Long-run Forecasts Research Articles

How do nuclear energy consumption, environmental taxes, and trade globalization impact ecological footprints? Novel policy insight from nuclear power countries

Energy systems should grab the opportunity to adopt clean energy sources and technologies to mitigate environmental impacts. This work explores the influence of nuclear energy consumption on reducing the ecological footprint, considering environmental taxes and trade globalization in five nuclear-powered economies from 1990 to 2021. This study employed several econometric models, including the preliminary analysis, the CS-ARDL test for long-run forecasting and the Dumitrescu-Hurlin causality test. The long-run findings revealed that nuclear energy usage and environmental taxes support reducing environmental costs by lowering the ecological footprint. However, trade globalization raises environmental costs by increasing the ecological footprint. Additionally, causality test outcomes show that a one-way causal link exists among nuclear energy and the ecological footprint. The results highlight government actions prioritizing investment in nuclear energy consumption projects and encouraging nuclear energy efficiency to mitigate ecological degradation and lead to a low-carbon future. This viewpoint also encourages policymakers to make well-informed decisions to uphold ecological sustainability.

Forecasting Inflation Using Economic Narratives

We use economic narratives to forecast inflation with a large news corpus and machine learning algorithms. The economic narratives from the full text content of over 880,000 Wall Street Journal articles are decomposed into multiple time series representing interpretable news topics, which are then used to predict inflation. The results indicate that narrative-based forecasts are more accurate than the benchmarks, especially during recession periods. Narrative-based forecasts perform better in long-run forecasting and provide incremental predictive information even after controlling macroeconomic big data. In particular, information about inflation expectations and prices of specific goods embedded in narratives contributes to their predictive power. Overall, we provide a novel representation of economic narratives and document the important role of economic narratives in inflation forecasting.

Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)

Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)

Tracking Inattention

Abstract This study proposes a real-time estimate of inattention, based on micro-level data. I show that a simple specification that estimates the persistence of a forecaster's deviation from the mean provides a direct estimate of parameters of information frictions according to prominent models of expectations. The new estimate can also be interpreted as a hybrid measure of both information frictions and behavioral frictions. Using the new specification, I revise several key findings documented in the previous literature. I find higher levels of inattention and document new forms of variations over time and across variables, horizons, individuals, and types of agents. I also report new results from long-run forecasts and document an unprecedented response to COVID-19.

Score-driven threshold ice-age models: Benchmark models for long-run climate forecasts

Climate variables are known to be subject to abrupt changes when some threshold levels are surpassed. We use data for the last 798,000 years on global ice volume (Ice), atmospheric carbon dioxide level (CO2), and Antarctic land surface temperature (Temp) to model and measure those long-run nonlinear climate effects. The climate variables have very long and asymmetric cycles, created by periods of upward trends, followed by periods of downward trends driven by exogenous orbital variables. The exogenous orbital variables considered by the Milankovitch cycles are eccentricity of Earth’s orbit, obliquity, and precession of the equinox. We show that our new score-driven threshold ice-age models improve the statistical inference and forecasting performance of competing ice-age models from the literature. The drawback of using our 1000-year frequency observations, is that we cannot measure the nonlinear climate effects of humanity created during the last 250 years, which are known to have generated abrupt structural changes in the Earth’s climate, due to unprecedented high levels of CO2 and Temp, and low levels of Ice volume. On the other hand, the advantage of using low-frequency data is that they allow us to obtain long-run forecasts on what would have occurred if humanity had not burned fossil fuels since the start of the Industrial Revolution. These long-run forecasts can serve as benchmarks for the long-run evaluation of the impact of humanity on climate variables. Without the impact of humanity on climate, we predict the existence of turning points in the evolution of the three climate variables for the next 5,000 years: an upward trend in global ice volume, and downward trends in atmospheric CO2 level and Antarctic land surface temperature.

