GDP Forecasts Research Articles

Analysis and Forecast of Urban Economic Vitality in Northeast China

Taking into account the passage of time, the original economic vitality index will vary with changes in social development, we use the BP neural network nearly a decade as the original GDP data for the next 30 years the GDP forecast. BP neural network in 1985, proposed by Rumelhart, the algorithm solves the system of learning problems multilayer neural network connection weights hidden layer [1].It consists of an input layer, a hidden layer, and an output layer. The principle is to continuously adjust the network weights and thresholds by transmitting errors backward and then correcting the errors to achieve the desired input-output mapping.

Nowcasting the Spanish Economy Using Very High Frequency Tax Data

We present a short-term forecasting model based on tax data. The model combines daily information from the Immediate Supply of Information System for VAT declaration forms, with monthly indicators derived from tax data. The model uses the GDP as a macroeconomic synthesis. The model combines signal extraction and forecasting at the daily frequency, by means of an unobserved components model, with a mixed frequency (monthly-quarterly) dynamic factor analysis for GDP now-casting and forecasting. The daily information, plus the flexibility and efficiency of the factor models, allows a permanently updated monitoring of the short-term economic conditions of the Spanish economy.

Economic Impact of COVID-19 in Nepal: A Question of Bad or Worst

Considering the fact and avoid its huge impact on economy, Nepal must take dramatic action to make sure economy is moving through this disaster. There will be the impact of 8 to 10 percent of GDP in this FY 2020 if this lockdown continue for 2 to 3 months. Government must make extra fund available for banks to lend to companies it is because business is already taking hit and nobody knows how bad it will hit but this is confirm that it depends on long this spread lasts. It is due to cost of isolation which will be higher and more dependable for each country. Protecting measures of governmental and non-governmental are severely affecting the economic activities. There is high impact on GDP but this impact is directly related to number of days the lockdown will be there. Extending the days for lockdown means extending the adverse impact on economy. Although global economy is projected contract by -3 percent in 2020, in reality, just projection and indices will not solve the issue, GDP forecast for bad or worst itself is dubious prediction of international financial institutions, businessmen, economist, media. What is confirm is that the impact will be much higher in Asian country and more in Nepal due to landlocked, open boarder and remittance based economy. In the recovery process, primary sector is necessary to mobilize well so that secondary and tertiary sector of the economy can get the opportunity of expansion and resource mobilization.

Forecasting the GDP per Capita for Egypt and Saudi Arabia Using ARIMA Models

Annual time series data is used to forecast GDP per capita using the Box-Jenkins Autoregressive-Integrated Moving-Average (ARIMA) model for the Egyptian and Saudi Arabian economies. The fitted ARIMA model is tested for per capita GDP forecasting of Egypt and of Saudi Arabia for the next ten years. Conclusions convey that the most accurate statistical model as in previous literature that forecast GDP per capita for Egypt and for Saudi Arabia is ARIMA (1,1,2) and ARIMA (1,1,1) respectively. The diagnostic tests reveal that the two models presented individually are both stable and reliable.

Covid-19's Impact on China's Economy Based on Data of Spring Festival Travel Rush

The outbreak of Covid-19 in China during the Spring Festival of 2020 has changed life as we knew it. To explore its impact on China's economy, we analyse the daily railway passenger volume data during the Spring Festival travel rush and establish two RegARMA models to predict GDP in the first quarter. The models forecast China might lose 4.8 trillion yuan in the first quarter of 2020 due to Covid-19, an expected decrease of 20.69 percent (year-on-year drop 15.60 percent). However, comparing our forecast GDP without Covid-19 (23.2 trillion yuan) with the real GDP (20.65 trillion yuan), there is a smaller drop of 2.55 trillion yuan, a gap of 12.35 percent. The reason for this overestimation is that some positive factors, including macro-control policies, are neglected in these models. With the global spread of Covid-19, China should adopt a policy of focusing on balancing both domestic and external affairs against the instability of the world economy.

Are GDP forecasts optimal? Evidence on European countries

We assess the accuracy of real GDP growth forecasts released by governments and international organizations for European countries in the years 1999–2017. We implement three testing procedures characterized by different assumptions on the forecasters’ loss functions. First, we test forecast rationality within the traditional approach based on a quadratic loss function (Mincer and Zarnowitz, 1969). Second, following Elliott, Timmermann and Komunjer (2005), we test rationality by allowing for a flexible loss function where the shape parameter driving the extent of asymmetry is unknown and estimated from the empirical distribution of forecast errors. Lastly, we implement the tests proposed by Patton and Timmermann (2007a) that hold regardless of the functional form of the loss function. We conclude that governmental forecasts are biased and not rational under a symmetric and quadratic loss function, but they are optimal under more general assumptions on the loss function. We also find that the preferences of forecasters change with the forecasting horizon: when moving from one- to two-year-ahead forecasts, the optimistic bias increases and the parameter of asymmetry in the loss function significantly increases.

