George Kapetanios

TESTING FOR COINTEGRATION IN NONLINEAR SMOOTH TRANSITION ERROR CORRECTION MODELS

This paper proposes a new testing procedure to detect the presence of a cointegrating relationship that follows a globally stationary smooth transition process. In the context of nonlinear smooth transition error correction models (ECMs) we provide two simple operational versions of the tests. First, we obtain the associated nonlinear ECM-based tests. Second, we derive the nonlinear analogue of the residual-based test for cointegration in linear models. We derive the asymptotic distributions of the proposed tests. Monte Carlo simulation exercises confirm that our proposed tests have much better power than the linear counterparts against the alternative of a globally stationary nonlinear cointegrating process. In an application to the price-dividend relationship, our test is able to find cointegration, whereas the linear-based tests fail to do so.We are grateful to an associate editor, two anonymous referees, Richard Baillie, In Choi, Atanas Christev, Hira Koul, Richard Harris, Cheng Hsiao, Changjin Kim, Joon Park, Peter Schmidt, Yoonjae Whang, Jeff Wooldridge, and seminar participants at University of Edinburgh, Heriot-Watt University, Korea University, University of Leeds, Michigan State University, University of Newcastle, and Sungkunkwan University for their helpful comments. Partial financial support from the ESRC (grant R000223399) is gratefully acknowledged. The usual disclaimer applies.

Assessing the Economy-Wide Effects of Quantitative Easing

This paper examines the macroeconomic impact of the first round of quantitative easing (QE) by the Bank of England which started in March 2009. Although Bank Rate, the UK policy rate, was reduced to ½%, effectively its lower bound, the Bank’s Monetary Policy Committee felt that additional measures were necessary to meet the inflation target in the medium term. The policy of QE entailed buying private and mainly public assets in large quantities using central bank money, with the aim of injecting money into the economy and boosting nominal spending, in order to help achieve the Bank’s inflation target. Over the period from March 2009 to January 2010, the Bank of England purchased £200 billion of assets, mainly consisting of government securities. We attempt to quantify the effects of these purchases by focusing on the impact of lower long-term interest rates on the wider economy. This is motivated by empirical evidence indicating that QE purchases reduced long-term UK government bond yields by about 100 basis points. Other transmission channels are also possible, but are not considered in this paper. We use three different models to conduct counterfactual simulations to estimate the impact of QE on output and inflation: a large Bayesian VAR; a change-point structural VAR; and a time-varying parameter VAR. Our preferred average estimates from the three models suggest that QE may have had a peak effect on the level of real GDP of around 1½% and a peak effect on annual CPI inflation of about 1¼ percentage points. These estimates are shown to vary considerably across the different model specifications, and with the precise assumptions made to generate the counterfactual simulations, and are therefore subject to considerable uncertainty.

Unit Root Tests in Three-Regime Setar Models

This paper proposes a simple direct testing procedure to distinguish a linear unit root process from a globally stationary three-regime self-exciting threshold autoregressive process. We derive the asymptotic null distribution of the Wald statistic, and show that it does not depend on unknown fixed threshold values. Monte Carlo evidence clearly indicates that the exponential average of the Wald statistic is more powerful than the Dickey-Fuller test that ignores the threshold nature under the alternative.

Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns

This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004), and the other uses principal components following the work of Stock and Watson (2002). The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.

An automatic leading indicator of economic activity: forecasting GDP growth for European countries

In the construction of a leading indicator model of economic activity, economists must select among a pool of variables which lead output growth. Usually the pool of variables is large and a selection of a subset must be carried out. This paper proposes an automatic leading indicator model which, rather than preselection, uses a dynamic factor model to summarize the information content of a pool of variables. Results using quarterly data for France, Germany, Italy and the United Kingdom show that the overall forecasting performance of the automatic leading indicator model appears better than that of more traditional VAR and BVAR models.

Nonlinear mean reversion in real exchange rates

Abstract This paper modifies a unit-root test procedure in the nonlinear STAR framework recently advanced by Kapetanios et al. [Journal of Econometrics (2001) in press]. Using a detrending methodology suggested by Schmidt and Phillips [Oxford Bulletin of Economics and Statistics 54 (1992) 257], we derive an alternative unit-root test and apply it to the bilateral real exchange rates for the G7 countries. We find that the use of our test is able to uncover evidence of nonlinear mean-reversion for most cases whereas the standard Dickey–Fuller test based on the linear model cannot.

A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets

The paradigm of a factor model is very appealing and has been used extensively in economic analyses. Underlying the factor model is the idea that a large number of economic variables can be adequately modeled by a small number of indicator variables. Throughout this extensive research activity on large dimensional factor models a major preoccupation has been the development of tools for determining the number of factors needed for modeling. This article provides an alternative method to information criteria as a tool for estimating the number of factors in large dimensional factor models. The new method is robust to considerable cross-sectional and temporal dependence. The theoretical properties of the method are explored and an extensive Monte Carlo study is undertaken. Results are favorable for the new method and suggest that it is a reasonable alternative to existing methods.

Factor-GMM estimation with large sets of possibly weak instruments

The use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity is analysed. In particular, the focus is on situations where many weak instruments exist and/or the factor structure is weak. Theoretical results, simulation experiments and empirical applications highlight the relevance of Factor-GMM estimation, which is also easily implemented.

A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also develop a consistent information criterion for the determination of the number of factors to be included in the model. Finally, we conduct a set of simulation experiments that show that our approach compares well with existing alternatives.

The Yen Real Exchange Rate May Be Stationary After All: Evidence from Nonlinear Unit-Root Tests

The empirical literature that tests for purchasing power parity (PPP) by focusing on the stationarity of real exchange rates has so far provided, at best, mixed results. The behaviour of the yen real exchange rate has most stubbornly challenged the PPP hypothesis and deepened this puzzle. This paper contributes to this discussion by providing new evidence on the stationarity of bilateral yen real exchange rates. We employ a non-linear version of the Augmented Dickey-Fuller test, based on an exponentially smooth-transition autoregressive model (ESTAR) that enhances the power of the tests against mean-reverting non-linear alternative hypotheses. Our results suggest that the bilateral yen real exchange rates against the other G7 and Asian currencies were mean reverting during the post-Bretton Woods era. Thus, the real yen behaviour may not be so different after all but simply perceived to be so due to the use of a restrictive alternative hypothesis in previous tests.