Meeting the European green deal objective of expanding organic farming

Research background: Economic growth is unsustainable. However, a circular economy has the potential to lead to sustainable development, while decoupling economic growth from the negative consequences of resource depletion and environmental degradation. The EU's strategy of climate neutralization in 2050 developed, inter alia, into a European Green Deal action plan aiming at the efficient use of resources by moving to a cleaner, circular economy. More sustainable EU food system is a cornerstone of the European Green Deal. The European Commission's goal is 25% of agricultural land to be used for organic production in 2030. The question is if it is possible to reach the objective with the use of current incentives. What else may be done to encourage European farmers to convert to organic farming? Purpose of the article: The aim of this research is to review the development of organic agriculture in Europe and the EU and to identify incentives for farmers to convert to organic farming. Methods: First of all, the methodological approach is to iteratively review the existing literature to frame the problem. Secondly, the data on organic agriculture in Europe is to be analyzed to answer the research questions. The analysis is based on international statistics, mainly collected by FiBL, IFOAM, EC Agri-food data portal and Eurostat..Fitting the trend functions to the actual data has been made in three scenarios (pessimistic, realistic and optimistic). These trend functions were used for the long-term forecasts of the share of organic farmland in the EU. Findings & value added: The long-run forecast might be treated as a goal, which can motivate to act more intensively to achieve the objective. The existing measures, including organic farming payments, are not sufficient to meet the goal of massive increase in the acreage under organic production. It is necessary to develop new incentives e.g. Green Public Procurement, innovative and effective media campaigns, development of a dynamic network of actors within the organic food supply chain with the use of blockchain technology.

Estimating Structural Shocks with the GVAR-DSGE Model: Pre- and Post-Pandemic

This paper investigates the possibility of using the global VAR (GVAR) model to estimate a simple New Keynesian DSGE-type multi-country model. The long-run forecasts from an estimated GVAR model were used to calculate the steady-states of macro variables as differences. The deviations from the long-run forecasts were taken as the deviation from the steady-states and were used to estimate a simple NK open economy model with an IS curve, Philips curve, Taylor rule, and an exchange rate equation. The shocks to these equations were taken as the demand shock, supply shock, monetary shock, and exchange rate shock, respectively. An alternative model was constructed to compare the results from GVAR long-run forecasts. The alternative model used a Hodrick–Prescott (HP) filter to derive deviations from the steady-states. The impulsive response functions from the shocks were then compared to results from other DSGE models in the literature. Both GVAR and HP estimates produced dissimilar results, although the GVAR managed to capture more from the data, given the explicit co-integration relationships. For the IRFs, both GVAR and HP estimated DSGE models appeared to be as expected before the pandemic; however, if we include the pandemic data, i.e., 2020, the IRFs are very different, due to the nature of the policy actions. In general, DSGE–GVAR models appear to be much more versatile, and are able to capture dynamics that HP filters are not.

MACRO FACTOR, MARKET VOLATILITY, AND STOCK-BOND CORRELATION: A DYNAMIC MIXED DATA SAMPLING FORECAST

This paper explores how macroeconomic fundamentals affect the long-run dynamics in the volatility of and correlation between the stock and bond market. We use China as a laboratory by employing the GARCH-MIDAS and DCC-MIDAS models, which possess proven ability to capture the long-run component of second-moment market performance in a rapidly-growing economy. With actual data and predictivity evaluation, the finding is that the industrial production growth rate accounts for 32.04% of variations in the total conditional volatility for Chinese stocks, and the industrial production growth volatility drives 29.63% of movements in the total conditional volatility for Chinese bonds. Our proposed model is particularly advantageous in long-run volatility forecasts. Moreover, the present study complements the literature concerning the impacts of macro factors on the time-varying correlations between China’s stock and bond markets. We find weak evidence in this regard, probably due to the absence of multi-market macro-strategy investors that prevail in more developed markets.

Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)

Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)

Malaysia's economic growth, consumption of oil, industry and CO2 emissions: evidence from the ARDL model