The Fed's Response to Economic News Explains the “Fed Information Effect”

High-frequency changes in interest rates around FOMC announcements are a standard method of measuring monetary policy shocks. However, some recent studies have documented puzzling effects of these shocks on private-sector forecasts of GDP, unemployment, or inflation that are opposite in sign to what standard macroeconomic models would predict. This evidence has been viewed as supportive of a “Fed information effect” channel of monetary policy, whereby an FOMC tightening (easing) communicates that the economy is stronger (weaker) than the public had expected. We show that these empirical results are also consistent with a “Fed response to news” channel, in which incoming, publicly available economic news causes both the Fed to change monetary policy and the private sector to revise its forecasts. We provide substantial new evidence that distinguishes between these two channels and strongly favors the latter; for example, (i) regressions that include the previously omitted public economic news, (ii) a new survey that we conduct of Blue Chip forecasters, and (iii) high-frequency financial market responses to FOMC announcements all indicate that the Fed and private sector are simply responding to the same public news, and that there is little if any role for a “Fed information effect”.

Systematic Financial Intermediation and Business Cycles

Theory suggests that financial intermediation (FI) spurs economic growth by reducing investment frictions. Prior literature focuses on bank lending as a driver of economic growth, with limited success for US GDP. We augment bank balance sheet data with that from shadow banks, mutual and pension funds, insurers, and brokers, finding that aggregate FI assets lead GDP growth and forecast recessions up to three quarters in advance, although the weight of each FI group in the aggregate fluctuates. Aggregated FIs expand their total assets when the slope of the Treasury yield curve is upward sloping and the effective Federal funds rate is higher, thereby revealing the importance of monetary policy in the financial intermediation process. We further show that aggregate FI contains leading information about investment and consumption and predicts industrial production and unemployment.

Investor Protection, Aggregate Accounting Earnings and Forecasts of Growth in GDP: International Evidence.

This study draws on the investor protection literature to examine differences in a country’s information environment that are likely to explain cross country variation in the extent to which macroeconomic forecasters take account of current earnings when forecasting future growth in GDP. Using a unique sample of analysts’ GDP forecasts from thirty countries, we find that economies with stronger levels of disclosure and enforcement regulation have a lower association between changes in aggregate earnings and errors in forecasts of GDP growth. The results are consistent with macroeconomic forecasters in countries with stronger investor protections taking greater account of changes in aggregate earnings when forecasting growth in GDP than analysts in countries with lower levels of investor protection.

Categorical Forecasts and Non-Categorical Loss Functions

This paper introduces a new test of the predictive performance and market timing for categorical forecasts based on contingency tables when the user has non-categorical loss functions. For example, a user might be interested in the return of an underlying variable instead of just the direction. This new test statistic can also be used to determine whether directional forecasts are derived from non-directional forecasts and whether point forecast have predictive value when transformed into directional forecasts. The tests are applied to the categorical exchange rate forecasts in the ifo-Institute's World Economic Survey and to the point forecasts for quarterly GDP in the Philadelphia Fed's Survey of Professional Forecasters. We find that the loss function matters as exchange rate forecasters perform better under non-categorical loss functions, and the GDP forecasts have value up to two quarters ahead.

The Fed’s Response to Economic News Explains the ‘Fed Information Effect’

High-frequency changes in interest rates around FOMC announcements are a standard method of measuring monetary policy shocks. However, some recent studies have documented puzzling effects of these shocks on private-sector forecasts of inflation, unemployment, or real GDP, which have the opposite sign from what standard macroeconomic models would predict. This evidence has been viewed as supportive of a “Fed information effect” channel of monetary policy, whereby an FOMC tightening (easing) communicates that the economy is stronger (weaker) than the public had expected. We show that these empirical results are also consistent with a “Fed response to news” channel, in which incoming, publicly available economic news causes both the to change monetary policy and the private sector to revise its forecasts. We provide substantial new evidence that distinguishes between these two channels and strongly favors the latter; for example, (i) high-frequency stock market responses to announcements, (ii) a new survey that we conduct of individual Blue Chip forecasters, and (iii) regressions that include the previously omitted public macroeconomic data releases all indicate that the and Blue Chip forecasters are simply responding to the same public news, and that there is little if any role for a Fed information effect.

