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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.xml_char_ref(d;) 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
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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.
E-books --- Economics --- Statistical methods --- Economic statistics --- Econometrics --- Statistical methods. --- Macroeconomics --- Econometrics. --- Econometric models. --- Business cycles --- Mathematical models. --- Economics, Mathematical --- Statistics --- Mathematical models --- Markov switching models. --- amplitudes. --- binary states. --- bivariate series. --- business cycles. --- contraction. --- cycles financial series. --- cycles. --- dating cycles. --- dating. --- durations. --- economic activity. --- economic models. --- economic recessions. --- economy. --- event indicators. --- expansion. --- financial cycles. --- financial shocks. --- fluctuation. --- global financial crisis. --- linear autoregression. --- macroeconomy. --- microeconometrics. --- model-based rules. --- multiple series. --- oscillation. --- peaks. --- policymakers. --- prediction. --- recession. --- recurrent events. --- recurrent states. --- regression. --- statistics. --- synchronization. --- time series. --- time. --- troughs. --- univariate series. --- volatility.
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This paper compares several statistical models for monthly stock return volatility. The focus is on U.S. data from 1834-19:5 because the post-1926 data have been analyzed in more detail by others. Also, the Great Depression had levels of stock volatility that are inconsistent with stationary models for conditional heteroskedasticity, We show the importance of nonlinearities in stock return behavior that are not captured by conventional ARCH or GARCH models. We also show the nonstationariry of stock volatility, even over the 1834-1925 period.
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