nep-ecm New Economics Papers
on All new papers
Issue of 2014‒09‒08
seven papers chosen by
Sune Karlsson
Örebro universitet

  1. Testing for Granger causality in large mixed-frequency VARs By Götz T.B.; Hecq A.W.
  2. Is regularization necessary? A Wald-type test under non-regular conditions By Duplinskiy A.
  3. Shrinkage Estimation in the Random Parameters Logit Model By R. Carter Hill; Tong Zeng
  4. Combining distributions of real-time forecasts: An application to U.S. growth By Götz T.B.; Hecq A.W.; Urbain J.R.Y.J.
  5. Nonparametric Estimation of Conditional Expectations for Sustainability Analyses By Alho, Juha
  6. Forecasting the U.S. Real House Price Index By Vasilios Plakandaras; Rangan Gupta; Periklis Gogas; Theophilos Papadimitriou
  7. Multi-jumps By Caporin, Massimiliano; Kolokolov, Aleksey; Renò, Roberto

  1. By: Götz T.B.; Hecq A.W. (GSBE)
    Abstract: In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels 2012, where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a high-frequency one and vice versa. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose two approaches to solve this problem, reduced rank restrictions and a Bayesian mixed-frequency VAR. For the latter, we extend the approach in Banbura et al. 2010 to a mixed-frequency setup, which presents an alternative to classical Bayesian estimation techniques. We compare these methods to a common aggregated low-frequency model as well as to the unrestricted VAR in terms of their Granger non-causality testing behavior using Monte Carlo simulations. The techniques are illustrated in an empirical application involving dailyrealized volatility and monthly business cycle fluctuations.
    Keywords: Hypothesis Testing: General; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models;
    JEL: C12 C32
    Date: 2014
  2. By: Duplinskiy A. (GSBE)
    Abstract: We study hypotheses testing in the presence of a possibly singular covariance matrix. We propose an alternative way to handle possible non-regularity in a covariance matrix of a Wald test, using the identity matrix as the weighting matrix when calculating the quadratic form. The resulting test statistic is not pivotal, but its asymptotic distribution can be approximated using bootstrap methods. In order to prove the validity of the approximations, we show that the square root of a positive semi-definite matrix is a continuously differentiable transformation with respect to the elements of the matrix. This result is important for the continuous mapping theorem to be applicable. We use two types of approximations. The first uses the parametric bootstrap and draws from the asymptotic distribution of the restriction with an estimated covariance matrix. The second applies the residual bootstrap to obtain the distribution of the test and delivers critical values, which control size and show good empirical power even in small samples. In contrast to regularization approaches, the test statistic considered in this paper does not involve arbitrary truncation parameters for which no practical guidelines are available and does not modify the information in the data.
    Keywords: Hypothesis Testing: General; Statistical Simulation Methods: General; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models;
    JEL: C12 C15 C32
    Date: 2014
  3. By: R. Carter Hill; Tong Zeng
    Abstract: We explore the properties of a Stein-like shrinkage estimator that combines the fully correlated and uncorrelated Random Parameters Logit model(RPLM). Monte Carlo experiments show that shrinkage and pretest estimators can improve upon the fully correlated RPLM estimator.
  4. By: Götz T.B.; Hecq A.W.; Urbain J.R.Y.J. (GSBE)
    Abstract: We extend the repeated observations forecasting ROF analysis of Croushore and Stark 2002 to allow for regressors of possibly higher sampling frequencies than the regressand. For the U.S. GNP quarterly growth rate, we compare the forecasting performances of an AR model with several mixed-frequency models among which is the MIDAS approach. Using the additional dimension provided by different vintages we compute several forecasts for a given calendar date and subsequently approximate the corresponding distribution of forecasts by a continuous density. Scoring rules are then employed to construct combinations of them and analyze the composition and evolvement of the implied weights over time. Using this approach, we not only investigate the sensitivity of model selection to the choice of which data release to consider, but also illustrate how to incorporate revision process information into real-time studies. As a consequence of these analyses, weintroduce a new weighting scheme that summarizes information contained in the revision process of the variables under consideration.
    Keywords: Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series; Forecasting and Prediction Methods; Simulation Methods ;
    JEL: C23 C53
    Date: 2014
  5. By: Alho, Juha
    Abstract: Optimal forecasts are, under a squared error loss, conditional expectations of the unknown future values of interest. When stochastic demographic models are used in macroeconomic analyses, it becomes important to be able to handle updated forecasts. That is, when population development turns out to differ from the expected one, the decision makers in the macroeconomic models may change their behavior. To allow for this, numerical methods have been developed that allow us to approximate how future forecasts might look like, for any given observed path. Some technical details of how this can be done in the R environment are given.
    Keywords: demography, forecasting, overlapping generations
    Date: 2014–08–25
  6. By: Vasilios Plakandaras; Rangan Gupta; Periklis Gogas; Theophilos Papadimitriou
    Abstract: The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.
    Keywords: house prices, forecasting, machine learning, Support Vector Regression.
    JEL: C32 C53 R31
    Date: 2014–08–29
  7. By: Caporin, Massimiliano; Kolokolov, Aleksey; Renò, Roberto
    Abstract: We provide clear-cut evidence for economically and statistically significant multivariate jumps (multi-jumps) occurring simultaneously in stock prices by using a novel nonparametric test based on smoothed estimators of integrated variances. Detecting multi-jumps in a panel of liquid stocks is more statistically powerful and economically informative than the detection of univariate jumps in the market index. On the contrary of index jumps, multi-jumps can indeed be associated with sudden and large increases of the variance risk-premium, and possess a statistically significant forecasting power for future volatility and correlations which implies a sizable deterioration in the diversification potential of asset allocation.
    Keywords: multi-jumps, co-jumps, price jumps, multivariate jumps, jumps testing
    JEL: C12 C32 C51 C53 C58 G01 G11 G17
    Date: 2014–08–28

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