MONTHLY GDP FORECASTING USING BRIDGE MODELS: APPLICATION FOR THE FRENCH ECONOMY

This paper presents a model to predict French gross domestic product (GDP) quarterly growth rate. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides, allowing thus economic interpretations. For each GDP component, bridge equations are specified by using a general‐to‐specific approach implemented in an automated way by Hoover and Perez and improved by Krolzig and Hendry. This approach allows to select explanatory variables among a large data set of hard and soft data. A rolling forecast study is carried out to assess the forecasting performance in the prediction of aggregated GDP, by taking publication lags into account in order to run pseudo real‐time forecasts. It turns out that the model outperforms benchmark models. The results show that changing the set of equations over the quarter is superior to keeping the same equations over time. In addition, GDP growth seems to be more precisely predicted from a supply‐side approach rather than a demand‐side approach.     

Nowcasting the French index of industrial production: A comparison from bridge and factor models

Governments and central banks need to have an accurate and timely assessment of indicators for the current month, as this is essential for providing a reliable and early analysis of the current economic situation. The index of industrial production (IIP) is probably the most important and widely analyzed monthly indicator, given the relevance of the manufacturing activity as a driver of the whole business cycle. This paper presents a series of models conceived to forecast the current French monthly IIP, based on regression models and dynamic factor models. The combination of these two approaches allows selecting economically relevant explanatory variables among a large data set. In addition, a rolling forecast study is carried out to assess the forecasting performance of the estimated models, using predictive ability and model confidence set tests. This latter allows getting several models displaying equivalent forecasting performance and therefore gives robustness to the forecasting exercise rather than to base the forecasting analysis only on one model.     

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.     

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.     

A bottom-up approach for forecasting GDP in a data-rich environment

In an increasingly data-rich environment, the use of factor models for forecasting purposes has gained prominence in the literature and among practitioners. Herein, we assess the forecasting behaviour of factor models to predict several GDP components and investigate the performance of a bottom-up approach to forecast GDP growth in Portugal, which was one of the hardest hit economies during the latest economic and financial crisis. We find supporting evidence of the usefulness of factor models and noteworthy forecasting gains when conducting a bottom-approach drawing on the main aggregates of GDP.     

Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.     

Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007–08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015–16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid–19 shock of 2020–21.

     

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.     

Forecast accuracy of small and large scale dynamic factor models in developing economies

Developing economies usually present limitations in the availability of economic data. This constraint may affect the capacity of dynamic factor models to summarize large amounts of information into latent factors that reflect macroeconomic performance. This paper addresses this issue by comparing the accuracy of two kinds of dynamic factor models at GDP forecasting for six Latin American countries. Each model is based on a dataset of different dimensions: a large dataset composed of series belonging to several macroeconomic categories (large scale dynamic factor model) and a small dataset with a few prescreened variables considered as the most representative ones (small scale dynamic factor model). Short‐term pseudo real time out‐of‐sample forecast of GDP growth is carried out with both models reproducing the real time situation of data accessibility derived from the publication lags of the series in each country. Results (i) confirm the important role of the inclusion of latest released data in the forecast accuracy of both models, (ii) show better precision of predictions based on factors with respect to autoregressive models and (iii) identify the most adequate model for each country according to availability of the observed data.     

Bottom-up or direct? Forecasting German GDP in a data-rich environment

In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.     

UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.     

Dissecting models' forecasting performance

The fact that the predictive performance of models used in forecasting stock returns, exchange rates, and macroeconomic variables is not stable and varies over time has been widely documented in the forecasting literature. Under these circumstances excessive reliance on forecast evaluation metrics that ignores this instability in forecasting accuracy, like squared errors averaged over the whole forecast evaluation sample, masks important information regarding the temporal evolution of relative forecasting performance of competing models. In this paper we suggest an approach based on the combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) of Welch and Goyal (2008) and the Bayesian change point analysis of Barry and Hartigan (1993) that tracks the contribution of forecast errors to the aggregate measures of forecast accuracy observation by observation. In doing so, it allows one to track the evolution of the relative forecasting performance over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.     

On winning forecasting competitions in economics

To explain which methods might win forecasting competitions on economic time series, we consider forecasting in an evolving economy subject to structural breaks, using mis-specified, data-based models. ‘Causal’ models need not win when facing deterministic shifts, a primary factor underlying systematic forecast failure. We derive conditional forecast biases and unconditional (asymptotic) variances to show that when the forecast evaluation sample includes sub-periods following breaks, non-causal models will outperform at short horizons. This suggests using techniques which avoid systematic forecasting errors, including improved intercept corrections. An application to a small monetary model of the UK illustrates the theory.     

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.     

Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries

This study provides the first attempt to examine the ability of the price of fine wine to forecast the Gross Domestic Product (GDP) for the major developed countries. Considering the limitation of a linear Granger causality test in detecting nonlinear causal relationships, a nonlinear Granger causality test is also employed. The results from our nonlinear causality test show that this new variable contains useful information to forecast GDP for the US, the UK, and Australia, suggesting that we may include it as a forecasting variable in GDP forecasting models, especially nonlinear models, for these three countries.     

Forecasting GDP at the Regional Level with Many Predictors

In this study, 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 with an autoregressive benchmark model, both for short‐ and long‐term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.     

Forecasting and combining competing models of exchange rate determination

This article investigates the out-of-sample forecast performance of a set of competing models of exchange rate determination. We compare standard linear models with models that characterize the relationship between exchange rate and the underlying fundamentals by nonlinear dynamics. Linear models tend to outperform at short forecast horizons especially when deviations from long-term equilibrium are small. In contrast, nonlinear models with more elaborate mean-reverting components dominate at longer horizons especially when deviations from long-term equilibrium are large. The results also suggest that combining different forecasting procedures generally produces more accurate forecasts than can be attained from a single model.     

FORECASTING THE YIELD CURVE USING PRIORS FROM NO‐ARBITRAGE AFFINE TERM STRUCTURE MODELS*

I propose a strategy for forecasting the term structure of interest rates that may produce significant gains in predictive accuracy. The key idea is to use the restrictions implied by Gaussian, no‐arbitrage, affine term structure models on a vector autoregression as prior information instead of imposing the restrictions dogmatically. This allows us to account for possible model misspecification. We use the proposed method to forecast a system of five U.S. yields up to 12 months ahead, and we find it provides significant gains in forecast accuracy.     

Time-series model forecasts and structural breaks: evidence from Spanish pre-EMU interest rates

Analysis of the future behaviour of economic variables can be biased if structural breaks are not considered. When these structural breaks are present, the in-sample fit of a model gives us a poor guide to ex ante forecast performance. This problem is true for both univariate and multivariate analysis and can be extremely important when co-integration relationships are analysed. The main goal of this article is to analyse the impact of structural breaks on forecast accuracy evaluation. We focus on forecasting several interest rates from the Spanish interbank money market. In order to carry out the analysis, we perform two forecasting exercises: (a) without structural breaks and (b) when structural breaks are explicitly considered. We use new sequential methods in order to estimate change-points in an endogenous way. This method allows us to detect structural breaks in all four rates in May 1993. However, the effects of these breaks are not very strong, since we found scarce gains in forecasting accuracy when the structural breaks are included in the models.     

Combination forecasts of tourism demand with machine learning models

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: support vector regression, Gaussian process regression and neural network models. We use an autoregressive moving average model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.