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Business Cycle Analysis

The Experts below are selected from a list of 1005 Experts worldwide ranked by ideXlab platform

Bing Zeng – 1st expert on this subject based on the ideXlab platform

  • wavelet a new tool for Business Cycle Analysis
    , 2005
    Co-Authors: Sharif Md Raihan, Bing Zeng

    Abstract:

    One basic problem in BusinessCycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of Business Cycle Analysis, such as the correlation Analysis and the spectral Analysis, cannot capture such historical information because they do not take the time-varying characteristics of the Business Cycles into consideration. In this paper, we introduce and apply a new technique to the studies of the Business Cycle: the wavelet-based time-frequency Analysis that has recently been developed in the field of signal processing. This new method allows us to characterize and understand not only the timing of shocks that trigger the Business Cycle, but also situations where the frequency of the Business Cycle shifts in time. Our empirical analyses show that 1973 marks a new era for the evolution of the Business Cycle.

  • joint time frequency distributions for Business Cycle Analysis
    Lecture Notes in Computer Science, 2001
    Co-Authors: Sharif Md Raihan, Bing Zeng

    Abstract:

    The joint time-frequency Analysis (JTFA) is a signal processing technique in which signals are represented in both the time domain and the frequency domain simultaneously. Recently, this Analysis technique has become an extremely powerful tool for analyzing nonstationary time series. One basic problem in BusinessCycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of Business Cycle Analysis, such as the correlation Analysis and the spectral Analysis, cannot capture such historical information because they do not take the time-varying characteristics of the Business Cycles into consideration. In this paper, we introduce and apply a new technique to the studies of the Business Cycle: the wavelet-based time-frequency Analysis that has recently been developed in the field of signal processing. This new method allows us to characterize and understand not only the timing of shocks that trigger the Business Cycle, but also situations where the frequency of the Business Cycle shifts in time. Applying this new method to post war US data, we are able to show that 1973 marks a new era for the evolution of the Business Cycle since World War II.

  • WAA – Joint Time-Frequency Distributions for Business Cycle Analysis
    Lecture Notes in Computer Science, 2001
    Co-Authors: Sharif Md Raihan, Bing Zeng

    Abstract:

    The joint time-frequency Analysis (JTFA) is a signal processing technique in which signals are represented in both the time domain and the frequency domain simultaneously. Recently, this Analysis technique has become an extremely powerful tool for analyzing nonstationary time series. One basic problem in BusinessCycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of Business Cycle Analysis, such as the correlation Analysis and the spectral Analysis, cannot capture such historical information because they do not take the time-varying characteristics of the Business Cycles into consideration. In this paper, we introduce and apply a new technique to the studies of the Business Cycle: the wavelet-based time-frequency Analysis that has recently been developed in the field of signal processing. This new method allows us to characterize and understand not only the timing of shocks that trigger the Business Cycle, but also situations where the frequency of the Business Cycle shifts in time. Applying this new method to post war US data, we are able to show that 1973 marks a new era for the evolution of the Business Cycle since World War II.

John C Williams – 2nd expert on this subject based on the ideXlab platform

  • putty clay and investment a Business Cycle Analysis
    Journal of Political Economy, 2000
    Co-Authors: Simon Gilchrist, John C Williams

    Abstract:

    This paper develops a general equilibrium model with putty-clay technology, investment irreversibility, and variable capacity utilization. Low short-run capital-labor substitutability induces the putty-clay effect of a tight link between changes in capacity and movements in employment and output. Permanent shocks to technology or factor prices generate a hump-shaped response of hours, persistence in output growth, and positive comovement in the forecastable components of output and hours. Capacity constraints result in asymmetric responses to large shocks with recessions deeper than expansions. Estimation of a two-sector model supports a significant role for putty-clay capital in explaining Business Cycle and medium-run dynamics.

  • putty clay and investment a Business Cycle Analysis
    National Bureau of Economic Research, 1998
    Co-Authors: Simon Gilchrist, John C Williams

    Abstract:

    This paper develops a dynamic stochastic general equilibrium model with putty-clay technology that incorporates embodied technology, investment irreversibility, and variable capacity utilization. Low short-run capital-labor substitutability native to the putty-clay framework induces the putty-clay effect of a tight link between changes in capacity and movements in employment and output. As a result, persistent shocks to technology or factor prices generate Business Cycle dynamics absent in standard neoclassical models, including a prolonged lump-shaped response of hours, persistence in output growth, and positive comovement in the forecastable components of output and hours. Capacity constraints result in nonlinear aggregate production function that implies asymmetric responses to large shocks with recessions steeper and deeper than expansions. Minimum distance estimation of a two-sector model that nests putty-clay and neoclassical production technologies supports a significant role for putty-clay capital in explaining BusinessCycle and medium-run dynamics.

Christian Merkl – 3rd expert on this subject based on the ideXlab platform

  • does short time work save jobs a Business Cycle Analysis
    European Economic Review, 2016
    Co-Authors: Almut Balleer, Christian Merkl, Britta Gehrke, Wolfgang Lechthaler

    Abstract:

    In the Great Recession most OECD countries used short-time work (publicly subsidized working time reductions) to counteract a steep increase in unemployment. We show that short-time work can actually save jobs. However, there is an important distinction to be made: While the rule-based component of short-time work is a cost-efficient job saver, the discretionary component appears to be completely ineffective. In a case study for Germany, we use the rich data available to combine micro- and macroeconomic evidence with macroeconomic modeling in order to identify, quantify and interpret these two components of short-time work.

  • does short time work save jobs a Business Cycle Analysis
    Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order, 2013
    Co-Authors: Christian Merkl, Almut Balleer, Britta Gehrke, Wolfgang Lechthaler

    Abstract:

    This paper analyzes the effects of short-time work (i.e., government subsidized working time reductions) on unemployment and output fluctuations. The central question is whether short-time work saves jobs in recessions. In our baseline scenario the rule based component of short-time work (i.e., due to the existence of the institution) stabilizes unemployment fluctuations by 15% and output fluctuations by 7%. Given the small share of short-time work expenses in terms of GDP, the stabilization effects are large compared to other instruments such as the income tax system. By contrast, discretionary short-time work interventions (i.e., rule changes) do not have any statistically significant effect on unemployment. These effects are based on a SVAR estimation,which uses an elasticity of the German establishment panel for identification purposes. The model shows that non-effects of discretionary interventions (i.e., 100% deadweight) may be due to their low persistence.