The Importance Of Common Cyclical Features in VAR Analysis: A Monte-Carlo Study.

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models.Reduced rank models; model selection criteria; forecasting; variance decomposition

The Missing Link: Using the NBER Recession Indicator to Construct Coincident and Leading Indices of Economic Activity.

We use the information content in the decisions of the NBER Business cycle Dating Committee to construct coincident and leading indices of economic activity for United States. Specifically, we use canonical correlation analysis to filter out the noisy information contained in the coincident series. Finally, to construct our preferred coincident index of the U.S. business cycle, we take account of measurement error in the commonly used coincident series by using instrumental-variable methods. The resulting index is a simple linear combination of four coincident series that encompassed currently popular coincident indices.Coincident and Leading Indicators; Business Cycle; Canonical Correlation; Instrumental Variable Probit; Encompassing

A common-feature approach for testing present-value restrictions with financial data

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

Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors

The mixed autoregressive causal-noncausal model (MAR) has been proposed to estimate economic relationships involving explosive roots in their autoregressive part, as they have stationary forward solutions. In previous work, possible exogenous variables in economic relationships are substituted into the error term to ensure the univariate MAR structure of the variable of interest. To allow for the impact of exogenous fundamental variables directly, we instead consider a MARX representation which allows for the inclusion of strictly exogenous regressors. We develop the asymptotic distribution of the MARX parameters. We assume a Student's t-likelihood to derive closed form solutions of the corresponding standard errors. By means of Monte Carlo simulations, we evaluate the accuracy of MARX model selection based on information criteria. We investigate the influence of the U.S. exchange rate and the U.S. industrial production index on several commodity prices

Forecasting multivariate time series under present-value model short- and long-run co-movement restrictions

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

Uma análise empírica da volatilidade do retorno de commodities agrícolas utilizando modelos ARCH: os casos do café e da soja

Examinou-se o processo da volatilidade dos retornos de duas importantes commodities agrícolas brasileiras, o café e a soja, por meio de modelos da classe ARCH. Os resultados empíricos sugerem fortes sinais de persistência e assimetria na volatilidade de ambas as séries. Além disso, os resultados sugerem que a implementação de políticas que criem, facilitem o acesso e estimulem a utilização de instrumentos de hedging baseados no mercado podem ser estratégias adequadas para tais setores diante da persistência de choques e volatilidade pronunciadas constatadas para os retornos destas commodities<br>We examined the volatility process of the returns of two important Brazilian agricultural commodities, coffee and soy, using ARCH class models. Empirical results suggest strong signs of persistence and asymmetry in the volatility of both series. Furthermore, the results suggest that the design of policies that create, facilitate the access and stimulate the use of market-based hedging devices can be proper strategies for such sectors in view of the persistence of shocks and the pronounced volatility found for the returns of these commodities

Um novo índice coincidente para a atividade industrial do Estado do Rio Grande do Sul

Este artigo utiliza o modelo de fator dinâmico de Stock e Watson para construir um índice coincidente que tenha um fundamento estatístico claro e que possa ser representativo do nível de atividade da indústria de transformação do Rio Grande do Sul. Além deste modelo linear, também é aplicada a metodologia de mudança de regime para caracterizar a assimetria no ciclo dos negócios na indústria do Estado, indicando os momentos de crescimento e queda na atividade econômica do setor com características diferenciadas. Este novo indicador é comparado com o índice de desempenho industrial (IDI) elaborado pela Federação das Indústrias do Estado do Rio Grande do Sul. Os resultados mostram que tanto o modelo linear quanto o não-linear estimam componentes que são altamente correlacionados como o índice de médias ponderadas atualmente calculado pela FIERGS.<br>The present article uses the dynamic factor model of Stock and Watson to construct a coincident index with a clear statistical foundation able to represent the level of activity of the processing industry of the state of Rio Grande do Sul. In addition to this linear model, we also employ a regime switching methodology in order to determine the asymmetry of the business cycle in the industry on a statewide basis, pointing out periods of economic growth and stagnation in this sector. This new indicator is compared with the industrial performance index developed by the Federation of the Industries of the State of Rio Grande do Sul (FIERGS). The results show that both linear and nonlinear models estimate components that are highly correlated, such as the weighted average index currently calculated by FIERGS