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.     

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.     

The macroeconomic drivers in hedge fund beta management

We investigate how macroeconomic indicators alter the dynamic risk exposure of different hedge fund style strategies. We implement a multifactor model to estimate the unobservable time-varying risk exposure conditional on macroeconomic information and a VAR to measure the impact of macroeconomic predictors on different time horizons. Using monthly returns on a cross-section of 10 different style indices from February 1997 to August 2019, we find that, on average, macroeconomic indicators explain approximately 30%, 55%, and 75% of the variability of betas at 1-, 6-, and 36-month horizons, respectively. Although macroeconomic predictors play a critical role at every horizon, at 1 month, the dominating effect comes from idiosyncratic shocks, which indicates that in the short run, hedge fund managers rely mostly on their own reallocation signals. Moreover, consistent with the fundamental drivers of the smart beta factors, we find that the interest rate level and GDP growth similarly impact hedge fund exposures across styles.     

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.     

BAYESIAN DYNAMIC VARIABLE SELECTION IN HIGH DIMENSIONS

This article addresses the issue of inference in time-varying parameter regression models in the presence of many predictors and develops a novel dynamic variable selection strategy. The proposed variational Bayes dynamic variable selection algorithm allows for assessing at each time period in the sample which predictors are relevant (or not) for forecasting the dependent variable. The algorithm is used to forecast inflation using over 400 macroeconomic, financial, and global predictors, many of which are potentially irrelevant or short-lived. The new methodology is able to ensure parsimonious solutions to this high-dimensional estimation problem, which translate into excellent forecast performance.     

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.     

Forecasting growth during the Great Recession: is financial volatility the missing ingredient?

The Great Recession endured by the main industrialized countries during the period 2008–2009, in the wake of the financial and banking crisis, has pointed out the major role of the financial sector on macroeconomic fluctuations. In this respect, many researchers have started to reconsider the linkages between financial and macroeconomic areas. In this paper, we evaluate the leading role of the daily volatility of two major financial variables, namely commodity and stock prices, in their ability to anticipate the output growth. For this purpose, we propose an extended MIDAS model that allows the forecasting of the quarterly output growth rate using exogenous variables sampled at various higher frequencies. Empirical results on three industrialized countries (US, France, and UK) show that mixing daily financial volatilities and monthly industrial production is useful at the time of predicting gross domestic product growth over the Great Recession period.     

Improving forecast accuracy of financial vulnerability: PLS factor model approach

We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.     

China's macroeconomic stability – an empirical study based on survey data

A complete financial stability analysis should include investigation on macroeconomic stability since macroeconomic development and potential imbalance can increase the financial instability and trigger a financial crisis. Survey data of rating on China's macroeconomic stability is analyzed by estimating an ordered logit model with random effect. Among the candidate macroeconomic indicators, we found that inflation is the key variable that determines China's macroeconomic stability, followed by the change in budget balance and GDP growth gap.     

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.     

Macroeconomic expectations and time varying heterogeneity:evidence from individual survey data

ABSTRACT

The goal of this paper is to investigate forecast heterogeneity and time variability in the formation of expectations using disaggregated monthly survey data on macroeconomic indicators provided by Bloomberg from June 1998 to August 2017. We show that our panel of forecasters are not rational and are moderately heterogeneous and thus confirm that previously well-established results on asset prices hold for macroeconomic indicators. We propose a flexible hybrid forecast model defined at any time as a combination of the extrapolative, regressive, adaptive and interactive heuristics. Controlling for endogenous structural breaks, we find that experts adjust their forecast behaviour at any time with some inertia in extrapolative and adaptive profiles. Changes in the formation of expectations are triggered mostly by financial shocks, and uncertainty is dealt with by using complex processes in which the fundamentalist component overweighs chartist activity. Forecasters whose models combine different relevant rules and display high temporal flexibility provide the most accurate forecasts. Authorities can then stabilize the domestic markets by encouraging fundamentalists’ forecasts through increased transparency policy.     

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.     

Predicting Canadian recessions using dynamic probit modelling approaches

Abstract This paper examines the ability of various financial and macroeconomic variables to forecast Canadian recessions. It evaluates four model specifications, including the advanced dynamic, autoregressive, dynamic autoregressive probit models as well as the conventional static probit model. The empirical results highlight several significant recession predictors, notably the government bond yield spread, growth rates of the housing starts, the real money supply and the composite index of leading indicators. Both the in‐sample and out‐of‐sample results suggest that the forecasting performance of the four probit models is mixed. The dynamic and dynamic autoregressive probit models are better in predicting the duration of recessions while the static and autoregressive probit models are better in forecasting the peaks of business cycles. Hence, the advanced dynamic models and the conventional static probit model can complement one another to provide more accurate forecasts for the duration and turning points of business cycles.     

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.     

