GDP Forecasts Research Articles

“Intelligent” finance and treasury management: what we can expect

Artificial intelligence poses a particular challenge in its application to finance/treasury management because most treasury functions are no longer physical processes, but rather virtual processes that are increasingly highly automated. Most finance/treasury teams are knowledge workers who make decisions and conduct analytics within often dynamic frameworks that must incorporate environmental considerations (foreign exchange rates, GDP forecasts), internal considerations (growth needs, business trends), as well as the impact of any actions on related corporate decisions which are also highly complex (e.g., hedging, investing, capital structure, liquidity levels). Artificial intelligence in finance and treasury is thus most analogous to the complexity of a human nervous system as it encompasses far more than the automation of tasks. Similar to the human nervous system, AI systems in finance/treasury must manage data quickly and accurately, including the capture and classification of data and its integration into larger datasets. At present, the AI network neural system has been gradually improved and is widely used in many fields of treasury management, such as early warning of potential financial crisis, diagnosis of financial risk, control of financial information data quality and mining of hidden financial data, information, etc.

Modelling and Forecasting of GDP in Bangladesh: An ARIMA Approach

Modelling and Forecasting of GDP in Bangladesh: An ARIMA Approach

Прогнозування попиту на паливно-енергетичні ресурси для енергоємних видів продукції з урахуванням потенціалу енергозбереження до 2040 р.

This paper uses the direct account method to predict energy consumption at two hierarchical levels: the upper level corresponds to the country, and the lower to industrial production, social sphere, and population. When forecasting at the upper level, we applied the parameters of GDP energy intensity and the GDP forecast with estimation of the energy-saving potential from structural and technological changes. At the lower level, the parameters of energy efficiency were applied according to each group: industrial production – specific consumptions of energy resources and production forecast, social sector – specific consumptions of fuel and energy resources for municipal and other economic services, and forecast of the volume of services provided, population – demographic forecast and predicted volume of heated housing. For the prediction of energy consumption in industrial production, from 51 types of industrial products, 26 most energy-intensive ones were selected, which, in total, consume at least 70% of energy resources and have high specific consumptions of energy resources for at least one type of them. For each of 26 types of products, we chose measures of energy savings and estimated the potential for energy savings up to 2040. We calculated the forecasts for fuel, heat, and electricity consumption by 26 types of products, the forecasts of consumption of total fuel and energy resources up to 2040 for energy-intensive products, population, and social sphere. A comparison of the correspondence between obtained forecasts at the upper and lower levels for certain types of energy resources is made. We conclude that it is necessary to combine several methods in forecasting energy consumption for different types of energy resources, for example, normative and direct account.

Prévoir la croissance du PIB en lisant le journal

GDP statistics in France are published on a quarterly basis, 30 days after the end of the quarter. In this article, we consider media content as an additional data source to traditional economic tools to improve short-term forecast / nowcast of French GDP. We use a database of more than a million articles published in the newspaper Le Monde between 1990 and 2017 to create a new synthetic indicator capturing media sentiment about the state of the economy. We compare an autoregressive model augmented by the media sentiment indicator with a simple autoregressive model. We also consider an autoregressive model augmented with the Insee Business Climate indicator. Adding a media indicator improves French GDP forecasts compared to these two reference models. We also test an automated approach using penalised regression, where we use the frequencies at which words or expressions appear in the articles as regressors, rather than aggregated information. Although this approach is easier to implement than the former, its results are less accurate.

Term Structure, Forecast Revision and the Signaling Channel of Monetary Policy

Monetary policy shocks affect interest rates at long horizons (10 years or more). Furthermore, the private sector’s real GDP forecasts are revised upward in response to a monetary tightening. These facts challenge the prevailing theories in academic and policy circles. In this paper, I propose a micro-founded model to rationalize those facts, based on the signaling channel of monetary policy. I consider a framework where the central bank has private information about future economic conditions. Agents update their beliefs according to Bayes’ theorem. Policy actions play a signaling role, and may therefore rationalize the above empirical findings.

Information content of DSGE forecasts

AbstractThis paper examines the question whether information is contained in forecasts from dynamic stochastic general equilibrium (DSGE) models beyond that contained in lagged values, which are extensively used in the models. Four sets of forecasts are examined. The results are encouraging for DSGE forecasts of real GDP. The results suggest that there is information in the DSGE forecasts not contained in forecasts based only on lagged values, and that there is no information in the lagged‐value forecasts not contained in the DSGE forecasts. The opposite is true for forecasts of the GDP deflator.

Information Content of DSGE Forecasts

This paper examines the question whether information is contained in forecasts from DSGE models beyond that contained in lagged values, which are extensively used in the models. Four sets of forecasts are examined. The results are encouraging for DSGE forecasts of real GDP. The results suggest that there is information in the DSGE forecasts not contained in forecasts based only on lagged values and that there is no information in the lagged-value forecasts not contained in the DSGE forecasts. The opposite is true for forecasts of the GDP deflator.

