999 resultados para Common cycles


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In this paper we analyze per capita incomes of the G7 countries using the common cycles test developed by Vahid and Engle (Journal of Applied Econometrics, 8:341–360, 1993) and extended by Hecq et al. (Oxford Bulletin of Economics and Statistics, 62:511–532, 2000; Econometric Reviews, 21:273–307, 2002) and the common trend test developed by Johansen (Journal of Economic Dynamics and Control, 12:231–254, 1988). Our main contribution is that we impose the common cycle and common trend restrictions in decomposing the innovations into permanent and transitory components. Our main finding is permanent shocks explain the bulk of the variations in incomes for the G7 countries over short time horizons, and is in sharp contrast to the bulk of the recent literature. We attribute this to the greater forecasting accuracy achieved, which we later confirm through performing a post sample forecasting exercise, from the variance decomposition analysis.

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In this paper we examine the role of permanent and transitory shocks in explaining variations in the S&P 500, Dow Jones and the NASDAQ. Our modeling technique involves imposing both common trend and common cycle restrictions in extracting the variance decomposition of shocks. We find that: (1) the three stock price indices are characterized by a common trend and common cycle relationship; and (2) permanent shocks explain the bulk of the variations in stock prices over short horizons.

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Although there has been substantial research on long-run co-movement (common trends) in the empirical macroeconomics literature. little or no work has been done on short run co-movement (common cycles). Investigating common cycles is important on two grounds: first. their existence is an implication of most dynamic macroeconomic models. Second. they impose important restrictions on dynamic systems. Which can be used for efficient estimation and forecasting. In this paper. using a methodology that takes into account short- and long-run co-movement restrictions. we investigate their existence in a multivariate data set containing U.S. per-capita output. consumption. and investment. As predicted by theory. the data have common trends and common cycles. Based on the results of a post-sample forecasting comparison between restricted and unrestricted systems. we show that a non-trivial loss of efficiency results when common cycles are ignored. If permanent shocks are associated with changes in productivity. the latter fails to be an important source of variation for output and investment contradicting simple aggregate dynamic models. Nevertheless. these shocks play a very important role in explaining the variation of consumption. Showing evidence of smoothing. Furthermore. it seems that permanent shocks to output play a much more important role in explaining unemployment fluctuations than previously thought.

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Reduced form estimation of multivariate data sets currently takes into account long-run co-movement restrictions by using Vector Error Correction Models (VECM' s). However, short-run co-movement restrictions are completely ignored. This paper proposes a way of taking into account short-and long-run co-movement restrictions in multivariate data sets, leading to efficient estimation of VECM' s. It enables a more precise trend-cycle decomposition of the data which imposes no untested restrictions to recover these two components. The proposed methodology is applied to a multivariate data set containing U.S. per-capita output, consumption and investment Based on the results of a post-sample forecasting comparison between restricted and unrestricted VECM' s, we show that a non-trivial loss of efficiency results whenever short-run co-movement restrictions are ignored. While permanent shocks to consumption still play a very important role in explaining consumption’s variation, it seems that the improved estimates of trends and cycles of output, consumption, and investment show evidence of a more important role for transitory shocks than previously suspected. Furthermore, contrary to previous evidence, it seems that permanent shocks to output play a much more important role in explaining unemployment fluctuations.

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This paper investigates the degree of short run and long run co-movement in U.S. sectoral output data by estimating sectoraI trends and cycles. A theoretical model based on Long and Plosser (1983) is used to derive a reduced form for sectoral output from first principles. Cointegration and common features (cycles) tests are performed; sectoral output data seem to share a relatively high number of common trends and a relatively low number of common cycles. A special trend-cycle decomposition of the data set is performed and the results indicate a very similar cyclical behavior across sectors and a very different behavior for trends. Indeed. sectors cyclical components appear as one. In a variance decomposition analysis, prominent sectors such as Manufacturing and Wholesale/Retail Trade exhibit relatively important transitory shocks.

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We present empirical evidence about the properties of economic sentiment cycle synchronization for Germany, France and the UK and compare them with the `crisis' countries Italy, Spain, Portugal and Greece. Instead of using output data we prefer to focus on the economic sentiment indicator (ESI), a forward-looking, survey-based variable consistently available from 1985. The cyclical nature of the ESI allows us to analyze the presence or not of synchronicity among country pairs before and after the onset of the financial crisis. Our results show that ESI movements were mostly synchronous before 2008 but they exhibit a breakdown after 2008, with this feature being more prominent in Greece. We also find that, after the political manoeuvring of the past two years, a cycle re-integration or re-synchronization is on the way. An analysis of the evolution of the synchronicity measures indicates that they can potentially be used to identify sudden phase breaks in ESI co-movement and they can offer a signal as to when the EU economies are getting “in” or “out of sync”.

