nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒07‒28
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Testing Dependence Among Serially Correlated Multi-category Variables By M. Hashem Pesaran; Allan Timmermann
  2. Time-Varying Quantiles By Giuliano De Rossi; Andrew Harvey
  4. Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases. By David H. Small; Domenico Giannone; Lucrezia Reichlin
  5. Detecting and predicting forecast breakdowns. By Raffaella Giacomini; Barbara Rossi
  6. A structural break in the effects of Japanese foreign exchange intervention on yen/dollar exchange rate volatility. By Eric Hillebrand; Gunther Schnabl
  7. On Infinite Horizon Optimal Stopping of General Random Walk By Jukka Lempa
  8. Inflation Expectations and Regime Shifts By Matti Viren

  1. By: M. Hashem Pesaran; Allan Timmermann
    Abstract: The contingency table literature on tests for dependence among discrete multi-category variables assume that draws are independent, and there are no tests that account for serial dependencies ? a problem that is particularly important in economics and finance. This paper proposes a new test of independence based on the maximum canonical correlation between pairs of discrete variables. We also propose a trace canonical correlation test using dynamically augmented reduced rank regressions or an iterated weighting method in order to account for serial dependence. Such tests are useful, for example, when testing for predictability of one sequence of discrete random variables by means of another sequence of discrete random variables as in tests of market timing skills or business cycle analysis. The proposed tests allow for an arbitrary number of categories, are robust in the presence of serial dependencies and are simple to implement using multivariate regression methods.
    Keywords: Contingency Tables, Canonical Correlations, Serial Dependence, Tests of Predictability
    JEL: C12 C22 C42 C52
    Date: 2006–07
  2. By: Giuliano De Rossi; Andrew Harvey
    Abstract: A time-varying quantile can be fitted to a sequence of observations by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm. Quantiles estimated in this way provide information on various aspects of a time series, including dispersion, asymmetry and, for financial applications, value at risk. Tests for the constancy of quantiles, and associated contrasts, are constructed using indicator variables; these tests have a similar form to stationarity tests and, under the null hypothesis, their asymptotic distributions belong to the Cramér von Mises family. Estimates of the quantiles at the end of the series provide the basis for forecasting. As such they offer an alternative to conditional quantile autoregressions and, at the same time, give some insight into their structure and potential drawbacks.
    Keywords: Dispersion; quantile regression; signal extraction; state space smoother; stationarity tests; value at risk.
    JEL: C14 C22
    Date: 2006–07
  3. By: Kazuhiko Nishina (Graduate School of Economics, Osaka University, Japan); Nabil Maghrebi (Graduate School of Economics, Wakayama, Japan University)
    Abstract: This paper examines nonlinearities in the dynamics of volatility expectations using benchmarks of implied volatility for the US and Japanese markets. The evidence from Markov regime-switching models suggests that volatility expectations are likely to be governed by regimes featuring a long memory process and significant leverage effects. Market volatility is expected to increase in bear periods and decrease in bull periods. Leverage effects constitute thus an important source of nonlinearities in volatility expectations. There is no evidence of long swings associated with financial crises, which do not have the potential of shifting volatility expectations from one regime to another for long protracted periods.
    Keywords: Markov Regime Switching, Implied Volatility Index, Nonlinear Modelling.
    JEL: C32 C51 G13 G15
    Date: 2006–07
  4. By: David H. Small (Federal Reserve Board - Monetary Studies Section, 20th and C Streets, NW, Washington , DC 20551, United States.); Domenico Giannone (Free University of Brussels (VUB/ULB), European Center for Advanced Research in Economics and Statistics (ECARES), Ave. Franklin D Roosevelt, 50 - C.P. 114, B-1050 Brussels, Belgium.); Lucrezia Reichlin (Free University of Brussels (VUB/ULB), European Center for Advanced Research in Economics and Statistics (ECARES), Ave. Franklin D Roosevelt, 50 - C.P. 114, B-1050 Brussels, Belgium.)
    Abstract: This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing news on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real variables and this effect is larger than that of the Employment Report. When we control for timeliness of the releases, the effect of hard data becomes sizeable. Prices and quantities affect the precision of the estimates of inflation while GDP is only affected by real variables and interest rates. JEL Classification: E52; C33; C53.
    Keywords: Forecasting; monetary policy; factor model; real time data; large data sets; news.
    Date: 2006–05
  5. By: Raffaella Giacomini (Department of Economics, UCLA, Box 951477, Los Angeles, CA 90095-1477, USA.); Barbara Rossi (Department of Economics, Duke University, Durham NC27708, USA.)
    Abstract: We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss function, is significantly worse than its in-sample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data generating process and relate the properties of our forecast breakdown test to those of existing structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips’ curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed. JEL Classification: C22; C52; C53.
    Keywords: Structural change; forecast evaluation; forecast rationality testing; in-sample evaluation; out-of-sample evaluation.
    Date: 2006–06
  6. By: Eric Hillebrand (Department of Economics, Louisiana State University, Baton Rouge, LA 70803, USA.); Gunther Schnabl (Department of Economics and Business Administration, Leipzig University, Marschenerstr. 31, 04109 Leipzig, Germany.)
    Abstract: While up to the late 1990s Japanese foreign exchange intervention was fully sterilized, Japanese monetary authorities left foreign exchange intervention unsterilized when Japan entered the liquidity trap in 1999. According to previous research on foreign exchange intervention, unsterilized intervention has a higher probability of success than sterilized intervention. Based on a GARCH framework and change point detection, we test for a structural break in the effectiveness of Japanese foreign exchange intervention. We find a changing impact of Japanese foreign exchange intervention on exchange rate volatility at the turn of the millennium when Japanese foreign exchange intervention started to remain unsterilized. JEL Classification: E58; F31; F33; G15.
    Keywords: Japan; foreign exchange intervention; exchange rate volatility; GARCH; change point detection; structural breaks.
    Date: 2006–06
  7. By: Jukka Lempa (Department of Economics, Quantitative Methods in Management, Turku School of Economics)
    Abstract: The objective of this study is to provide an alternative characterization of the optimal value function of a certain Black- Scholes-type optimal stopping problem where the underlying stochastic process is a general random walk, i.e. the process constituted by partial sums of an IID sequence of random variables. Furthermore, the pasting principle of this optimal stopping problem is studied.
    Keywords: General random walk, optimal stopping, minimal functions, continuous pasting
    JEL: G35 G31 C44 Q23
    Date: 2006–04
  8. By: Matti Viren (Department of Economics, University of Turku)
    Abstract: This paper focuses on the determination of inflation expectations. The following two questions are examined: How much do inflation expectations reflect different economic and institutional regime shifts and in which way do inflation expectations adjust to past inflation? The basic idea in the analysis is an assumption that inflation expectations do not mechanically reflect past inflation as may econometric specification de facto assume but rather they depend on the relevant economic regime. Also the adjustment of expectations to past inflation is different in different inflation regimes. The regime analysis is based on panel data from EMU/EU countries for the period 1973–2004, while the inflation adjustment analysis mainly uses the Kalman filter technique for individual countries for the same period. Expectations (forecasts) are derived from OECD data. Empirical results strongly favour the regime-sensitivity hypothesis and provide an explanation for the poor performance of conventional estimation procedures in the context of Phillips curves
    Keywords: inflation expectations, Kalman filter, stability
    JEL: E32 E37
    Date: 2006–04

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