Trade openness and economic growth: Bayesian model averaging estimate of cross-country growth regressions

In this paper, we investigate the robustness of the relationship between trade openness and long-run economic growth over the sample period 1960–2000, utilising Bayesian model averaging techniques to account for model uncertainty issues in a systematic manner. We find no evidence that trade openness is directly and robustly correlated with economic growth in the long run. We further check the robustness of this finding by employing a battery of proxies for trade openness, namely, current openness, real openness, the fraction of open years based on the Sachs and Warner (1995) criteria and the weighted averages of tariff rates, non-tariff barriers and the black market premium. The main result is robust to the inclusion of different trade openness proxies and none of the proxies is robustly associated with economic growth. The data evidence also indicates that economic institutions and macroeconomic uncertainties such as those induced by high inflation and excess government consumption are key factors in explaining economic growth.     

Forecasting exchange rates using panel model and model averaging

We propose to produce accurate point and interval forecasts of exchange rates by combining a number of well known fundamental based panel models. Combination of each model utilizes a set of weights computed using a linear mixture of experts's framework, where weights are determined by log scores assigned to each model's predictive performance. As well as model uncertainty, we take potential structural break in the parameters of the models into consideration. In our application, to quarterly data for ten currencies (including the Euro) for the period 1990q1–2008q4, we show that the forecasts from ensemble models produce mean and interval forecasts that outperform equal weight, and to a lesser extent random walk benchmark models. The gain from combining forecasts is particularly pronounced for longer-horizon forecasts for central forecasts, but much less so for interval forecasts. Calculations of the probability of the exchange rate rising or falling using the combined or ensemble model show a good correspondence with known events and potentially provide a useful measure for uncertainty of whether the exchange rate is likely to rise or fall.     

Bayesian model averaging and identification of structural breaks in time series

Bayesian Model Averaging (BMA) is used for testing for multiple break points in univariate series using conjugate normal-gamma priors. This approach can test for the number of structural breaks and produce posterior probabilities for a break at each point in time. Results are averaged over specifications including: stationary; stationary around trend and unit root models, each containing different types and number of breaks and different lag lengths. The procedures are used to test for structural breaks on 14 annual macroeconomic series and 11 natural resource price series. The results indicate that there are structural breaks in all of the natural resource series and most of the macroeconomic series. Many of the series had multiple breaks. Our findings regarding the existence of unit roots, having allowed for structural breaks in the data, are largely consistent with previous work.     

Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

On predicting the semiconductor industry cycle: a Bayesian model averaging approach

This study considers the model uncertainty and utilizes the Bayesian model averaging (BMA) approach to identify useful predictors of the semiconductor industry cycle from a list of 70 potential predictors. The posterior inclusion probabilities, posterior means, and posterior standard deviations over the period of 1995:05–2012:10 are estimated and consequently used to identify the main determinants of the industry cycle. It is found that the Philadelphia Semiconductor Index and total inventories in various downstream industries have important roles in signaling the industry growth. The results from an out-of-sample forecasting exercise also reveal the predictive potential and usefulness of BMA for the long-term prediction.     

Growth,convergence and public investment. A Bayesian model averaging approach

The aim of this study is twofold. First, the determinants of economic growth are studied among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, various types of private, public and human capital in the group of growth factors are included. Also, it is analysed whether Spanish provinces have converged in economic terms in recent decades. The second objective is to obtain cross-section and panel data parameter estimates that are robust to model specification. For this purpose, a Bayesian Model Averaging (BMA) approach is used. Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.     

Research & development and growth: A Bayesian model averaging analysis

We examine the effect of research and development (R&D) on long-term economic growth using the Bayesian model averaging (BMA) to deal rigorously with model uncertainty. Previous empirical studies, which applied BMA, investigated the effect of dozens of regressors on long-term growth, but they did not examine the effect of R&D due to data unavailability. We extend these studies by proposing to capture the investment in R&D by the number of Nobel prizes in science. Using our indicator, the estimates show that R&D exerts a positive effect on long-term growth. This result is robust to many different parameter and model prior structures as well as to alternative definitions of R&D indicator.     

