Testing instantaneous causality in presence of non constant unconditional variance

The problem of testing instantaneous causality between variables with time-varying unconditional variance is investigated. It is shown that the classical tests based on the assumption of stationary processes must be avoided in our non standard framework. More precisely we underline that the standard test does not control the type I errors, while the tests with White (1980) and Heteroscedastic Autocorrelation Consistent (HAC) corrections can suffer from a severe loss of power when the variance is not constant. Consequently a modified test based on a bootstrap procedure is proposed. The relevance of the modified test is underlined through a simulation study. The tests considered in this paper are also compared by investigating the instantaneous causality relations between US macroeconomic variables.Comment: Keywords : VAR model, Unconditionally heteroscedastic errors, instantaneous causalit

Estimating risk premia in money market rates

This paper empirically tests the expectations hypothesis on both daily EONIA swap rates and monthly EURIBOR rates extended backwards with German LIBOR rates. In addition, we quantify the size of the risk premia in the money market at maturities of one, three, six and nine months. Using implied forward and spot rates in a cointegrated VAR model, we find that the data support the expectations hypothesis in the euro area and in Germany prior to 1999. We find that risk premia are relatively limited at the shorter maturities but more significant at maturities of six and nine months. Furthermore, the results on LIBOR/EURIBOR rates tentatively indicate a downward shift in the structure of the risk premia after the introduction of the euro. JEL Classification: E43, C32

Robust Tests for White Noise and Cross-Correlation

Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung-Box tests can be significantly distorted. This paper adapts standard correlogram and portmanteau tests to accommodate hidden dependence and non-stationarities involving heteroskedasticity, thereby uncoupling these tests from limiting assumptions that reduce their applicability in empirical work. To enhance the Ljung-Box test for non-i.i.d. data a new cumulative test is introduced. Asymptotic size of these tests is unaffected by hidden dependence and heteroskedasticity in the series. Related extensions are provided for testing cross-correlation at various lags in bivariate time series. Tests for the i.i.d. property of a time series are also developed. An extensive Monte Carlo study confirms good performance in both size and power for the new tests. Applications to real data reveal that standard tests frequently produce spurious evidence of serial correlation

DYNAMICS AND PRICE VOLATILITY IN FARM-RETAIL LIVESTOCK PRICE RELATIONSHIPS

This study uses an error correction model (ECM) to investigate dynamics in farm-retail price relationships. The ECM is a more general method of incorporating dynamics and the long-run, steady-state relationships between farm and retail prices than has been used to data. Monthly data for beef and pork are used to test the time-series properties for the ECM specification. The model is extended to study price volatility through the generalized autoregressive conditional heteroskedasticity (GARCH) process. Accommodation of the GARCH process provides a useful way of analyzing both mean and variance effects of policy or market structure changes.Demand and Price Analysis, Livestock Production/Industries,

Wealth effects: the French case.

This paper studies the relationship between consumption and wealth based on the concept of cointegration. The analysis focuses on French data over the 1987 - 2006 period. This relationship is expressed in two ways: in terms of Marginal Propensity to Consume out of wealth (MPC) and in terms of Elasticity of consumption to wealth. Three concepts of consumption are investigated: total households consumption expenditure, consumption excluding financial services and consumption excluding durable goods. Different estimators are also considered. Based on the MPC approach, when considered as permanent by households, an increase (decrease) in total wealth of one euro would lead to an increase (decrease) of 1 cent in total consumption. In terms of elasticity, an increase (de- crease) of 10% in wealth would imply also a relatively small impact of 0.8 to 1.1% on consumption depending on the concept of consumption considered. In most cases, the effect of a change in financial wealth is bigger than of a change in housing wealth. The results indicate that the wealth effects are smaller in France than in the UK and US but close to what is observed in Italy. In addition, any deviation of the variables from their common trends is corrected at first by adjustments in disposable income in line with what has been uncovered by studies on Germany and consistent with the "saving for the rainy days" approach of Campbell (1987). But our results contrast with the seminal study of Lettau and Ludvigson (2004) in the US where asset prices make the bulk of the adjustment.consumption, wealth effect, France.

Time Series of Count Data : Modelling and Estimation

This paper compares various models for time series of counts which can account for discreetness, overdispersion and serial correlation. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, we also consider a dynamic ordered probit model as a flexible specification to capture the salient features of time series of counts. For all models, we present appropriate efficient estimation procedures. For parameter-driven specifications this requires Monte Carlo procedures like simulated Maximum likelihood or Markov Chain Monte-Carlo. The methods including corresponding diagnostic tests are illustrated with data on daily admissions for asthma to a single hospital. --Efficient Importance Sampling,GLARMA,Markov Chain Monte-Carlo,Observation-driven model,Parameter-driven model,Ordered Probit

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors

The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). © 2020, The Author(s)