The leading cause of global climate change and warming is greenhouse gas emissions. In developing economic activities, energy plays an important role, and human activities are responsible for climate change, carbon dioxide (CO2) emissions adverse impacts on the environment. This study investigates carbon dioxide emissions from liquid fuel consumption on CO2 emissions rather than total energy consumption. Carbon dioxide (CO2) emissions have a causal relationship of CO2 emissions from liquid fuel consumption (CO2L), industry (IND), economic growth (GDP), and trade openness (TR). The ARDL bound method incorporates the structure break and Granger causality method to measure the long-run and short-run cointegration relationship between the variables based on annual data from 1971 to 2016 Malaysia. Both the augmented Dickey-Fuller and Phillips-Perron analysis also supported exploring the study variables' stationarity. The long-run forecasts indicate that CO2L, IND, TR, and GDP are significantly related to CO2 emissions. The error correction term (ECT) value was -0.952, revealing that CO2 emissions diverged from a short-run to long-run equilibrium by 95.2% each year. Furthermore, the Granger causality test shows bidirectional causality between trade openness and CO2L. A unidirectional causality runs from the trade openness to industry at a 1% level form. The current study aims to find out the relationship between carbon dioxide emissions, economic growth, energy consumption, and trade in Malaysia to fill this scientific gap. Based on this study's results, the government needs effective policies and initiatives to identify Malaysia's ecological issues. It is also essential to determine the basic target of Malaysia's consumption of liquid fuel and its environmental mitigation policies.

Naive Forecast for COVID-19 in Utah Based on the South Korea and Italy Models-the Fluctuation between Two Extremes.

Differences in jurisdictional public health actions have played a significant role in the relative success of local communities in combating and containing the COVID-19 pandemic. We forecast the possible COVID-19 outbreak in one US state (Utah) by applying empirical data from South Korea and Italy, two countries that implemented disparate public health actions. Forecasts were created by aligning the start of the pandemic in Utah with that in South Korea and Italy, getting a short-run forecast based on actual daily rates of spread, and long-run forecast by employing a log-logistic model with four parameters. Applying the South Korea model, the epidemic peak in Utah is 169 cases/day, with epidemic resolution by the end of May. Applying the Italy model, new cases are forecast to exceed 200/day by mid-April, with the potential for 250 new cases a day at the epidemic peak, with the epidemic continuing through the end of August. We identify a 3-month variation in the likely length of the pandemic, a 1.5-fold difference in the number of daily infections at outbreak peak, and a 3-fold difference in the expected cumulative cases when applying the experience of two developed countries in handling this virus to the Utah context.

Time-Varying Trend Models for Forecasting Inflation in Australia

We investigate whether a class of trend models with various error term structures can improve upon the forecast performance of commonly used time series models when forecasting CPI inflation in Australia. The main result is that trend models tend to provide more accurate point and density forecasts compared to conventional autoregressive and Phillips curve models. The best short-term forecasts come from a trend model with stochastic volatility in the transitory component, while medium to long-run forecasts are better made by specifying a moving average component. We also find that trend models can capture various dynamics in periods of significance which conventional models cannot. This includes the dramatic reduction in inflation when the RBA adopted inflation targeting, the one-off 10 per cent Goods and Services Tax inflationary episode in 2000, and the gradual decline in inflation since 2014.

COMPARING MULTIVARIATE MODELS’ FORECASTS OF INFLATION FOR BRICS AND OPEC COUNTRIES

Purpose – This study identifies the most appropriately selected multivariate model for forecasting inflation in different economic environments. In specifying the multivariate models, the study test for the orders of integration of variables and for those that are nonstationary. For non-stationary variables, this study examines whether they are cointegrated. Engle and Granger (1987) establish that a cointegrating equation can be represented as an error correction model that incorporates both changes and levels of variables such that all of the elements are stationary. However, VARs estimated with cointegrated data will be misspecified if all of the data are differenced because long-run information will be omitted, and will have omitted stationarity inducing constraints if all the data are used in levels. Further, including variables in both levels and differences should sat-isfy stationarity requirements. However, they will omit cointegrating restrictions that may improve the model. Of course, these constraints will be satisfied asymptotically; but efficiency gains and improved multi-step forecasts may be achieved by imposing the constraints (Engle and Granger 1987, p. 259). Therefore, this study test for order of integration and compare inflation forecasting performance of different multivariate models for BRICS and OPEC countries. Research methodology – The following approaches were considered; the first approach is to construct a VAR model in differences (stationary form) to forecast inflation. The second approach is to construct a VECM without imposing cointegrating restrictions. The third approach is to construct a VEC that imposes cointegrating restrictions on the VECM. This will help to understand whether imposing cointegrating restrictions via a VEC improves long-run forecasts. Research limitation – The proposed multivariate models focused on differencing and cointegrating restrictions to ensure the stationarity of the data, the available variables were combined and specified based on their level of integration to forecast inflation. For instance, a VAR model is estimated based on differenced variables I(0); the same holds true for VECM and VEC models, where differenced variables and linear combinations of I(I) covariates are stationary. In future, multivariate models guided by economic theory rather than the order of integration of variables are suggested. Findings – The result shows that the forecast performance of inflation depends on the nature of the economy and whether the country experiencing higher inflation or low inflation. For instance, the model that includes long-run information in the form of a specified cointegrated equation generally improves the inflation forecasting performance for BRICS countries and one OPEC country (Saudi Arabia) that has a history of low inflation. Practical implications – This research will improve the policy makers decision on how to select appropriate model to forecast inflation over different economic environment. Originality/Value – These methods have not been used to forecast inflation for many emerging economies such as OPEC and BRICS countries despite the importance of many of these countries to the global economy. This study fills this gap by evaluating the forecasting performance of inflation using multivariate VAR and cointegrating models for OPEC and BRICS economies.

Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy

This paper constructs hybrid forecasts that combine forecasts from vector autoregressive (VAR) model(s) with both short- and long-term expectations from surveys. Specifically, we use the relative entropy to tilt one-step-ahead and long-horizon VAR forecasts to match the nowcasts and long-horizon forecasts from the Survey of Professional Forecasters. We consider a variety of VAR models, ranging from simple fixed-parameter to time-varying parameters. The results across models indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. Accuracy improvements are achieved for a range of variables, including those that are not tilted directly but are affected through spillover effects from tilted variables. The accuracy gains for hybrid inflation forecasts from simple VARs are substantial, statistically significant, and competitive to time-varying VARs, univariate benchmarks, and survey forecasts. We view our proposal as an indirect approach to accommodating structural change and moving end points.

Industrial Development Strategy and Stimulator Industry of Iran’s Economy

With a glance on planning methodology, we try to analyze “Iran’s industrial development strategy proposition”. This proposition to somehow covers the requirements of Iran’s economic development strategy, and in this regard can be treated as an economic strategy plan and not industrial strategy plan. Approaches of this proposition-ignoring its long-run quantitative forecasts- can be used as a general solution to Iran’s long-run structural problems and can be summarized by international linkages and competitiveness as the main proposed strategies. But this proposition does not introduce stimulator engine sector of the economy; in this paper, we try to touch this nomination via an input-output sectoral linkages framework. We conclude that, to start of Iran’s economic fast growth, sectors of glass and glassware, cement, mine, other mineral & non-ferrous products, basic steel and iron mill products, paper & wood industries, chemical & plastic industries, copper & copper products, water and electricity in turn should be activated more to fulfill the needs for intermediate products in the economy. The sectors of foodstuff industries, aluminum, and other basic products of non-ferrous metals, copper and copper products, steel and iron mill products are able to increase the demand for intermediate products of other sectors more than the others. Basic steel and steel mill products, copper and copper products, aluminum and other basic non-ferrous metals products, cement, paper, and wood industries, glass and glassware, other mineral non-ferrous products, chemical industries, and plastic are more integrated than the others relating both forward and backward linkages. Machinery and equipment, basic products of steel and iron mill, chemical industries and plastic are mainly based on import and share of intermediate import to production of aluminum sectors and other basic products of non-ferrous metals, chemical industries, and plastic, basic steel and iron mill products, machinery and equipment industries is more than the other sectors. Aluminum and other basic products of non-ferrous metals, foodstuffs industries, basic products of steel and steel mill have a higher multiplier effect in increasing economy income, in other words, it is possible to consider them as pioneer sectors and economy stimulator engine. Aluminum and other basic products of non-ferrous metals, foodstuff industries, basic products of steel and iron mill have more affect the growth of other sectors. Sectors of chemical and plastic industries, agriculture, transportation services, warehouse keeping and communications, commercial services provide the highest amount of input for other sectors. We consider two scenarios for Iran’s industrial strategy as foodstuffs industries sector with export promotion, and the second, metals sectors (aluminum and other basic products of non-ferrous metals, basic products of steel and iron mill) with export promotion and import substitution.