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It is well known that cointegration between the level of two variables (labeled Yt and yt in this paper) is a necessary condition to assess the empirical validity of a present-value model (PV and PVM, respectively, hereafter) linking them. The work on cointegration has been so prevalent that it is often overlooked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. The basis of this result is the use of rational expectations in forecasting future values of variables in the PVM. If this condition fails, the present-value equation will not be valid, since it will contain an additional term capturing the (non-zero) conditional expected value of future error terms. Our article has a few novel contributions, but two stand out. First, in testing for PVMs, we advise to split the restrictions implied by PV relationships into orthogonality conditions (or reduced rank restrictions) before additional tests on the value of parameters. We show that PV relationships entail a weak-form common feature relationship as in Hecq, Palm, and Urbain (2006) and in Athanasopoulos, Guillén, Issler and Vahid (2011) and also a polynomial serial-correlation common feature relationship as in Cubadda and Hecq (2001), which represent restrictions on dynamic models which allow several tests for the existence of PV relationships to be used. Because these relationships occur mostly with nancial data, we propose tests based on generalized method of moment (GMM) estimates, where it is straightforward to propose robust tests in the presence of heteroskedasticity. We also propose a robust Wald test developed to investigate the presence of reduced rank models. Their performance is evaluated in a Monte-Carlo exercise. Second, in the context of asset pricing, we propose applying a permanent-transitory (PT) decomposition based on Beveridge and Nelson (1981), which focus on extracting the long-run component of asset prices, a key concept in modern nancial theory as discussed in Alvarez and Jermann (2005), Hansen and Scheinkman (2009), and Nieuwerburgh, Lustig, Verdelhan (2010). Here again we can exploit the results developed in the common cycle literature to easily extract permament and transitory components under both long and also short-run restrictions. The techniques discussed herein are applied to long span annual data on long- and short-term interest rates and on price and dividend for the U.S. economy. In both applications we do not reject the existence of a common cyclical feature vector linking these two series. Extracting the long-run component shows the usefulness of our approach and highlights the presence of asset-pricing bubbles.

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In this paper we examine the relative importance of permanent and transitory shocks in explaining variations in income, consumption and investment at business cycle horizons for Australia. We use the common trend–common cycle restrictions to estimate a variance decomposition of shocks, and find that over short horizons the bulk of the variations in income and investment are due to permanent shocks, while transitory shocks explain the bulk of the variations in consumption. The former finding is consistent with real business cycle models which attribute business cycles to aggregate supply shocks, while the findings for consumption are consistent with the Keynesian view, which attributes business cycles to aggregate demand shocks.

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This paper uses canonical correlation analisys to identify leading and coincident indicators of economic activity in Brazil. ln contrast with the traditional literature on the subject, no restrictions are made regarding the number of common cycles that are necessary to explain the complete cyclical behavior of the coincident variables. For the brazillian data, it is found that three common cycles exhaust all the cyclical pattern of economic activity. Based on the methodology developed here, it is also sugested an alternative chronology of the recent brazillian recessions.

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It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

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This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

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Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

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Neste artigo, analisam-se as séries de tempo individuais dos preços do café e do cacau no mercado internacional por meio de apropriados testes de estacionariedade e de raiz unitária. A diferença existente entre as séries temporais econômicas de curto prazo e de longo prazo tem atraído bastante a atenção de economistas nas duas últimas décadas. Os dados de longo prazo são freqüentemente associados às séries temporais não estacionárias conhecidas por tendências, enquanto as flutuações de curto prazo são séries de tempo estacionárias e são chamadas de ciclos. As séries temporais econômicas e financeiras podem ser vistas como combinações desses componentes de ciclos e tendências. Entretanto, a presença ou não de fatores comuns entre duas ou mais séries temporais pode produzir um efeito tal que a combinação das séries temporais não manifeste possuir nenhuma característica individualmente. Poderia haver uma tendência comum partilhada por duas séries temporais. Se não há mais tendências numa série de tempo, então as duas séries de tempo são cointegradas. Esse tipo de análise fator comum pode ser estendido e aplicado aos ciclos comuns.