Nonlinear Autoregresssive Leading Indicator Models of Output in G-7 Countries

This paper studies linear and nonlinear autoregressive leading indicator models of business cycles in G7 countries. The models use the spread between short-term and long-term interest rates as leading indicators for GDP, and their success in capturing business cycles is gauged by non-parametric shape tests, and their ability to predict the probability of recession. We find that bivariate nonlinear models of output and the interest rate spread can successfully capture the shape of the business cycle in cases where linear models fail. Also, our nonlinear leading indicator models for USA, Canada and the UK outperform other models of GDP with respect to predicting the probability of recession.Business Cycles, Leading Indicators, Model Evaluation, Nonlinear Models, Yield Spreads.

Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI) is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX) in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics

The Multistep Beveridge-Nelson Decomposition

The Beveridge-Nelson decomposition defines the trend component in terms of the eventual forecast function, as the value the series would take if it were on its long-run path. The paper introduces the multistep Beveridge-Nelson decomposition, which arises when the forecast function is obtained by the direct autoregressive approach, which optimizes the predictive ability of the AR model at forecast horizons greater than one. We compare our proposal with the standard Beveridge-Nelson decomposition, for which the forecast function is obtained by iterating the one-step-ahead predictions via the chain rule. We illustrate that the multistep Beveridge-Nelson trend is more efficient than the standard one in the presence of model misspecification and we subsequently assess the predictive validity of the extracted transitory component with respect to future growth.Trend and Cycle; Forecasting; Filtering.

An Empirical Analysis of Real Activity and Stock Returns in an Emerging Market

The present paper analyzes the role of stock market returns as a predictor of real output for a fast-growing emerging market, Malaysia. In the analysis, forecasting equations for 1-, 2-, 4-, and 8-quarter forecasting horizons based on autoregressive distributed lags framework are adopted. From the estimation, we find evidence that stock market returns do contain predictive ability at short-forecasting horizons, especially at less than 4-quarter horizons. Estimating the forecasting models recursively, we note reduction of out-of-sample forecasting evaluation statistics, namely the mean absolute errors (MAE) and the mean squared forecast errors (MSFE), from those obtained from the simple autoregressive (AR) model. More importantly, the null hypothesis of equal predictive accuracy between the model with stock returns as a predictor and the AR model is rejected for the 1-quarter and 2-quarter forecasting horizons by the McCraken’s (2007) out-of-sample-F statistics.Stock Return, Real GDP Growth, Out-of-Sample Forecasts, Malaysia

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Forecasting GDP at the regional level with many predictors

In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a ’data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single– indicator, multi–indicator, pooled and factor forecasts in a pseudo real–time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP

Forecasts with single-equation Markov-switching model: an application to the gross domestic product of Latvia

The paper compares one-period ahead forecasting performance of linear vector-autoregressive (VAR) models and single-equation Markov-switching (MS) models for two cases: when leading information is available and when it is not. The results show that single-equation MS models tend to perform slightly better than linear VAR models when no leading information is available. However, if reliable leading information is available, single-equation MS models tend to give somewhat less precise forecasts than linear VAR models.Markov-switching, VAR, forecasting, leading information

The Record and Improvability of Economic Forecasting

Have macroeconomic forecasts grown more or less accurate over time? This paper assembles, examines, and interprets evidence bearing on this question. Contrary to some critics, there are no indications that U.S. forecasts have grown systematically worse, that is, less accurate, more biased, or both. Neither do any definite trends in a positive direction emerge from comparisons of annual and quarterly multiperiod forecasts and time-series projections for the principal aggregative variables. The argument is developed and to some extent documented that major failures of forecasting are related to the incidence of slowdowns and contractions in general economic activity. Not only the forecasts of real GNP growth and unemployment but also those of nominal GNP growth and inflation often go seriously wrong when such setbacks occur. Forecasters tend to rely heavily on the persistence of trends in spending, output, and the price level. More attention to data and techniques that are sensitive to business cycle movements and turning points could help improve their record.

FOREIGN CAPITAL AND AFRICA’S ECONOMIC PROGRESS: FACTS FROM NIGERIA AND SOUTH AFRICA

Foreign capital inflow is usually believed as a means of supplementing domestic capital. The paper examined the influence of foreign capital on Africa’s economic progress focusing on Nigeria and South Africa (1970-2004). Data sourced from IFS, CBN and others were analyzed with econometric techniques. Empirical facts from cointegration and Granger casualty tests are as follows: There is a long-run relationship between foreign capital and economic progress in South Africa but in Nigeria it is short-run oriented; Foreign capital Granger-causes economic progress in South Africa, while in Nigeria casualty runs on the reverse; a bi-directional causality exists between economic progress and domestic capital in South Africa, for Nigeria it is uni-directional running from domestic capital to economic progress; Labour force in both countries Granger-causes their economic progress. In the light of the above, foreign capital should be promoted in South Africa to enhance her economic progress while in Nigeria polices that can reduce the level of capital flight (e.g. dependable institutional framework etc) are essential for foreign capital to have long-run influence on her economic progress. The need for the countries to rely more on domestic capital is equally suggested as viable factors for their economic progress