Short Sellers and Long-Run Management Forecasts

We examine how short sellers affect long-run management forecasts using a natural experiment (Regulation SHO) that relaxes short-selling constraints on a group of randomly selected firms (referred to as pilot firms). We find that compared to other firms, the pilot firms issue more long-run good news forecasts but do not change the frequency of long-run bad news forecasts. The increase in good news forecasts is greater when the pilot firms have higher quality forecasts, more uncertainty about firm value, or higher manager equity incentives. Overall, these results and the results of additional analyses indicate that the reduction in short-selling constraints and the increase in short-selling threat induce managers to enhance disclosures through more long-run forecasts, primarily in the case of good news, to discourage short sellers.

Tracking Inattention

This study proposes a real-time estimate of inattention, based on micro-level data. I show that a simple specification that estimates the persistence of a forecaster’s deviation from the mean provides a direct estimate for parameters of information frictions according to prominent models of expectations. The new estimate can also be interpreted as a hybrid measure of both informational and behavioral frictions. Using the new specification, I revise several key findings documented in the previous literature. I find higher levels of inattention and document new forms of variations over time and across variables, horizons, individuals, and types of agents. I also report new results from long-run forecasts and document an unprecedented response to COVID-19.

Estimating the Equity Risk Premium Using Implied Cost of Capital

Research on the implied cost of capital (ICC) has found that the equity risk premium is approximately 3%, on average, much lower than estimates based on the mean of historical stock market returns. The validity of such ICC estimates, however, faces both theoretical and empirical challenges. The theoretical equivalence between ICC and expected return requires the stringent condition that the latter is a constant, which is inconsistent with theory and empirical evidence. The empirical estimation of ICC depends on short-run and long-run earnings forecasts, whose biases can severely limit the validity of the estimates. By employing a forecasting procedure that induces little bias in both short-run and long-run forecasts and taking into account the stochastic nature of expected returns, we show that the average market risk premium ranges between 3% and 6%. We also demonstrate that the results of prior research are affected by input errors that are partially self-canceling.

Uncertainty in forecasts of long-run economic growth

Forecasts of long-run economic growth are critical inputs into policy decisions being made today on the economy and the environment. Despite its importance, there is a sparse literature on long-run forecasts of economic growth and the uncertainty in such forecasts. This study presents comprehensive probabilistic long-run projections of global and regional per-capita economic growth rates, comparing estimates from an expert survey and a low-frequency econometric approach. Our primary results suggest a median 2010-2100 global growth rate in per-capita gross domestic product of 2.1% per year, with a standard deviation (SD) of 1.1 percentage points, indicating substantially higher uncertainty than is implied in existing forecasts. The larger range of growth rates implies a greater likelihood of extreme climate change outcomes than is currently assumed and has important implications for social insurance programs in the United States.

Understanding the Volatility of the Canadian Exchange Rate

In this Commentary, we document the nature of the Bank of Canada’s current monetary policy regime by focusing on the following questions: what are the historical determinants of the Canadian–US dollar nominal exchange rate, and can they be used in real-time forecasting applications? We find that the current real exchange rate is more useful than commodity prices for forecasting changes in the nominal exchange rate. In fact, short-run forecasts based on the real exchange rate are as good as random-walk forecasts according to which the future exchange rate is expected to be the same as today’s. Strikingly, medium- and long-run forecasts based on the real exchange rate are superior to random-walk forecasts. We argue that these findings reflect the fact Bank of Canada and the U.S. Federal Reserve System follow similar inflation-targeting regimes and neither actively manages exchange rates. A fundamental question is whether Canadian policymakers are satisfied with the current inflation targeting regime. A cost of the current regime is that it allows for very volatile nominal and real exchange rates. A benefit is that consumers and firms can avoid many of the changes in prices and wages that would be required if the nominal exchange rate did not adjust in a flexible manner. In this Commentary, we take no stand on the merits of the current regime. Instead, we highlight the tradeoffs that policymakers face. Evaluating the costs and benefits of those tradeoffs should play an important role in the process leading to the Bank of Canada’s next five-year agreement with the government.