chapter discusses a particular way of producing rules to summarize the nature of the recurrent events. These rules come from the idea that the data incorporating the recurrent event can be captured by models that specify a number of regimes, and then using the information provided by the fitted model to date the recurrent event. The chapter discusses variants of Markov switching models in the context where there is only a single series in which the recurrent event is observed. It then deals with dating cycles with univariate series. Finally, it considers model-based rules for dating events with multivariate series.

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 chapter argues that the problems in predicting recessions stem from the nature of the definition of a recession. Much of the literature that claims success does not predict recessions as such. Rather it focuses on either whether one can predict growth in economic activity or whether one can identify the current status of the economy—what is often referred to as “nowcasting” rather than forecasting. The chapter reviews the literature on predicting recessions. The review leads to the conclusion that there are good reasons it is extremely difficult to predict recessions. Understanding these leads to an appreciation of the barriers to be faced in the task, and also suggests that many of the claims made about how the forecasting record can be improved should be treated with skepticism.

This chapter investigates the three ways that economic researchers have approached the task of describing the ups and downs in the macroeconomy: oscillations, fluctuations, and cycles. Each has a different way of describing the phenomenon being investigated and summarizing it. These vary depending on the frequency of the data one is working with. Moreover, often many series rather than a single one are used to describe the recurrent pattern. Oscillations refer to the fact that peaks and troughs occur in a regular fashion. Cycles refer to the fact that the ups and downs in economic activity can be seen in a graph as a set of local peaks and troughs. Fluctuations come from the observation that a series showing ups and downs can be said to exhibit volatility.

This chapter looks at observed features of the cycle in a variety of time series. It sets out these features for the United States and a number of other countries, and then asks whether these features can be replicated by the use of a particular statistical model—a linear autoregression. For such linear models it is possible to broadly account for the observed features using moments of the series for growth rates, and this strategy is employed in the chapter. It then uses a particular nonlinear statistical model to see if it can match all the features, and further looks at two other nonlinear models first dealt with in Chapter 4. The chapter concludes with an examination of whether the binary indicators summarizing the recurrent states can be used in the context of standard multivariate methods such as vector autoregressions. This turns out not to be straightforward owing to the nature of the binary variables.

This chapter begins with a discussion of why we would expect to find that the time spent in expansions (bull markets, etc.) would be much greater than the time spent in contractions (bear markets, etc.). By focusing on the probabilities of getting particular outcomes for the binary variables summarizing the recurrent events, we can provide an explanation of this long-observed feature. The remainder of the chapter looks at many proposals for summarizing other features of the recurrent events. These involve well-known quantities such as durations and amplitudes, as well as lesser known ones, such as the sharpness of peaks and troughs.

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Economic events such as expansions and recessions in economic activity, bull and bear markets in stock prices and financial crises have long attracted substantial interest. In recent times there has been a focus upon predicting the events and constructing Early Warning Systems of them. Econometric analysis of such recurrent events is however in its infancy. One can represent the events as a set of binary indicators. However they are different to the binary random variables studied in micro-econometrics, being constructed from some (possibly) continuous data. The lecture discusses what difference this makes to their econometric analysis. It sets out a framework which deals with how the binary variables are constructed, what an appropriate estimation procedure would be, and the implications for the prediction of them. An example based on Turkish business cycles is used throughout the lecture.

Macroeconometric and financial researchers often use binary data constructed in a way that creates serial dependence. We show that this dependence can be allowed for if the binary states are treated as Markov processes. In addition, the methods of construction ensure that certain sequences are never observed in the constructed data. Together these features make it difficult to utilize static and dynamic Probit models. We develop modeling methods that respect the Markov-process nature of constructed binary data and explicitly deals with censoring constraints. An application is provided that investigates the relation between the business cycle and the yield spread.

The key feature of the pit is that they are bounded below by 0 and above by 1. Thus we are guaranteed that all moments of (p1t, ..., pIt) exist for all data-generating processes for the nominal price vector (P1t, ..., PIt), something that is not true of (q1t, ..., qIt). This means that one can use moments to summarise the features of (p1t, ..., pIt), something that we cannot be assured is valid when summarising the features of (q1t, ..., qIt). A second important difference between Definitions (1) and (2) is that the former depends on the choice of numerator while the latter does not. This means that summary statistics built on Definition (1) will depend on the choice of numerator.

The fact that the Global Financial Crisis, and the Great Recession it ushered in, was largely unforeseen, has led to the common opinion that macroeconomic models and analysis is deficient in some way. Of course it has probably always been true that businessmen, journalists and politicians have agreed on the proposition that economists can't forecast recessions. Yet we see an enormous published literature that presents results which suggest it is possible to do so, either with some new model or some new estimation method e.g. Kaufman (2010), Galvao (2006), Dueker (2005), Wright (2006) and Moneta (2005). Moreover, there seem to be no shortage of papers still emerging that make claims along these lines. So a question that naturally arises is how one is to reconcile the existence of an expanding literature on predicting recessions with the scepticism noted above?

To match the NBER business cycle features it is necessary to employ Generalised Dynamic Categorical (GDC) models that impose certain phase restrictions and permit multiple indexes. Theory suggests additional shape restrictions in the form of monotonicity and boundedness of certain transition probabilities. Maximum likelihood and constraint weighted bootstrap estimators are developed to impose these restrictions. In the application these estimators generate improved estimates of how the probability of recession varies with the yield spread.

The review and evaluation of some of Paul Krugman's macroeconomic analysis is discussed. Krugman's macroeconomic analysis has been quite inconsistent and his policy recommendations have been found to lack the required consistency and coherency.

This paper examines recessions and recoveries in advanced economies and the role of countercyclical macroeconomic policies. Are recessions and recoveries associated with financial crises different from others? What are the main features of globally synchronized recessions? Can countercyclical policies help to shorten recessions and strengthen recoveries? The results suggest that recessions associated with financial crises tend to be unusually severe and that recoveries from such recessions are typically slow. Similarly, globally synchronized recessions are often long and deep, and recoveries from these recessions are generally weak. Countercyclical monetary policy can help shorten recessions, but its effectiveness is limited in financial crises. By contrast, expansionary fiscal policy seems particularly effective in shortening recessions associated with financial crises and in boosting recoveries. However, its effectiveness is a decreasing function of the level of public debt. These findings suggest that the current recession is likely to be unusually long and severe and the recovery sluggish.

This paper is concerned with the issues that arise in building a small Dynamic Stochastic General Equilibrium (DSGE) model of the Australian economy. Our ultimate objective is to build a model that can be used to study long run economic growth and the business cycle. We agree with Cooley and Prescot�s (1995) view that these are phenomena to be studied jointly rather than separately. Adopting this view has several implications for what constitutes the essential components of our a model. We see these as being: a major role for a persistent technology shock in driving economic activity; and consistency with a version of the Ramsey-Cass-Koopmans (RCK) exogenous growth model. Without the former it is not possible to generate realistic business cycle features; demand shocks alone are insuffcient see Harding and Pagan (2007). The RCK exogenous growth model remains the simplest model available to encompass the salient features of economic growth which is why we rate it as essential. We also take the methodological stance that it is desirable to obtain a satisfactory baseline model before adding other desirable features such as: money; openness to international trade, capital flows, and immigration; and price and wage stickiness. In short we see small real business cycle (RBC) models as the natural starting point for our work.