nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒11‒01
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Asymptotic theory for cointegration analysis when the cointegration rank is deficient By David Bernstein; Bent Nielsen
  2. Asymptotics for the conditional-sum-of-squares estimator in multivariate fractional time series models By Morten Ørregaard Nielsen
  3. Density forecasts with MIDAS models By Knut Are Aastveit; Claudia Foroni; Francesco Ravazzolo
  4. Evaluating Conditional Forecasts from Vector Autoregressions By Clark, Todd E.; McCracken, Michael W.
  5. Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions By Guillén, Osmani; Hecq, Alain; Issler, João Victor; Saraiva, Diogo

  1. By: David Bernstein (Dept of Economics, University of Miami); Bent Nielsen (Dept of Economics and Nuffield College, Oxford University)
    Abstract: We consider cointegration tests in the situation where the cointegration rank is decient. This situation is of interest in nite sample analysis and in relation to recent work on identication robust cointegration inference. We derive asymptotic theory for tests for cointegration rank and for hypotheses on the cointegrating vectors. The limiting distributions are tabulated. An application to US treasury yields series is given.
    Keywords: Cointegration, rank deciency, weak identication.
    Date: 2014–10–01
  2. By: Morten Ørregaard Nielsen (Queen?s University and CREATES)
    Abstract: This paper proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time series models. The model is parametric and quite general, and, in particular, encompasses the multivariate non-cointegrated fractional ARIMA model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probablity, thus making the proof much more challenging than usual. The neighborhood around the critical point where uniform convergence fails is handled using a truncation argument.
    Keywords: Asymptotic normality, conditional-sum-of-squares estimator, consistency, fractional integration, fractional time series, likelihood inference, long memory, nonstationary, uniform convergence.
    JEL: C22 C32
    Date: 2014–09–10
  3. By: Knut Are Aastveit; Claudia Foroni; Francesco Ravazzolo
    Abstract: In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions with and without an autoregressive component. First, we compare the forecasting performance of the different MIDAS models in Monte Carlo simulation experiments. We find that the results in terms of point and density forecasts are coherent. Moreover, the results do not clearly indicate a superior performance of one of the models under scrutiny when the persistence of the low frequency variable is low. Some differences are instead more evident when the persistence is high, for which the AR- MIDAS and the AR-U-MIDAS produce better forecasts. Second, in an empirical exercise we evaluate density forecasts for quarterly US output growth, exploiting information from typical monthly series. We find that MIDAS models provide accurate and timely density forecasts.
    Keywords: Mixed Data Sampling, Density Forecasts, Nowcasting
    JEL: C10 C53 E37
    Date: 2014–09
  4. By: Clark, Todd E. (Federal Reserve Bank of St. Louis); McCracken, Michael W. (Federal Reserve Bank of St. Louis)
    Abstract: Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical, Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout the analysis, we focus on tests of bias, efficiency, and equal accuracy applied to conditional forecasts from VAR models.
    Keywords: Prediction; forecastingf out-of-sample
    JEL: C12 C32 C52 C53
    Date: 2014–09–01
  5. By: Guillén, Osmani; Hecq, Alain; Issler, João Victor; Saraiva, Diogo
    Abstract: This paper has two original contributions. First, we show that the presentvalue model (PVM hereafter), which has a wide application in macroeconomicsand fi nance, entails common cyclical feature restrictions in the dynamics of thevector error-correction representation (Vahid and Engle, 1993); something thathas been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. Wealso provide the present value reduced rank constraints to be tested within thelog-linear model. Our second contribution relates to forecasting time seriesthat are subject to those long and short-run reduced rank restrictions. Thereason why appropriate common cyclical feature restrictions might improveforecasting is because it finds natural exclusion restrictions preventing theestimation of useless parameters, which would otherwise contribute to theincrease of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to besubject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes thelevels of interest rates with long and short maturities, the second includes thelevel of real price and dividend for the S&P composite index, and the thirdincludes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that betterforecasts can be achieved when restrictions are applied to them. Moreover,imposing short-run restrictions produce forecast winners 70% of the time fortarget variables of PVMs and 63.33% of the time when all variables in thesystem are considered.
    Date: 2014–06–02

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