Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore

We apply multivariate statistical methods to a large dataset of Singapore’s macroeconomic variables and global economic indicators with the objective of forecasting business cycles in a small open economy. The empirical results suggest that three common factors are present in the time series at the quarterly frequency, which can be interpreted as world, regional and domestic economic cycles. This leads us to estimate a factor-augmented vector autoregressive (FAVAR) model for the purpose of optimally forecasting real economic activity in Singapore. By taking explicit account of the common factor dynamics, we find that iterative forecasts generated by this model are significantly more accurate than direct multi-step predictions based on the identified factors as well as forecasts from univariate and vector autoregressions.business cycles; principal components; dynamic factor model; factor-augmented VAR; forecasting; Singapore

MULTIVARIATE AUTOREGRESSIVE MODEL FOR FORECASTING THE DEMAND OF CONTAINER THROUGHPUT IN INDONESIA

Abstract This research was proposed to forecast the demand of container throughput in Indonesia. The analysis was carried out in multivariate autoregressive model. Augmented Dickey-Fuller (ADF) test was used to check the stationarity of data and order of integration. Johansen approach was used to find the existence and the number of cointegration relationship. The number of cointegration relations was established by a sequential likelihood ratio test on the rank of an estimated parameter matrix from vector error correction model (VECM), impulse response function (IRF) was performed to know response to a shock of a variable of other variables. The empirical analysis demonstrated that the estimation model provides indication of goodness-of-fit and the forecasting potential of the model. Most of the model estimation results follow the long-term development of the actual data series closely. The impulse response of a shock of a variable to itself and other variables die out after certain period. These results verified the stability of all the estimated models. The forecast of container throughput in Indonesia generated by VECM indicated the reasonable result. Keywords: container throughput, forecasting, multivariate autoregressive

The predictive content of the real interest rate gap for macroeconomic variables in the euro area

The real interest rate gap -IRG-, i.e. the gap between the short term real interest rate and its “natural†level, is a theoretical concept of potential policy relevance for central banks, at least to evaluate the monetary policy stance, at best as a guideline for policy moves. This paper aims at clarifying the practical relevance of IRG indicators for monetary policy. To this end, it provides an empirical assessment of the usefulness of various univariate and multivariate estimates of the real IRG for predicting inflation, real activity and real credit growth in the euro area. On the basis of out-of-sample evidence using real-time data, I find that IRG measures are globally of little help to improve our knowledge of future inflation in the euro area. By contrast, some of the estimated IRG measures exhibit a significant predictive power for future real activity, in line with the intuition from a traditional IS curve, as well as for credit growth. Nevertheless, in most cases, the forecasting models that include estimated IRG do not outperform a simpler AR model augmented with the first difference of the nominal interest ratenatural rate of interest, monetary policy, forecasting

Regional employment forecasts with spatial interdependencies

"The labour-market policy-mix in Germany is increasingly being decided on a regional level. This requires additional knowledge about the regional development which (disaggregated) national forecasts cannot provide. Therefore, we separately forecast employment for the 176 German labour- market districts on a monthly basis. We first compare the prediction accuracy of standard time-series methods: autoregressive integrated moving averages (ARIMA), exponentially weighted moving averages (EWMA) and the structural-components approach (SC) in these small spatial units. Second, we augment the SC model by including autoregressive elements (SCAR) in order to incorporate the influence of former periods of the dependent variable on its current value. Due to the importance of spatial interdependencies in small labour-market units, we further augment the basic SC model by lagged values of neighbouring districts in a spatial dynamic panel (SCSAR). The prediction accuracies of the models are compared using the mean absolute percentage forecast error (MAPFE) for the simulated out-of-sample forecast for 2005. Our results show that the SCSAR is superior to the SCAR and basic SC model. ARIMA and EWMA models perform slightly better than SCSAR in many of the German labour-market districts. This reflects that these two moving-average models can better capture the trend reversal beginning in some regions at the end of 2004. All our models have a high forecast quality with an average MAPFE lower than 2.2 percent." (Author's abstract, IAB-Doku) ((en))regionaler Arbeitsmarkt, Beschäftigungsentwicklung, Prognoseverfahren, Arbeitsmarktprognose - Methode

Real-time representations of the output gap

Methods are described for the appropriate use of data obtained and analysed in real time to represent the output gap. The methods employ cointegrating VAR techniques to model real-time measures and realizations of output series jointly. The model is used to mitigate the impact of data revisions; to generate appropriate forecasts that can deliver economically meaningful output trends and that can take into account the end-of-sample problems encountered in measuring these trends; and to calculate probability forecasts that convey in a clear way the uncertainties associated with the gap measures. The methods are applied to data for the United States 1965q4–2004q4, and the improvements over standard methods are illustrated

Bayesian VAR Models for Forecasting Irish Inflation

In this paper we focus on the development of multiple time series models for forecasting Irish Inflation. The Bayesian approach to the estimation of vector autoregressive (VAR) models is employed. This allows the estimated models combine the evidence in the data with any prior information which may also be available. A large selection of inflation indicators are assessed as potential candidates for inclusion in a VAR. The results confirm the significant improvement in forecasting performance which can be obtained by the use of Bayesian techniques. In general, however, forecasts of inflation contain a high degree of uncertainty. The results are also consistent with previous research in the Central Bank of Ireland which stresses a strong role for the exchange rate and foreign prices as a determinant of Irish prices.Bayesian; BVAR; inflation forecasts; Ireland

Are realized volatility models good candidates for alternative Value at Risk prediction strategies?

In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation’s distribution is estimated with the fully parametric method using either the normal or the skewed student distributions and also with the Filtered Historical Simulation (FHS), or the Extreme Value Theory (EVT) methods. Our analysis is based on two S&P 500 cash index out-of-sample forecasting periods, one of which covers exclusively the recent 2007-2009 financial crisis. Using an extensive array of statistical and regulatory risk management loss functions, we find that the realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR forecasts and allow for more efficient regulatory capital allocations. The skewed student distribution is also an attractive alternative, especially during periods of high market volatility.High frequency intraday data; Filtered Historical Simulation; Extreme Value Theory; Value-at-Risk forecasting; Financial crisis.

Forecasting Irish inflation using ARIMA models

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. The emphasis is on forecast performance which suggests more focus on minimising out-of-sample forecast errors than on maximising in-sample 'goodness of fit'. Thus, the approach followed is unashamedly one of 'model mining' with the aim of optimising forecast performance. Practical issues in ARIMA time series forecasting are illustrated with reference to the harmonised index of consumer prices (HICP) and some of its major sub-components.