Abstract not found

It is obvious that forecasts are of great importance and widely used in economics and finance. Quite simply, good forecasts lead to good decisions. The importance of forecast evaluation and combination techniques follows immediately-- forecast users naturally have a keen interest in monitoring and improving forecast performance. More generally, forecast evaluation figures prominently in many questions in empirical economics and finance, such as: Are expectations rational? (e.g., Keane and Runkle, 1990; Bonham and Cohen, 1995) Are financial markets efficient? (e.g., Fama, 1970, 1991) Do macroeconomic shocks cause agents to revise their forecasts at all horizons, or just at short- and medium-term horizons? (e.g., Campbell and Mankiw, 1987; Cochrane, 1988) Are observed asset returns "too volatile"? (e.g., Shiller, 1979; LeRoy and Porter, 1981) Are asset returns forecastable over long horizons? (e.g., Fama and French, 1988; Mark, 1995)

: This paper links the term structure to perceptions of monetary policy. Long-horizon forecasts of short rates required by no-arbitrage term structure models are heavily influenced by the endpoints, or limiting conditional forecasts, of the short rate process. Common assumptions that the short rate is mean-reverting or contains a unit root are shown to generate unrealistic yield predictions. Failures occur because these assumptions inadequately account for historical shifts in market perceptions of the policy target for inflation. This paper links endpoint shifts to agent learning about shifts in long-term policy goals. Shifting endpoints in short rate processes significantly improve yield predictions. Keywords: Expectations Hypothesis, changepoints, breakpoints, learning JEL classification: E43 a Federal Reserve Bank of Kansas City, 925 Grand Boulevard, Kansas City MO 64198, USA. b Faculty of Economics and Politics, University of Cambridge, Cambridge CB3 9DD, UK. We are grateful ...

Abstract: The paper investigates the common dynamic properties of business cycle fluctuations across countries, regions, and the world. We employ a Bayesian dynamic latent factor model to estimate common components in the main macroeconomic aggregates (output, consumption and investment) in a sixty-country sample covering seven regions of the world. In particular, we simultaneously estimate (i) a dynamic factor common to all aggregates, regions, and countries (the world factor); (ii) a set of 7 regional dynamic factors common across aggregates within a region; (iii) 60 country factors to capture dynamic comovement across aggregates within each country; and (iv) a component for each aggregate that captures idiosyncratic dynamics. We decompose the volatility in each aggregate into the fraction due to the world, region, country, and idiosyncratic components. The results indicate that the world factor is an important source of volatility for aggregates in most countries, providing evidence for a world business cycle. We find that the region-specific factor plays only a minor role in explaining fluctuations in economic activity. While the world and regional factors together account for a larger share of fluctuations in output than in consumption, the country-specific and idiosyncratic components play much larger roles in explaining investment dynamics. We also explore how the three aggregates in each country relate to the world, region and country factors, and document similarities and differences across regions, countries and aggregates. We link the empirical results to the economic structures of the countries in the sample.

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of non-stationarity in the data. We refer to it as PANIC – a ‘Panel Analysis of Non-stationarity in Idiosyncratic and Common components’. PANIC consists of univariate and panel tests with a number of novel features. It can detect whether the nonstationarity is pervasive, or variable-specific, or both. It tests the components of the data instead of the observed series. Inference is therefore more accurate when the components have different orders of integration. PANIC also permits the construction of valid panel tests even when cross-section correlation invalidates pooling of statistics constructed using the observed data. The key to PANIC is consistent estimation of the components even when the regressions are individually spurious. We provide a rigorous theory for estimation and inference. In Monte Carlo simulations, the tests have very good size and power. PANIC is applied to a panel of inflation series.

: This paper explores nonlinear dynamics for the time series of the short term interest rate in the United States. The proposed model is an autoregressive threshold model augmented by conditional heteroskedasticity. The performance of the model is evaluated by considering its implications for the term structure of interest rates. The nonlinear dynamics imply a form of nonlinearity in the levels relation between the long and the short rate. Empirical results indicate that the implied nonlinearity is present in the data. Keywords: Nonlinear Dynamics, Term Structure of Interest Rates, SETAR Models. ? Gerard Pfann gratefully acknowledges the hospitality of the Graduate School of Business of the University of Chicago, where part of this research was done, and the financial support of the William J. Fulbright Commission and of the Royal Netherlands Academy of Arts and Sciences. Rolf Tschernig gratefully acknowledges the hospitality and support of the University of Limburg. Part of his rese...

This paper brings together two important but hitherto largely unrelated areas of the forecasting literature, density forecasting and forecast combination. It proposes a simple data-driven approach to direct combination of density forecasts using optimal weights. These optimal weights are those weights that minimize the ‘distance’, as measured by the Kullback-Leibler information criterion, between the forecasted and true but unknown density. We explain how this minimization both can and should be achieved. Comparisons with the optimal combination of point forecasts are made. An application to simple time-series density forecasts and two widely used published density forecasts for U.K. inflation, namely the Bank of England and NIESR “fan” charts, illustrates that combination can but need not always help.

this paper we focus on the first approach with reference to first order cointegrated systems; we distinguish two different solution both based on the same reduced form model, namely the error correction model. The starting point is a famous result by Stock and Watson (1988), who extended the univariate Beveridge-Nelson decomposition showing that cointegrated systems can be represented in terms of a reduced number of common stochastic trends, whose data generating process is the random walk, plus transitory or stationary components. King et al. (1991) pursued further the issue carrying out an exercise

for helpful suggestions. Iowe special thanks to an anonymous referee who carefully

informs doi 10.1287/mnsc.1060.0520