Martyna Marczak

Outlier Detection in Structural Time Series Models: The Indicator Saturation Approach

Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general to specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit-root autoregressions. By focusing on impulse- and step-indicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries.

EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area

EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged–edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules.

Bidirectional relationship between investor sentiment and excess returns: new evidence from the wavelet perspective

ABSTRACT We propose to use the wavelet concept of the phase angle to determine the lead–lag relationship between investor sentiment and excess returns that are related to the bubble component of stock prices. The wavelet phase angle allows for decoupling short- and long-run relations and is additionally capable of identifying time-varying comovement patterns. Based on the monthly S&P500 index and two alternative monthly US sentiment indicators, we find that in the short run (until 3 months), sentiment is leading returns whereas for periods above 3 months the opposite can be observed. Moreover, the initially strong positive relationship becomes less pronounced with increasing time horizon, thereby indicating that the over- or undervaluation in the short run is gradually corrected in the long run.

Illuminating the world cup effect: Night lights evidence from South Africa

This paper evaluates the economic impact of the

A Data-Cleaning Augmented Kalman Filter for Robust Estimation of State Space Models

14 billion preparatory investments for the 2010 FIFA World Cup in South Africa. We use satellite data on night light luminosity at municipality and electoral district level as a proxy for economic development, applying synthetic control methods for estimation. For the average World Cup municipality, we find significantly positive, short-run effects before the tournament, corresponding to a reduction of unemployment by 1.3 percentage points. At the electoral district level, we reveal distinct effect heterogeneity, where especially investments in transport infrastructure are shown to have long-lasting, positive effects, particularly in more rural areas.

Cyclicality of real wages in the USA and Germany: New insights from wavelet analysis

This article presents a robust augmented Kalman filter that extends the data-cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M-type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series.