Short-term Forecasting Methods Based on the LEI Approach: The Case of the Czech Republic

This paper is aimed at developing short-term forecasting methods based on the LEI (leading economic indicators) approach. We use a set of econometric models (PCA, SURE) that provide estimates of GDP growth for the Czech economy for a co-incident quarter and a few quarters ahead. These models exploit monthly or quarterly indicators such as business surveys, financial or labour market indicators, monetary aggregates, interest rates and spreads, etc. that become available before the release of data on GDP growth itself. Our tests show that the LEIs provide relatively accurate forecasts of GDP fluctuations in the short run.Leading indicators, principal component analysis, seemingly unrelated regression estimate.

Forecasting Dynamic Investment Timing under the Cyclic Behavior in Real Estate

This paper applies the Hodrck-Prescott (HP) filter to forecast short-term residential real estate prices under cyclical movements. We separate the trend component from the cyclical component. We show that each regional residential market reacts not only to previous price movements, but also that these regional markets react to previous shocks under Auto Regressive Integrated Moving Average (ARIMA) modeling. Using the S&P Case-Shiller Home Price Index, we compare our forecast to index values from the Chicago Mercantile Exchange (CME) Housing Futures and Options. Our study identifies possible systematic errors from the different price adjustments reflecting current market situations.Real estate investment; Real estate cycle; residential housing futures contract; Real estate risk hedging

Assessing Malaysia’s Business Cycle indicators

An empirical assessment shows that Malaysia’s business cycle indicators can be improved. Turning point detection is not impressive, especially for troughs. Lead times are also variable. However, the relationship between the leading and coincident indicators over the entire cycle shows quite strong correlations from the late 1980s onwards, although lead times have shortened. Empirical evidence is very strong that the leading index Granger-causes the coincident index. Business and consumer confidence surveys also show much promise in improving prediction of the reference cycle. However, implications of the changing economic structure on the performance of the leading index needs to be fully taken into account, especially the emergence of new services sector activities.Business/growth cycle, Malaysian economy, growth cycle chronology, turning point analysis, Granger causality

Modelling Italian potential output and the output gap

The aim of the paper is to estimate a reliable quarterly time-series of potential output for the Italian economy, exploiting four alternative approaches: a Bayesian unobserved component method, a univariate time-varying autoregressive model, a production function approach and a structural VAR. Based on a wide range of evaluation criteria, all methods generate output gaps that accurately describe the Italian business cycle over the past three decades. All output gap measures are subject to non-negligible revisions when new data become available. Nonetheless they still prove to be informative about the current cyclical phase and, unlike the evidence reported in most of the literature, helpful at predicting inflation compared with simple benchmarks. We assess also the performance of output gap estimates obtained by combining the four original indicators, using either equal weights or Bayesian averaging, showing that the resulting measures (i) are less sensitive to revisions; (ii) are at least as good as the originals at tracking business cycle fluctuations; (iii) are more accurate as inflation predictors.potential output, business cycle, Phillips curve, output gap

New Eurocoin: Tracking Economic Growth in Real Time

This paper presents ideas and methods underlying the construction of an indicator that tracks the euro area GDP growth, but, unlike GDP growth, (i) is updated monthly and almost in real time; (ii) is free from hort-run dynamics. Removal of short-run dynamics from a time series, to isolate the mediumlong-run component, can be obtained by a band-pass filter. However, it is well known that band-pass filters, being two-sided, perform very poorly at the end of the sample. New Eurocoin is an estimator of the medium- long-run component of the GDP that only uses contemporaneous values of a large panel of macroeconomic time series, so that no end-of-sample deterioration occurs. Moreover, as our dataset is monthly, New Eurocoin can be updated each month and with a very short delay. Our method is based on generalized principal components that are designed to use leading variables in the dataset as proxies for future values of the GDP growth. As the medium- long-run component of the GDP is observable, although with delay, the performance of New Eurocoin at the end of the sample can be measured.coincident indicator, band-pass filter, large-dataset factor models, generalized principal components

GEFCOM 2014 - Probabilistic Electricity Price Forecasting

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.Comment: 10 pages, 5 figures, KES-IDT 2015 conference. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19857-6_

Nowcasting With Google Trends in an Emerging Market

Most economic variables are released with a lag, making it difficult for policy-makers to make an accurate assessment of current conditions. This paper explores whether observing Internet browsing habits can inform practitioners about real-time aggregate consumer behavior in an emerging market. Using data on Google search queries, we introduce a simple index of interest in automobile purchases in Chile and test whether it improves the fit and efficiency of nowcasting models for automobile sales. We also examine to what extent our index helps us identify turning points in sales data. Despite relatively low rates of Internet usage among the population, we find that models incorporating our Google Trends Automotive Index outperform benchmark specifications in both in-sample and outof- sample nowcasts while providing substantial gains in information delivery times.