Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity

Advanced information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, 2003. Empirical research in macroeconomics as well as in financial economics is largely based on time series. Ever since Economics Laureate Trygve Haavelmo's work it has been standard to view economic time series as realizations of stochastic processes. This approach allows the model builder to use statistical inference in constructing and testing equations that characterize relationships between economic variables. This year's Prize rewards two contributions that have deepened our understanding of two central properties of many economic time series - nonstationarity and time-varying volatility - and have led to a large number of applicationstime-series; cointegration

Unit roots and cointegration in panels

This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components. --Panel Unit Roots,Panel Cointegration,Cross Section Dependence,Common Effects

Unit Roots and Cointegration in Panels

This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components

Cointegrated VARMA models and forecasting US interest rates

We bring together some recent advances in the literature on vector autoregressive moving-average models creating a relatively simple specification and estimation strategy for the cointegrated case. We show that in the cointegrated case with fixed initial values there exists a so-called final moving representation which is usually simpler but not as parsimonious than the usual Echelon form. Furthermore, we proof that our specification strategy is consistent also in the case of cointegrated series. In order to show the potential usefulness of the method, we apply it to US interest rates and find that it generates forecasts superior to methods which do not allow for moving-average terms.Cointegration, VARMA models, forecasting

DYNAMICS AND PRICE VOLATILITY IN FARM-RETAIL LIVESTOCK PRICE RELATIONSHIPS

This study uses an error correction model (ECM) to investigate dynamics in farm-retail price relationships. The ECM is a more general method of incorporating dynamics and the long-run, steady-state relationships between farm and retail prices than has been used to data. Monthly data for beef and pork are used to test the time-series properties for the ECM specification. The model is extended to study price volatility through the generalized autoregressive conditional heteroskedasticity (GARCH) process. Accommodation of the GARCH process provides a useful way of analyzing both mean and variance effects of policy or market structure changes.Demand and Price Analysis, Livestock Production/Industries,

Testing the New Keynesian Phillips Curve through Vector Autoregressive models : Results from the Euro area.

In this paper we set out a test of the New Keynesian Phillips Curve (NKPC) based on Vector Autoregressive (VAR) models. The proposed technique does not rely on the Anderson and Moore (1985) method and can be implemented with any existing econometric software. The idea is to use a VAR involving the inflation rate and the forcing variable(s) as the expectation generating system and find the restrictions that nest the NKPC within the VAR. The model can be estimated and tested through maximum likelihood methods. We show that the presence of feedbacks from the inflation rate to the forcing variable(s) can a?ect solution properties of the NKPC; when feedbacks are detected the VAR should be regarded as the final form solution of a more general structural model. Possible non-stationary in the variables can be easily taken into account within our framework. Empirical results point that the standard “hybrid” versions of the NKPC are far from being a good first approximation to the dynamics of inflation in the Euro area.Inflation dynamics, New Keynesian Phillips Curve, Forwardlooking behavior, VEqCM.

Econometrics: A bird's eye view

As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledge and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks and forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of the "real time econometrics". This paper attempts to provide an overview of some of these developments

Measuring intertemporal substitution: The role of durable goods

As pointed out by Hall (1988), intertemporal substitution by consumers is a central element of many modern macroeconomic and international models. For example, many of the policy implications of an endogenous growth model studied by Barro (1990) depends on the assumption that the intertemporal elasticity of substitution is positive. In estimating the intertemporal elasticity of substitution (IES), however, Hall (1988) fmds that when time aggregation is taken into account, his point estimates are small and not significantly different from zero. Hall concludes that ti:e elasticity is unlikely to be much above 0.1 and may well be zero. We argue that Hall's estimator for the IES is downward biased because the intra-temporal substitution between nondurable consumption goods and durable consumption goods is ignored and because the changes in real interest rates affect user costs of durable goods. We use a two-step procedure that combines a cointegration approach to preference parameter estimation with Hansen and Singleton's (1982) Generalized Method of Moments approach in order to take these effects into account. In contrast to Hall's result, our estimates for the IES are positive and significantly different from zero even when time aggregation is taken into account.consumption durable goods real interest rates saving

The causality between energy consumption and economic growth: A multi-sectoral analysis using non-stationary cointegrated panel data

The increasing attention given to global energy issues and the international policies needed to reduce greenhouse gas emissions have given a renewed stimulus to research interest in the linkages between the energy sector and economic performance at country level. In this paper, we analyse the causal relationship between economy and energy by adopting a Vector Error Correction Model for non-stationary and cointegrated panel data with a large sample of developed and developing countries and four distinct energy sectors. The results show that alternative country samples hardly affect the causality relations, particularly in a multivariate multi-sector frameworkEnergy Sector, Panel Unit Roots, Panel Cointegration, Vector Error Correction Models, Granger Causality