The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey

This paper introduces a new nowcasting model of the French quarterly real GDP growth rate (MIBA), developed at the Banque de France and based on monthly business surveys. The model is designed to target initial announcements of GDP in a mixed-frequency framework. The selected equations for each forecast horizon are consistent with the time frame of real-time nowcasting exercises: the first one includes mainly information on the expected evolution of economic activity, while the second and third equations rely more on information on observed business outcomes. The predictive accuracy of the model increases over the forecast horizon, consistent with the gradual increase in available information. Furthermore, the model outperforms a wide set of alternatives, such as its previous version and MIDAS regressions, although not a specification including also hard data. Further research should evaluate the performance of the MIBA model with respect to promising alternative approaches for nowcasting GDP (e.g. mixed-frequency factor models with targeted predictors), and consider forecast combinations and density forecasts.     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.     

Exports,Supply Constraints and Growth: An Investigation using Regional Data

In this work we first model the role of demand‐ and supply‐side factors (labour market adjustment, productive efficiency) in explaining economic growth. Empirically testing the model, we evaluate why different growth regimes may appear in the 20 Italian administrative regions. This exercise uses a two‐stage econometric approach. Estimates for the elasticity of manufacturing output to exports are obtained from regional time series: a significant long‐run relationship indicates the existence of a demand‐constrained growth regime. We then ascertain whether the regional dispersion of supply‐side factors has an impact on the regional dispersion of growth regimes. The empirical evidence supports our expectations of strong regional differences. Southern regions are less likely to display demand‐constrained regimes. In explanation of these differences, second‐stage analysis reveals that a strong role is played by such efficiency‐enhancing factors as technological innovation, bank diffusion and ‘social capital’. No role is found for labour market rigidities.     

Balance‐of‐payment constrained growth: the case of China, 1979–2002

The aim of this study is to empirically test the validity of Thirlwall’s Law in China during the reform period of 1979–2002. This study finds: (1) that for 1979–2002, the Chinese economy has grown on average as fast as Thirlwall’s Law predicts – the average actual growth rate and predicted growth rate were, respectively, 9.25 and 8.55, which are statistically identical; (2) that the growth of GDP and of exports are cointegrated. Both (1) and (2) provide strong support for Thirlwall’s Law in China during the reform period after 1978. The supportive result of Thirlwall’s Law implies the relevance of a demand‐side approach to the economic growth in China. For time series analyses, a bounds test approach is adopted.     

TESTING CATCHING‐UP BETWEEN THE DEVELOPING COUNTRIES: “GROWTH RESISTANCE” AND SOMETIMES “GROWTH TRAGEDY”

This paper provides empirical evidence that there is no convergence between the GDP per‐capita of the developing countries since 1950. Relying upon recent econometric methodologies (non‐stationary long‐memory models, wavelet models and time‐varying factor representation models), we show that the transition paths to long‐run growth (the catch‐up dynamics) are very persistent over time and non‐stationary, thereby yielding a variety of potential steady states (conditional convergence). Our findings do not support the idea according to which the developing countries share a common factor (such as technology) that eliminates per‐capita output divergence in the very long run. Instead, we conclude that growth is an idiosyncratic phenomenon that yields different forms of transitional economic performance: growth tragedy (some countries with an initial low level of per‐capita income diverge from the richest ones), growth resistance (with many countries experiencing a low speed of growth convergence), and rapid convergence.     

Innovation,Diffusion and Cumulative Causation: Changes in the Spanish Growth Regime, 1960–2001

This article presents a model of macroeconomic growth that combines in a single formalization two complementary views on innovation and economic growth, the technology‐gap approach and the Kaldorian theory of cumulative causation. The model suggests that what matters for economic growth in the long run is the existence of a good match between the patterns of technological change, income distribution and demand growth. The model is estimated for the Spanish economy during the period 1960–2001, and the econometric results show that important changes have happened in its growth regime over time. Since the 1980s, innovation and diffusion of new technologies provide a greater stimulus to productivity growth, but the technology push on the supply‐side is not sustained by the prevailing patterns of income distribution and demand growth.     

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.     

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.

     

Mark-ups, economies of scale and the role of knowledge spillovers in OECD industries

Using R&D-based models of economic growth as a foundation, this paper argues that market-driven knowledge creation is necessarily linked as an engine of productivity growth to economies of scale and market-power. A cost function and factor demand model is applied to a cross-country industry data set to study market-power, economies of scale and the role of knowledge in an integrated approach. Empirical results reveal the presence of market-power and economies of scale in all of the industries investigated. R&D and spillovers explain some of the productivity growth observed. Spillovers are identified as an external source of economies of scale.     

FORECASTING US INFLATION USING DYNAMIC GENERAL‐TO‐SPECIFIC MODEL SELECTION

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out‐of‐sample forecasts are generated from models identified from a multipath general‐to‐specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well‐established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.     

Forecasting economic activity by Bayesian bridge model averaging

This paper proposes the use of Bayesian model averaging (BMA) as an alternative tool to forecast GDP relative to simple bridge models and factor models. BMA is a computationally feasible method that allows us to explore the model space even in the presence of a large set of candidate predictors. We test the performance of BMA in now-casting by means of a recursive experiment for the euro area and the three largest countries. This method allows flexibility in selecting the information set month by month. We find that BMA-based forecasts produce smaller forecast errors than standard bridge model when forecasting GDP in Germany, France and Italy. At the same time, it also performs as well as medium-scale factor models when forecasting Eurozone GDP.     