A Short Term Forecasting Model for the Spanish GDP and its Demand Components

This paper proposes a new version of the Spain-STING (Spain, Short-Term INdicator of Growth), a dynamic factor model used by the Banco de España for the short-term forecasting of the Spanish economy. The extended and revised version of the Spain-STING presented in this document includes a forecast for each of the demand components of the National Accounts. In order to select the indicators that best estimate the Spanish GDP and its demand components, several models are considered. Following this strategy, the selected models are those in which the common factor explains the highest proportion of the variance of the GDP. These models allow us to forecast GDP, private consumption, public expenditure, investment in capital goods, construction investment, exports and imports in a consistent way. We assess the predictive power of the models for GDP and its demand components for the period 2005–2017. With regard to the GDP forecast, we find some improvement of the predictive power compared to the previous version of Spain-STING. As for the demand components, we show that our proposal has better predictive power than other possible time series models.

The Power of Narratives in Economic Forecasts

We apply textual analysis tools to the narratives that accompany Federal Reserve Board economic forecasts to measure the degree of optimism versus pessimism expressed in those narratives. Text sentiment is strongly correlated with the accompanying economic point forecasts, positively for GDP forecasts and negatively for unemployment and inflation forecasts. Moreover, our sentiment measure predicts errors in FRB and private forecasts for GDP growth and unemployment up to four quarters out. Furthermore, stronger sentiment predicts tighter than expected monetary policy and higher future stock returns. Quantile regressions indicate that most of sentiment’s forecasting power arises from signaling downside risks to the economy and stock prices.

Research on GDP Forecast of Ji’an City Based on ARIMA Model

In this paper, based on the GDP of Ji’an City from 1978 to 2018, we use the ARIMA(0, 2, 1) model to forecast the GDP of Ji’an City from 2019 to 2023. The prediction results can provide scientific reference for Ji’an City to formulate economic development goals.

Anti-Crisis Fiscal Measures in the European Union during the COVID-19 Pandemic and their Impact on GDP

The aim of this study is to analyze the connection between anti-crisis fiscal measures adopted by EU governments in response to the COVID-19 pandemic and these countries’ GDP growth. The study relies on methods of statistical analysis, including cluster analysis, to examine the challenges of forecasting tax revenue collections during the COVID-19 pandemic. It is possible to make preliminary conclusions regarding the relationship between fiscal anti-crisis measures in EU countries and these countries’ GDP growth even in the absence of the actual data. The study has revealed variations in forecast GDP growth caused by a higher than usual degree of uncertainty. The best way to minimize such variations is to constantly monitor the situation and adjust the forecast estimates depending on the changes in the relevant factors. The variations in forecast estimates can also stem from adjustments for the changes in tax revenues of EU countries implementing fiscal anti-crisis measures. Most EU countries resorted to such instruments as deferral of certain tax payments, temporary tax breaks, reduction of tax rates, tax loss carryforwards, cancellation or reductions of social contributions. The European leaders in terms of anti-crisis fiscal measures are the Czech Republic and Ireland – these countries used four out of five instruments and were followed by Austria, Hungary and the UK, which used three instruments. We also analyzed the coefficient of tax elasticity for European countries and demonstrated that tax reliefs (tax preferences) influence the level of tax revenue. The hypothesis that there is an indirect connection between the anti-crisis fiscal measures and GDP growth was confirmed. It is shown that clusters of EU countries grouped depending on their anti-crisis fiscal measures do not coincide with the clusters of countries grouped depending on their GDP growth estimates. Thus, a tentative forecast can be made that the fiscal anti-crisis measures taken by EU countries will not have a direct impact on their GDP growth.

Real-time Forecasting of the Australian Macroeconomy Using Flexible Bayesian VARs

This paper evaluates the real-time forecast performance of alternative Bayesian Vector Autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and estimate a set of model specifications with different covariance structures. The results suggest that a large VAR model with 20 variables tends to outperform a small VAR model when forecasting GDP growth, CPI inflation and unemployment rate. We find consistent evidence that the models with more flexible error covariance structures forecast GDP growth and inflation better than the standard VAR, while the standard VAR does better than its counterparts for unemployment rate. The results are robust under alternative priors and when the data includes the early stage of the COVID-19 crisis.

Nowcasting East German GDP growth: a MIDAS approach

Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.