Firm performance and macro forecast accuracy

Combining a unique survey of Japanese firms’ GDP forecasts with accounting data for 25 years, we find three main results. First, firms’ GDP forecasts are associated with their employment, investment, and output growth in the subsequent year. Second, over optimistic and pessimistic forecast errors predict lower profitability and productivity, consistent with our model of input choice under uncertainty. Third, larger and more cyclical firms make forecasts closer to professionals, presumably reflecting their higher return to accuracy. Forecasts by more productive and older firms are also more similar to professional forecasts, implying forecasting ability is linked to management ability and experience.

Euro area real-time density forecasting with financial or labor market frictions

We compare real-time density forecasts for the euro area using three DSGE models. The benchmark is the Smets and Wouters model, and its forecasts of real GDP growth and inflation are compared with those from two extensions. The first adds financial frictions and expands the observables to include a measure of the external finance premium. The second allows for the extensive labor-market margin and adds the unemployment rate to the observables. The main question that we address is whether these extensions improve the density forecasts of real GDP and inflation and their joint forecasts up to an eight-quarter horizon. We find that adding financial frictions leads to a deterioration in the forecasts, with the exception of longer-term inflation forecasts and the period around the Great Recession. The labor market extension improves the medium- to longer-term real GDP growth and shorter- to medium-term inflation forecasts weakly compared with the benchmark model.

An Analysis of the Eurosystem/ECB Projections

The Eurosystem/ECB staff macroeconomic projection exercises constitute an important input to the ECB's monetary policy. This work marks a thorough analysis of the Eurosystem/ECB projection errors by looking at criteria of optimality and rationality using techniques widely employed in the applied literature of forecast evaluation. In general, the results are encouraging and suggest that Eurosystem/ECB staff projections abide to the main characteristics that constitute them reliable as a policy input. Projections of GDP - up to one year - and inflation are optimal - in the case of inflation they are also rational. A main finding is that GDP forecasts can be substantially improved, especially at long horizons.

Forecasting GDP Growth: Application of Autoregressive Integrated Moving Average Model

This paper uses Box-Jenkins approach to model and forecast real GDP growth in Ethiopia. Such an approach could easily provide forecast for key macroeconomic variables in limited data environment. Based on the approach, the paper estimates Autoregressive Integrated Moving Average ARIMA (1,1,1) model and forecasts real GDP growth. Both the in-sample fit and pseudo-out of sample forecasts show that the ARIMA model’s performance are good and better than other forecasts.

Tfp, News, and “Sentiments”: the International Transmission of Business Cycles

AbstractWe propose a novel identification scheme for a nontechnology business cycle shock, which we label “sentiment”. This is a shock orthogonal to identified surprise and news TFP shocks that maximize the short-run forecast error variance of an expectational variable, alternatively a GDP forecast or a consumer confidence index. We then estimate the international transmission of three identified shocks—surprise TFP, news of future TFP, and sentiment—from the United States to Canada. The US sentiment shock produces a business cycle in the United States, with output, hours, and consumption rising following a positive shock, and accounts for the bulk of the US short-run business cycle fluctuations. The sentiment shock also has a significant impact on Canadian macroaggregates. In the short run, it is more important than either the surprise or the news TFP shocks in generating business cycle comovement between the United States and Canada, accounting for over 40% of the forecast error variance of Canadian GDP and over one-third of Canadian hours, imports, and exports. The news shock is responsible for some comovement at 5–10 years, and surprise TFP innovations do not generate synchronization. We provide a simple theoretical framework to illustrate how the US sentiment shocks can transmit to Canada.

Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information

How did DSGE model forecasts perform before, during and after the financial crisis, and what type of off-model information can improve the forecast accuracy? We tackle these questions by assessing the real-time forecast performance of a large DSGE model relative to statistical and judgmental benchmarks over the period from 2000 to 2013. The forecasting performances of all methods deteriorate substantially following the financial crisis. That is particularly evident for the DSGE model’s GDP forecasts, but augmenting the model with a measure of survey expectations made its GDP forecasts more accurate, which supports the idea that timely off-model information is particularly useful in times of financial distress.

Google data in bridge equation models for German GDP

Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.