Macroeconomic determinants of the credit risk in the banking system: The case of the GIPSI

In this paper, we analyze the link between the macroeconomic developments and the banking credit risk in a particular group of countries – Greece, Ireland, Portugal, Spain and Italy (GIPSI) – recently affected by unfavourable economic and financial conditions.Employing dynamic panel data approaches to these five countries over the period 1997q1–2011q3, we conclude that the banking credit risk is significantly affected by the macroeconomic environment: the credit risk increases when GDP growth and the share and housing price indices decrease and rises when the unemployment rate, interest rate, and credit growth increase; it is also positively affected by an appreciation of the real exchange rate; moreover, we observe a substantial increase in the credit risk during the recent financial crisis period. Several robustness tests with different estimators have also confirmed these results.The findings of this paper indicate that all policy measures that can be implemented to promote growth, employment, productivity and competitiveness and to reduce external and public debt in these countries are fundamental to stabilize their economies.     

Financial deepening and economic growth

The core of Shapley–Shubik games and general equilibrium models with a Venn diagram is applied for a theory on the role of real finance in economic growth among advanced economies. Then the dynamic computable general equilibrium (DCGE) models for Germany, France, the UK, Japan and the USA are constructed to assess the validity of the over-financing hypothesis that has reappeared after the financial crisis of 2008. Actual financial deepening ratios observed in the nonconsolidated balance sheet of the OECD exceeded by factors of 3.5, 2.4, 5.1, 11.6 and 4.8 than the optimal financial deepening ratios implied by DCGE models, respectively, in these countries because of excessive leveraging and bubbles up to 19 times of GDP which were responsible for this great recession. Containing such massive fluctuations for macroeconomic stability and growth in these economies are not possible in conventional fiscal and monetary policy models and require a DCGE analysis like this along with adoption of separating equilibrium strategy in line of Miller–Stiglitz–Roth mechanisms to avoid problem of asymmetric information in the process of financial intermediation so that the gaps between actual and optimal ratios of financial deepening remain as small as possible.     

Expected risk and excess returns predictability in emerging bond markets

This study examines the predictability of expected excess returns from eight emerging bond markets within an international asset pricing framework. Two sets of instruments are used, which include both world and local factors, to forecast emerging bond returns. Besides investigating the influence of the macroeconomic factors in specific countries on bond returns in those countries, this study also divides local factors into macroeconomic and financial factors. Unlike previous studies, we apply macroeconomic instruments that contain more information on excess returns as a proxy for local risk factors via principal component analysis methodology. The information variable approach enables the prediction of excess bond returns based on world and local factors and facilitating understanding of the degree of integration between emerging bond markets and developed bond markets. The results indicate that the bond market in emerging world is partially integrated to that in the developed world and the predictability of local factors that include both financial and macroeconomic information variables can forecast around 25–66% of the returns of emerging bonds. Incorporating the macroeconomic variables increases the explanatory power of the model. Both world and country-specific local instruments can forecast excess bond returns, but local instruments appear to be better predictors of such returns, particularly the local credit spread to US. Additionally, this study finds that investor risk aversion is significant among most of sample countries.     

Sovereign credit ratings,growth volatility and the global financial crisis

Using monthly data from January 1996 to May 2010 for a panel of 76 developed and emerging economies and adopting an instrumental variable (IV) estimation technique by correcting for both heterogeneity and endogeneity with the generalized two-stage least squares (G2SLS, EC2SLS) procedure method suggested by Balestra and Varadharajan-Krishnakumar (1987) and Baltagi and Li (1995), this article provides empirical evidence that volatility of per capita GDP growth is reduced when there are positive changes in credit ratings; in other words when sovereign credit risk improves. To deal with potential simultaneity between sovereign credit ratings and output volatility, a system (3SLS) approach is undertaken, and our findings remain robust. By weakening the volatility dampening effects of ratings changes, it is found that the global financial crisis (GFC) has enhanced macroeconomic volatility. One of the channels via which sovereign rating changes affect growth volatility is the financial markets’ repricing of sovereign default risk that is reflected in sovereign credit default swap (CDS) spreads and its volatility.     

Structural analysis with mixed-frequency data: A model of US capital flows

We develop a new structural Vector Autoregressive (SVAR) model for analysis with mixed-frequency data. The MIDAS-SVAR model allows to identify structural dynamic links exploiting the information contained in variables sampled at different frequencies. It also provides a general framework to test homogeneous frequency-based representations versus mixed-frequency data models. A set of Monte Carlo experiments suggests that the test performs well both in terms of size and power. The MIDAS-SVAR is then used to study how monetary policy and financial uncertainty impact on the dynamics of gross capital inflows to the US. While no relation is found when using standard quarterly data, mixed frequency analysis exploiting the variability present in the series within the quarter shows that the effect of an interest rate shock is greater the longer the time lag between the month of the shock and the end of the quarter.     

Real-time forecasting US GDP from small-scale factor models

We show that the single-index dynamic factor model developed by Aruoba and Diebold (Am Econ Rev, 100:20–24, 2010) to construct an index of the US business cycle conditions is also very useful to forecast US GDP growth in real time. In addition, we adapt the model to include survey data and financial indicators. We find that our extension is unequivocally the preferred alternative to compute backcasts. In nowcasting and forecasting, our model is able to forecast growth as well as AD and better than several baseline alternatives. Finally, we show that our extension could also be used to infer the US business cycles very precisely.