PMIs: Reliable indicators for exports?

Foreign economic activity is a major determinant of export developments. However, foreign GDP figures are published too late to be useful for short‐term forecasting. This paper presents a number of indicators based on the widely available PMI surveys that provide very early signals of foreign activity. Using MIDAS models we analyze the in‐ and out‐of‐sample performance of these and related indicators for two very trade‐exposed countries (Germany and Switzerland). We find that the monthly indicators based on foreign PMIs are strongly correlated with quarterly export growth. The forecast comparison shows that PMI‐based indicators perform very well relative to other benchmark models.     

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.     

结构变化、效率改进与能源需求预测——以中国电力行业为例

本文利用宏观经济学的方法提出一个长期电力需求模型以分析影响中国电力需求的主要因素。如预期的一样 ,当各种因素受市场力量的进一步约束时 ,影响需求的各项变量之间的关系在中国经济改革以后更加稳定也更为显著。一个误差修正模型为预测中国电力总需求的短期波动提供了适合的框架。 1 978年经济改革以后 ,需求的GDP弹性估计为 0 .8左右 ,低于改革前 (1 978年以前 )。结果表明 ,虽然GDP仍是影响电力需求的最重要因素 ,但电力需求与中国的结构变化及效率改进是负相关的。这意味着对于一个快速增长的经济来说 ,GDP的高速增长并不总是伴随着高的电力需求 ,并解释了为什么 1 998年经济增长率为 7 8% ,但电力消费却只增长了 2 8%。     

Government Size and Stochastic Growth

This paper analyses the effects of the size of government on economic growth in a stochastic endogenous growth model involving the supply‐side effect and demand‐side effect produced by government spending. We show that a rise in the government spending affects economic growth through three channels, including the crowding‐out effect, the spin‐off effect and the resource mobilisation effect. We demonstrate that there exists an optimal size of government that maximises the economic growth rate.     

Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach

Abstract Quantifying the probability of U.S. recessions has become increasingly important since August 2007. In a data‐rich environment, this paper is the first to apply a Probit model to common factors extracted from a large set of explanatory variables to model and forecast recession probability. The results show the advantages of the proposed approach over many existing models. Simulated real‐time analysis captures all recessions since 1980. The proposed model also detects a significant jump in the next six‐month recession probability based on data up to November 2007, one year before the formal declaration of the recent recession by the NBER.     

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.     

Short-Run and Long-Run Effects of Copper Mining on Peru’s Recent Economic Growth

Peru is the second-largest producer and exporter of copper in the world. This paper proposes a novel approach to assess short-run and long-run effects of copper on Peru’s recent economic growth. Annual data over the 2014–2018 period were used to calculate a Mining Contribution Index (MCI). An institutional quality indicator of the World Competitiveness Index of the World Economic Forum measured the dependence of Peruvian economic growth on mining and the quality of its institutions, respectively. Then, monthly data during the period 2005–2018 were used to run vector autoregressive (VAR) and vector error correction (VEC) models to measure copper’s effects on the country’s economy over time. VAR-VEC models included copper production, exports, international price, investment, taxes paid by producing companies, and Peru’s gross domestic product (GDP). Stationarity and causality of variables were verified with the Augmented Dickey-Fuller and Granger tests, respectively. Due to the presence of non-stationary variables, a VEC model was implemented to forecast short- and long-run effects. The main results show that real GDP responds to copper output and other related explanatory variables differently, depending upon the instrument applied. Peruvian GDP has increased dependence on copper mining. The quality of its institutions could explain the presence of Dutch Disease or resource curse theory. Short- and long-run effects of copper output on GDP were generally statistically non-significant. GDP was statistically significant in relation to other mining variables, such as copper exports and the international price of copper.

     

A nowcasting model for Ecuador: Implementing a time-varying mean output growth

We propose a model to nowcast the annual growth rate of real GDP for Ecuador, whose economy lacks timely macroeconomic information for some key variables and has gone through unstable periods due to its dependence on oil exports. Our specification combines monthly information for 30 macroeconomic and financial variables with quarterly information for real GDP in a mixed-frequency approach. Our setup includes a time-varying coefficient on the mean annual growth rate of output to allow the model to incorporate prolonged periods of low or high growth. The model produces more accurate nowcasts of real output growth in pseudo out-of-sample exercises than a nowcasting model that assumes a constant mean real GDP growth rate. We also conduct sensitivity analyses on our nowcasting model within the time-varying mean setup and find that including financial variables can be detrimental to the performance of the proposed model.     

MILITARY SPENDING,THREATS AND STOCHASTIC GROWTH

This paper analyses the impact of home military spending and foreign military threat on economic growth in a stochastic endogenous growth model involving the supply‐side and demand‐side effects produced by military spending. The paper states that an increase in home military spending affects economic growth through three channels, including the crowding‐out effect, the spin‐off effect, and the resource mobilization effect. The net effect which depends on these three channels is ambiguous. Hence, we demonstrate that there exists an optimal defence burden that maximizes the economic growth rate. Furthermore, the optimal defence burden depends on the degree of risk preference. Namely, the optimal defence burden of the risk‐loving agent is more than that of the risk‐neutral agent, and in turn is more than that of the risk‐averse agent. At the same time, we prove that the relationship between the volatility in military spending and economic growth also depends on the degree of risk preference. In addition, we show that greater volatility in foreign military spending leads to a decrease in home aggregate consumption, and hence speeds up economic growth in the home country.