Endogeneity and panel data in growth regressions: A Bayesian model averaging approach

Bayesian model averaging (BMA) has been successfully applied in the empirical growth literature as a way to overcome the sensitivity of results to different model specifications. In this paper, we develop a BMA technique to analyze panel data models with fixed effects that differ in the set of instruments, exogeneity restrictions, or the set of explanatory variables in the regression. The large model space that typically arises can be effectively analyzed using a Markov Chain Monte Carlo algorithm. We apply our technique to investigate the effect of foreign aid on per capita GDP growth. We show that BMA is an effective tool for the analysis of panel data growth regressions in cases where the number of models is large and results are sensitive to model assumptions.     

Model uncertainty and asset return predictability: an application of Bayesian model averaging

We investigate model uncertainty associated with predictive regressions employed in asset return forecasting research. We use simple combination and Bayesian model averaging (BMA) techniques to compare the performance of these forecasting approaches in short-vs. long-run horizons of S&P500 monthly excess returns. Simple averaging involves an equally-weighted averaging of the forecasts from alternative combinations of factors used in the predictive regressions, whereas BMA involves computing the predictive probability that each model is the true model and uses these predictive probabilities as weights in combing the forecasts from different models. From a given set of multiple factors, we evaluate all possible pricing models to the extent, which they describe the data as dictated by the posterior model probabilities. We find that, while simple averaging compares quite favorably to forecasts derived from a random walk model with drift (using a 10-year out-of-sample iterative period), BMA outperforms simple averaging in longer compared to shorter forecast horizons. Moreover, we find further evidence of the latter when the predictive Bayesian model includes shorter, rather than longer lags of the predictive factors. An interesting outcome of this study tends to illustrate the power of BMA in suppressing model uncertainty through model as well as parameter shrinkage, especially when applied to longer predictive horizons.     

Time stability of the beta convergence among EU countries: Bayesian model averaging perspective

This paper analyzes the time stability of the GDP beta convergence in two subsamples: EU27 countries during 1993–2010 and EU15 during 1972–2010. Additionally, the article checks for the strength and stability of influence of particular economic growth factors. In order to address the problem of variables' selection, Bayesian model averaging (BMA) is used while choosing the appropriate variables for the regression. In order to loose the assumption of stability overtime in the regression, interaction terms of particular regressors are introduced with time dummies (the whole sample is divided into time intervals and is allowed for structural breaks). Finally, in order to address the problem of potential inconsistency of “typically used” estimators, the study employs Blundell and Bond's GMM system estimator. The main findings are the following. (1) The EU27 countries converged at the rate of about 5% per annum while the EU15 countries—at 3% p.a., which is an enormous difference as compared with the widely cited 2% rate of convergence. (2) The pure mechanism of conditional convergence of the countries under study was rather constant over time: there were periods of more rapid or slower convergence but the differences were not as huge as one could expect. (3) The considered economic growth determinants exhibited very mixed and differentiated impact on economic growth in various subperiods.     

Forecasting the output of integrated circuit industry using a grey model improved by the Bayesian analysis

The production values of the integrated circuit industry has the following attributes, short product life cycle, numerous influencing factors on the market, and rapid changing of technology. These features obstruct the precision of forecasting the outputs of integrated circuit industry using the traditional statistical methods. The grey forecast model can obviously conquer these difficulties with a small sample set and ambiguity of available information. This study evaluates original and Bayesian grey forecast models for the integrated circuit industry. Bayesian method uses the technique of Markov Chain Monte Carlo to estimate the parameters for grey differential function. The predictive value of integrated circuit in Taiwan was evaluated along with mean absolute percentage error. Various parameters and efficiency of three forecast models were compared and summary outcomes were reported. Meanwhile, the Bayesian grey model was the most accurate one among these models.     