Analysis of Bilateral Trade Flow and Machine Learning Algorithms for GDP Forecasting

Analysis of Bilateral Trade Flow and Machine Learning Algorithms for GDP Forecasting

Analysis of Bilateral Trade Flow and Machine Learning Algorithms for GDP Forecasting

The terms imports and exports describe goods and services traded between countries. Countries import goods they cannot produce domestically or can obtain at a lower cost from another country. According to the World Trade Organization (WTO) reports, the U.S. is the world’s largest importer based on capital investment, followed by the E.U., China, Germany, and Japan. For exports, China leads the world with an official trade amount of $1.904 trillion in 2013. E.U. ranks second, followed by U.S., Germany, and Japan. Trade in goods and services is defined as a change in ownership of material resources and services between economies. Trade indicators include the sale of goods and services as well as barter transactions or goods exchanged and are measured in million USD, the percentage of GDP for net trade, and the annual export and import growth. This study analyzes imports and exports of all countries for the 1960-2017 period and evaluates the correlations in trade statistics to predict future imports and exports. Since the GDP for any country depends mainly on trade, this study examines trade data and compares various machine learning algorithms to forecast a country’s GDP.

World Economic Prospects Monthly

Overview: US strength has a sting in the tail for EM ▀ Although we have made no changes to our overall global GDP forecasts, we now see the US expansion easing more gradually in 2019 than previously assumed. But the knock‐on effects of faster Fed tightening and a stronger US$, at least in the short term, have prompted us to become more downbeat about growth prospects in the emerging markets (EM). ▀ World GDP growth in H1 was remarkably resilient. But recent news has been more negative overall. The global composite PMI in September fell to its lowest level this year, perhaps a sign that the gloomy mood music of H1 is finally filtering through to the real economy. Meanwhile, some of the key concerns of 2018 to date – protectionism, populism in Italy, higher oil prices and tougher conditions for emerging markets – have either escalated or remain firmly to the fore. ▀ The big plus has been signs that the US economy is continuing to fare well and appears resilient to the threat from trade wars. Despite President Trump announcing tariffs on a further $200bn of Chinese goods, we have raised our 2019 US GDP growth forecast by 0.2pp to 2.5%. However, this positive development has a sting in the tail. This strength, along with Fed comments, has led us to raise our Fed funds forecast – we now see three, rather than two, hikes in 2019. Concerns that the US neutral interest rate is rising has also pushed up bond yields. All this may prompt the US dollar to rise further in the near term. ▀ These developments, plus a slightly weaker outlook for China, higher oil prices and recent EM financial market weakness, have prompted us to downgrade our emerging market forecasts. We now see GDP growth in EMs slowing from 4.4% this year to 4.2% in 2019 (down 0.2pp from last month). ▀ Overall, we see world GDP growth slowing from 3.1% this year to a still‐solid 2.8% in 2019. Recession risks still appear very low for 2019, but vulnerabilities have risen.

Combining Model‐Based Near‐Term GDP Forecasts and Judgmental Forecasts: A Real‐Time Exercise for the G7 Countries

AbstractWe investigate the effects of combining model‐based near‐term GDP forecasts and judgmental (quarterly) forecasts by professional analysts (Consensus forecasts) in a real‐time setting for the G7 countries over the years 1999–2013. Model‐based forecasts are produced by a dynamic factor model (DFM). We consider as combination schemes the weighted average and the linear combination. Combining with subjective information delivers sizable gains in forecasting ability of statistical models for all countries except Japan, even when subjective forecasts are somewhat dated. Accuracy gains are much more pronounced in the volatile period after 2008 due to a marked improvement in predictive power of Consensus forecasts relative to the DFM. A possible explanation is that mechanical models may be more vulnerable to extreme observations in estimation samples. Consensus forecasts are superior at the moment of publication for most countries since 2008. For some countries forecast combinations can improve upon Consensus forecasts in between the quarterly release dates of the Consensus survey.

Short Term Forecasts of Economic Activity: Are Fortnightly Factors Useful?

A short term mixed-frequency model is proposed to estimate and forecast the Italian economic activity fortnightly. Building on Frale et al. (2011), we introduce a dynamic factor model with three frequencies (quarterly, monthly and fortnightly), by selecting indicators that show significant coincident and leading properties and are representative of both demand and supply. We find that high-frequency indicators improve the real time forecasts of Italian GDP. Moreover, the model provides a new fortnightly indicator of GDP, consistent with the official quarterly series. Our results emphasize the potential benefit of the high frequency series, providing forecasting gains beyond those based on monthly variables alone.

'Deja Vol' Revisited: Survey Forecasts of Macroeconomic Variables Predict Volatility in the Cross-Section of Industry Portfolios

We investigate the question whether macroeconomic variables contain information about future stock volatility beyond that contained in past volatility. We show that forecasts of GDP and industrial production growth from the Federal Reserve's Survey of Professional Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher growth rates is associated with lower stock volatility. Inflation forecasts predict higher or lower stock volatility depending on the state of the economy and the stance of monetary policy. Forecasts of higher unemployment rates are good news for stocks during expansions and go along with lower stock volatility. Our results hold in- as well as out-of-sample and pass various robustness checks.