The Fed's Communications: Suggestions for Improvement

The chief economist of Berenberg Capital Markets proposes three broad ways of improving the Fed's communications: (1) establish a more systematic approach to achieving its dual mandate; (2) clarify the proper role of monetary policy in achieving those objectives by distinguishing what is within the scope of monetary policy from what is clearly beyond it; and (3) articulate the Fed's goals and role in achieving macro‐prudential risk management and financial stability.With these three ends in view, the author begins by urging FOMC members to refrain from making public comments immediately following government data releases and, when making public speeches and statements, to relate their comments on the economy to the Fed's dual mandate. The author also suggests three modifications of the Fed's official Policy Statement following FOMC meetings. First, each statement should start with an assessment of monetary policy and its consistency with achieving the Fed's statutory mandate, rather than the Fed's assessments of the economy and its subsectors with which such statements now begin. Second, the Fed should communicate separate explicit risk assessments of inflation and of the prospects for employment and the economy. (The Fed's current practice of sometimes dropping the risk assessments from statements and replacing them with nuanced language—for example, on changes in inflation and inflationary expectations—can be a source of confusion.) Third, all statements should discuss as clearly as possible the Fed's strategy for its balance sheet and unwind policy.The Fed's quarterly Summary of Economic Projections (SEPs) should be redesigned to include FOMC estimates of forecast uncertainties and what they imply for monetary policy, and such alternative forecasts should be presented in place of the current central tendency and range of forecasts. The redesigned SEPs should be (1) based on a rigorous Fed assessment of expected monetary policy under different situations and contingency planning, and (2) as transparent as possible about the Fed's economic and inflation outlooks, the uncertainties in forecasting, and the conditionality of monetary policy. An illustration is provided of the alternative SEPs that would replace the current “dot plots” and include the Fed's forecasts of nominal GDP, calculated confidence intervals around the FOMC's median forecasts, and three separate forecasts of the Fed's perceived appropriate Fed funds rate.Finally, the author views the “optimal solution” as a more systematic approach in which the Fed publishes a single forecast based on a model consistent with its dual mandate that shows how the appropriate Fed funds rate path would be expected to vary under different economic and inflation outcomes. Such an approach, by thus mapping likely monetary policy responses to alternative plausible economic and inflation outcomes, would increase the Fed's accountability as well as its transparency.

Costs and potential funding of expanded public pension coverage in Asia

Public pension burdens in most emerging Asian economies are still relatively small. However, there are a number of reasons to believe that they will increase markedly in the coming years. First, many Asian economies will face rapidly ageing populations, which will raise pension and other old‐age‐related spending dramatically. Second, as economies develop, political pressures to expand the coverage of public pensions and raise pension benefits will likely increase. The first objective of this paper is to identify the potential fiscal burden of public pensions in 23 emerging Asian economies, based on econometric models and forecasts of GDP and demographic trends. Using two different methodologies yields estimated increases in the average share of public pension expenditures in GDP of 1.0 percentage point and 3.6 percentage points by 2030 compared with current levels. We believe the latter estimate is more realistic. The second objective is to recommend policies to provide adequate funding for public pension needs, including enhancing the efficiency of social insurance programs, improving the balance of revenues and expenditures, implementing more explicit fiscal rules and frameworks, and establishing stronger fiscal surveillance at the national and regional levels.

Weighted‐Covariance Factor Decomposition of Varma Models Applied to Forecasting Quarterly U.S. Real GDP at Monthly Intervals

Suppose a vector autoregressive moving‐average model is estimated for m observed variables of primary interest for an application and n–m observed secondary variables to aid in the application. An application indicates the variables of primary interest but usually only broadly suggests secondary variables that may or may not be useful. Often, one has many potential secondary variables to choose from but is unsure which ones to include in or exclude from the application. The article proposes a method called weighted‐covariance factor decomposition (WCFD), comparable to Stock and Watson's method here called principle‐components factor decomposition (PCFD), for reducing the secondary variables to fewer factors to obtain a parsimonious estimated model that is more effective in an application. The WCFD method is illustrated in the article by forecasting quarterly observed U.S. real GDP at monthly intervals using monthly observed four coincident and eight leading indicators from the Conference Board ( http://www.conference‐board.org). The results show that root mean‐squared errors of GDP forecasts of PCFD‐factor models are 0.9–11.3% higher than those of WCFD‐factor models especially as estimation‐forecasting periods pass from the pre‐2007 Great Moderation through the 2007–2009 Great Recession to the 2009–2016 Slow Recovery.