Forecasting European economic policy uncertainty

Forecasting the economic policy uncertainty in Europe is of paramount importance given the ongoing sovereign debt crisis. This paper evaluates monthly economic policy uncertainty index forecasts and examines whether ultra‐high frequency information from asset market volatilities and global economic uncertainty can improve the forecasts relatively to the no‐change forecast. The results show that the global economic policy uncertainty provides the highest predictive gains, followed by the European and US stock market realized volatilities. In addition, the European stock market implied volatility index is shown to be an important predictor of the economic policy uncertainty.     

Forecasting economic magnitudes with neural network models

This paper is an introduction to artificial neural networks as a statistical and econometric tool. The fundamentals of the theory are presented and two applications illustrate the power of artificial neural networks in predicting results.     

Forecasting economic magnitudes with neural network models

This paper is an introduction to artificial neural networks as a statistical and econometric tool. The fundamentals of the theory are presented and two applications illustrate the power of artificial neural networks in predicting results.An earlier version of this paper was incorrectly printed in the May 1999 issue ofIAER.     

Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data

In this paper, we develop a methodology for forecasting key macroeconomic indicators, based on business survey data. We estimate a large set of models, using an autoregressive specification, with regressors selected from business and household survey data. Our methodology is based on the Bayesian averaging of classical estimates method. Additionally, we examine the impact of deterministic and stochastic seasonality of the business survey time series on the outcome of the forecasting process. We propose an intuitive procedure for incorporating both types of seasonality into the forecasting process. After estimating the specified models, we check the accuracy of the forecasts.     

Credit growth,monetary policy and economic activity in a three-regime TVAR model

We employ a Threshold Vector Autoregression (TVAR) methodology in order to examine the nonlinear nature of the interactions among credit market conditions, monetary policy and economic activity. We depart from the existing literature on the subject along two dimensions. First, we focus on a model in which the relevant threshold variable describes the state of economic activity rather than credit market conditions. Second, in contrast to the existing TVAR literature, which concentrates exclusively on single-threshold models, we allow for the presence of a second threshold, which is overwhelmingly supported by all relevant statistical tests. Our results indicate that the dynamics of the interactions among credit market conditions, monetary policy and economic activity change considerably as the economy moves from one phase of the business cycle to another and that single-threshold TVAR models are too restrictive to fully capture the nonlinear nature of those interactions. The impact of most shocks tends to be largest during periods of subpar economic activity and smallest during times of moderate economic growth. By contrast, credit risk shocks have the largest impact when output growth is considerably above its long-term trend.     

TFP growth and its determinants: a model averaging approach

Total Factor Productivity (TFP) accounts for a sizable proportion of the income differences across countries. Two challenges remain to researchers aiming to explain these differences: on the one hand, TFP growth is hard to measure empirically; on the other hand, model uncertainty hampers consensus on its key determinants. This paper combines a non-parametric measure of TFP growth with Bayesian model averaging techniques in order to address both issues. Our empirical findings suggest that the most robust TFP growth determinants are time-invariant unobserved heterogeneity and trade openness. We also investigate the main determinants of two TFP components: efficiency change (i.e., catching up) and technological progress.     

Personality traits and economic activity

Following Lynn’s (1991) studying showing that the scores of undergraduate students in 43 countries on variables such as work ethic and achievement motivation predicted per capita income and economic growth rates in those countries, the present study explored whether the average scores of residents of the 50 states in the United States were associated with the gross state product. States whose residents scored higher on openness had higher gross state products while those whose residents scored higher in openness and lower on neuroticism had higher gross state products per capita.     

Unanticipated inflation and economic activity

This paper presents a new empirical test of the expectations-adjusted supply fucntion, obtaining the market's inflationary expectations from the market interest rate. The results support the existence of a positive relationship between unanticipated inflation and the level of economic activity, while anticipated inflation seems to have no effect.