Classification-relevant Importance Measures for the West German Business Cycle

When analyzing business cycle data, one observes that the relevant predictor variables are often highly correlated. This paper presents a method to obtain measures of importance for the classification of data in which such multicollinearity is present. In systems with highly correlated variables it is interesting to know what changes are inflicted when a certain predictor is changed by one unit and all other predictors according to their correlation to the first instead of a ceteris paribus analysis. The approach described in this paper uses directional derivatives to obtain such importance measures. It is shown how the interesting directions can be estimated and different evaluation strategies for characteristics of classification models are presented. The method is then applied to linear discriminant analysis and multinomial logit for the classification of west German business cycle phases. --

What does financial volatility tell us about macroeconomic fluctuations?

This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as composed of a long-run component that is common across all series, and a short-run component. If volatility has components, volatility proxies are characterized by large measurement error, which veils analysis of their fundamental information and relationship with the economy. We find that there are substantial gains from using the long term component of the volatility measures for linearly projecting future economic activity, as well as for forecasting business cycle turning points. When we allow for asymmetry in the long-run volatility component, we find that it provides early signals of upcoming recessions. In a real-time out-of-sample analysis of the last recession, we find that these signals are concomitant with the first signs of distress in the financial markets due to problems in the housing sector around mid-2007 and the implied chronology is consistent with the crisis timeline.Realized Volatility, Business Cycles, Forecasting, Dynamic Factor Models, Markov Switching

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.

Models Of The Intra-Regional Trade Influence On Economic Sustainable Development In Romania

This paper estimates the impact of trade between Romania and EU (export and imports) on sustainable development (defined through the following ratios: GDP growth and employment level) using dynamic forecasting and vector auto-regression (VAR) methods. Logistic regression forecasts the evolution of the GDP based on the evolution of exports and imports in the light of the Lisbon agenda, and the time horizon of 2010. Using data regarding intra-regional trade, the model may predict the GDP evolution, and, consequently, the economic sustainable development in Romania, as a new member of European Union.sustainable development intra-regional trade, economic integration, regression, model

Times-To-Default:Life Cycle, Global and Industry Cycle Impact

This paper studies times-to-default of individual firms across risk classes. Using Standard & Poor’s ratings database we investigate common drivers of default probabilities and address two shortcomings of many papers in the credit literature. First, we identify relevant determinants of default intensities using business cycle and credit market proxies in addition to financial markets indicators, and reveal the time-span of their impacts. We show that misspecifications of financial based factor models are largely corrected by non financial information. Second, we show that past economic conditions are of prime importance in explaining probability changes: current shocks and long term trends jointly determine default probabilities. Finally, we exhibit industry contagion indicators which might be helpful to capture leading and persistency patterns of the default cycle.censored durations; proportional hazard; business cycle; credit cycle; default determinants; default prediction

Economic regimes identification using machine learning technics

43 páginas.Trabajo de Máster en Economía, Finanzas y Computación. Director: Dr. José Manuel Bravo Caro. Economic conditions over long time periods can be distinguished by regimes. Regime identification has been object of numerous investigations in economics and financial modeling for years. Recently, new machine learning technics such as decision trees, support vector machines and neural networks, among others, followed by alternative datasets and cheap computational processing power became available, allowing for alternative ways to model complex economic relationships. In the present work, we develop a supervised machine learning classifier using Random Forest technic to identify economic regimes using the S&P 500 stock market index series.Las condiciones económicas durante largos períodos de tiempo pueden distinguirse por regímenes. La identificación del régimen ha sido objeto de numerosas investigaciones en economía y modelos financieros durante años. Recientemente, se pusieron a disposición nuevas técnicas de aprendizaje automático, como árboles de decisión, máquinas de suporte vectorial y redes neuronales, entre otras, seguidas de conjuntos de datos alternativos y una capacidad de procesamiento computacional barata, que permite formas alternativas de modelar relaciones económicas complejas. En el presente trabajo, desarrollamos un clasificador de aprendizaje automático supervisado utilizando la técnica de Random Forest para identificar regímenes económicos utilizando la serie del índices de mercado S&P 500

Deciding to peg the exchange rate in developing countries: the role of private-sector debt

We argue that a higher share of the private sector in a country's external debt raises the incentive to stabilize the exchange rate. We present a simple model in which exchange rate volatility does not affect agents' welfare if all the debt is incurred by the government. Once we introduce private banks who borrow in foreign currency and lend to domestic firms, the monetary authority has an incentive to dampen the distributional consequences of exchange rate fluctuations. Our empirical results support the hypothesis that not only the level, but also the composition of foreign debt matters for exchange-rate policy. --Exchange rate regimes,foreign debt,monetary policy

Evaluating the German Inventory Cycle – Using Data from the Ifo Business Survey

Inventory fluctuations are an important phenomenon in business cycles. However, the preliminary data on inventory investment as published in the German national accounts are tremendously prone to revision and therefore ill-equipped to diagnose the current stance of the inventory cycle. The Ifo business survey contains information on the assessments of inventory stocks in manufacturing as well as in retail and wholesale trade. Static factor analysis and a method building on canonical correlations are applied to construct a composite index of inventory fluctuations. Based on recursive estimates, the different variants are assessed as regards the stability of the weighting schemes and the ability to forecast the “true” inventory fluctuations better than the preliminary official releases.inventory investment, revisions, composite indices, canonical correlation, factor models, national accounts data, Ifo business survey, Germany

The prediction of the success of first-year MBA candidates: One business school as a case study

AbstractThe focus of this study was to determine the best predictors of academic success of first-year MBA students. Selection criteria and variables are tested for the reliable prediction of successful completion of the first year of an MBA programme (MBAI). A longitudinal quantitative research design is followed using data of students from a South African business school, enrolled between the years 2006 and 2013. The study population consisted of a total of N=777 students enrolled on the MBA programme for this period. Numerical- and verbal cognitive ability assessments gathered as part of enrolment assessment were used and compared to MBAI examination results. Logistic regression analysis was used to determine the significance of different variables to predict MBA first-year success, defined here as the successful completion of all first-year MBA modules within the first academic year. Results indicate that cognitive ability is related to MBA first-year success. The numerical was a better predictor than the verbal cognitive assessment. Type of undergraduate education was found to play a role in MBA first-year success. Language of delivery proved to have an influence on academic performance and Younger students performed better than their older counterparts did. Determining the best predictors MBA first-year success has practical implications on selection processes and throughput