The Experts below are selected from a list of 1005 Experts worldwide ranked by ideXlab platform
Bing Zeng - One of the best experts on this subject based on the ideXlab platform.
-
wavelet a new tool for Business Cycle Analysis
2005Co-Authors: Sharif Md Raihan, Bing ZengAbstract:One basic problem in Business-Cycle 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, 2001Co-Authors: Sharif Md Raihan, Bing ZengAbstract: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 Business-Cycle 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, 2001Co-Authors: Sharif Md Raihan, Bing ZengAbstract: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 Business-Cycle 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 - One of the best experts on this subject based on the ideXlab platform.
-
putty clay and investment a Business Cycle Analysis
Journal of Political Economy, 2000Co-Authors: Simon Gilchrist, John C WilliamsAbstract: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, 1998Co-Authors: Simon Gilchrist, John C WilliamsAbstract: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 Business-Cycle and medium-run dynamics.
Christian Merkl - One of the best experts on this subject based on the ideXlab platform.
-
does short time work save jobs a Business Cycle Analysis
European Economic Review, 2016Co-Authors: Almut Balleer, Britta Gehrke, Wolfgang Lechthaler, Christian MerklAbstract: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, 2013Co-Authors: Christian Merkl, Almut Balleer, Britta Gehrke, Wolfgang LechthalerAbstract: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.
Sharif Md Raihan - One of the best experts on this subject based on the ideXlab platform.
-
wavelet a new tool for Business Cycle Analysis
2005Co-Authors: Sharif Md Raihan, Bing ZengAbstract:One basic problem in Business-Cycle 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, 2001Co-Authors: Sharif Md Raihan, Bing ZengAbstract: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 Business-Cycle 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, 2001Co-Authors: Sharif Md Raihan, Bing ZengAbstract: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 Business-Cycle 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.
Monica Billio - One of the best experts on this subject based on the ideXlab platform.
-
nonlinear dynamics and wavelets for Business Cycle Analysis
DYNAMIC MODELING AND ECONOMETRICS IN ECONOMICS AND FINANCE, 2014Co-Authors: Monica Billio, Peter Martey Addo, Dominique GueganAbstract:We provide a signal modality Analysis to characterize and detect nonlinearity schemes in the US Industrial Production Index time series. The Analysis is achieved by using the recently proposed “delay vector variance” (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV Analysis are obtained via a differential entropy based method using Fourier and wavelet-based surrogates. A complex Morlet wavelet is employed to detect and characterize the US Business Cycle. A comprehensive Analysis of the feasibility of this approach is provided. Our results coincide with the Business Cycles peaks and troughs dates published by the National Bureau of Economic Research (NBER).
-
Studies in Nonlinear Dynamics and Wavelets for Business Cycle Analysis
2013Co-Authors: Peter Martey Addo, Monica Billio, Dominique GueganAbstract:We provide a signal modality Analysis to characterize and detect nonlinearity schemes in the US Industrial Production Index time series. The Analysis is achieved by using the recently proposed ‘delay vector variance’ (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV Analysis are obtained via a differential entropy based method using Fourier and wavelet-based surrogates. A complex Morlet wavelet is employed to detect and characterize the US Business Cycle. A comprehensive Analysis of the feasibility of this approach is provided. Our results coincide with the Business Cycles peaks and troughs dates published by the National Bureau of Economic Research (NBER)
-
evaluation of regime switching models for real time Business Cycle Analysis of the euro area
Post-Print, 2013Co-Authors: Monica Billio, Laurent Ferrara, Dominique Guegan, Gian Luigi MazziAbstract:In this paper, we aim at assessing Markov switching and threshold models in their ability to identify turning points of economic Cycles. By using vintage data updated on a monthly basis, we compare their ability to date ex post the occurrence of turning points, evaluate the stability over time of the signal emitted by the models and assess their ability to detect in real-time recession signals. We show that the competitive use of these models provides a more robust Analysis and detection of turning points. To perform the complete Analysis, we have built a historical vintage database for the euro area going back to 1970 for two monthly macroeconomic variables of major importance for short-term economic outlook, namely the industrial production index and the unemployment rate. Copyright © 2013 John Wiley & Sons, Ltd.
-
beta autoregressive transition markov switching models for Business Cycle Analysis
Studies in Nonlinear Dynamics and Econometrics, 2011Co-Authors: Monica Billio, Roberto CasarinAbstract:We propose a new class of Markov-switching models useful for Business Cycle Analysis, with transition probabilities following independent beta autoregressive processes. We study the effects of the autoregressive dynamics on the regime duration. We propose a full Bayesian inference approach and particular attention is paid to the parameters of the latent beta autoregressive processes. We discuss the choice of the prior distributions and propose a Markov-chain Monte Carlo algorithm for estimating both the parameters and the latent variables. Finally, we provide an application to the Euro area Business Cycle.
-
bayesian estimation of stochastic transition markov switching models for Business Cycle Analysis
2010Co-Authors: Monica Billio, Roberto CasarinAbstract:We propose a new class of Markov-switching (MS) models for Business Cycle Analysis. As usually done in the literature, we assume that the MS latent factor is driving the dynamics of the Business Cycle but the transition probabilities can vary randomly over time. Transition probabilities are generated by random processes which may account for the stochastic duration of the regimes and for possible stochastic relations between the MS probabilities and some explanatory variables, such as autoregressive components and exogenous variables. The presence of latent factors and nonlinearities calls for the use of simulation-based inference methods. We propose a full Bayesian inference approach which can be naturally combined with Monte Carlo methods. We discuss the choice of the priors and a Markov-chain Monte Carlo (MCMC) algorithm for estimating the parameters and the latent variables. We provide an application of the model and of the MCMC procedure to data of Euro area. We also carry out a real-time comparison between different models by employing sequential Monte Carlo methods and some concordance statistics, which are widely used in Business Cycle Analysis.
