We analyze the effect of green patents on G7 stock market returns. First, we build a small IS-LM model to identify the relevant channels, augmented with open-economy channels and the Green Tobin’s q (Faria et al., 2022). The model highlights that the intertemporal impacts of greening on stock returns are ambiguous. We then turn to an estimated global vector autoregressive model to more rigorously analyze the effect of monetary and green patenting shocks across the G7. Both shocks influence green patents through real and financial markets. As regards green patent shocks, results suggest that a tension exists over time between promoting pollution reduction and energy efficiency and the profitability of (green and brown) companies in the aggregate. We perform a variety of robustness exercises around our main results. Our results provide something of a challenge to the literature and call for more research effort to understand the various channels that might explain this dynamic—and in turn whether any particular policy recommendations follow.

Forecasts play a central role in decision-making under uncertainty. After a brief review of the general issues, this article considers ways of using high-dimensional data in forecasting. We consider selecting variables from a known active set, known knowns, using Lasso and One Covariate at a time Multiple Testing, and approximating unobserved latent factors, known unknowns, by various means. This combines both sparse and dense approaches to forecasting. We demonstrate the various issues involved in variable selection in a high-dimensional setting with an application to forecasting UK inflation at different horizons over the period 2020q1–2023q1. This application shows both the power of parsimonious models and the importance of allowing for global variables.

This paper assesses the information content and predictive capabilities of Divisia monetary indicators concerning sector-specific economic activities. Although existing evidence strongly supports the informative nature and predictive potential of various Divisia indicators at an aggregate level, studies focusing on Divisia information content for specific industries are notably sparse. Sector-level data provide a more detailed insight into economic and labor market dynamics. By analyzing comprehensive sector-specific data on real GDP, value added, employment, and unemployment rates across thirteen diverse sectors in the United States, this paper investigates the predictive abilities of narrow and broad Divisia money across three categories (original, credit card-augmented, and credit card-augmented inside money). The results show that narrow Divisia money serve as robust predictors of sector-specific economic and labor market indicators, often surpassing the predictive capacity of the conventional Fed funds rate and slightly outperforming broad Divisia measures in relation to these indicators.

Can we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices.

Longevity risk is putting more and more financial pressure on governments and pension plans worldwide due to pensioners’ increasing trend of life expectancy and the growing numbers of people reaching retirement age. Lee and Carter (1992, Journal of the American Statistical Association, 87(419), 659–671.) applied a one-factor dynamic factor model to forecast the trend of mortality improvement, and the model has since become the field’s workhorse. It is, however, well known that their model is subject to the limitation of overlooking cross-dependence between different age groups. We introduce Factor-Augmented Vector Autoregressive (FAVAR) models to the mortality modelling literature. The model, obtained by adding an unobserved factor process to a Vector Autoregressive (VAR) process, nests VAR and Lee–Carter models as special cases and inherits both frameworks’ advantages. A Bayesian estimation approach, adapted from the Minnesota prior, is proposed. The empirical application to the US and French mortality data demonstrates our proposed method’s efficacy in both in-sample and out-of-sample performance.

Expectations are among the main driving forces for economic dynamics. Therefore, managing expectations has become a primary objective for monetary policy seeking to stabilize the business cycle. In this paper, we study whether central banks can manage private-sector expectations by means of publishing one-period ahead inflation projections in a New Keynesian learning-to-forecast experiment. Subjects in the experiment observe these projections along with the historic development of the economy and subsequently submit their own one-period ahead inflation forecasts. In this context, we find that the central bank can significantly manage private-sector expectations and that this management strongly supports monetary policy in stabilizing the economy. Moreover, published central bank inflation projections drastically reduce the probability of a deflationary spiral after strong negative shocks to the economy.

Based on evidence gathered from a newly built large macroeconomic dataset (MD) for the UK, labelled UK-MD and comparable to similar datasets for the United States and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.

Nowcasting, that is, forecasting the current economic conditions, is a key ingredient for decision making, but it is complex, even more so for a small open economy, due to the higher volatility of its GDP. In this paper, we review the required steps, taking Luxembourg as an example. We consider both standard and alternative indicators, used as inputs in several nowcasting methods, including various factor and machine learning models. Overall, mixed frequency dynamic factor models and neural networks perform well, both in absolute terms and in relative terms with respect to a benchmark autoregressive model. The gains are larger during problematic times, such as the financial crisis and the recent Covid period.

The UK faces a number of economic challenges in the short to medium term. Prior to COVID-19, renegotiation of trading arrangements with the European Union was the most prominent of these. We build on existing macroeconomic analysis by assessing prospects for the UK’s regions generated by combining a global macroeconometric model and a regional computable general equilibrium of the UK. A central macroeconomic scenario shows a national average annual GDP growth rate of 1.7 per cent to 2044. When the macroeconomic scenario is applied across regions, growth rates range from 1.6 per cent for Cambridge to 2.2 per cent for Pembrokeshire; the standard deviation is low at 0.07 per cent and the coefficient of variation is 0.04 per cent. In contrast, much wider variation is observed in the standard deviation for exports (0.36 per cent), investment (0.11 per cent) and consumption (0.14 per cent). The country results favour Scotland, which grows at an annual rate of 1.8 per cent, whereas Wales is the slowest growing of the countries at 1.7 per cent. Consistent with the macroeconomic analysis, international trade is the most important contributor to the regional variation in growth rates. We also analyse the effects of higher government consumption relative to the forecasts and find most regions are predicted to experience lower economic activity except the handful in which government consumption is a much higher share of GDP than average.

I assess a novel rule that was introduced in the UK in 2015. It gave the British government fiscal flexibility whenever GDP growth warranted it. This rule lasted just a year, but it had features worth exploring. I apply solution methods for models with occasionally-binding constraints to assess the demand stabilisation properties of state-contingent fiscal rules. First it is shown that fiscal flexibility can make recessions shallower. Second, it is suggested that GDP growth, rather than measures of the output gap, is a better indicator for triggering fiscal flexibility.

This paper charts the evolution of mainly empirical research at the NIESR over the 1970s and 80s. As was all too evident there were very large discrete technical improvements in data handling and manipulation over this period. Less well appreciated were the effects on the economy of major supply-side shocks coming from the World economy leading to ‘Stagflation’ and, interconnected but somewhat later, in the UK, marked changes in macro policy regime. This latter was strongly influenced by the seminal papers of Lucas (1976) and Sims (1980); both highly critical of the then current practices in macroeconomics, though each having very different intellectual stances. The response in the NIESR was to engage at an early stage in these innovations; applying the mantra that an informed criticism was more efficient than an uninformed one. During this period it became a leader in the econometrics of applied macro modelling under different expectations assumptions, including the rational expectations hypothesis (REH). Throughout, it remained critical of the anti-empirical drift encouraged in the Lucas Critique, criticism borne out more recently by the financial instability of the 1990s and the crisis that followed.

The Institute is a world leader in macroeconomic modelling and forecasting. It has produced quarterly economic forecasts for around sixty years, supported by macroeconomic models. The aim of the original builders of macroeconomic models was to transform understanding of how economies worked and use that knowledge to improve economic policy. In the early years, when computers were rare, macroeconomic modelling was a new frontier and Institute economists were among the first to produce a working model of the UK economy. It is remarkable how quickly models were being used to produce forecasts, assess policy and influence the international macroeconomic research agenda. The models built at the Institute were mainstream in the sense that they followed the contents of standard macroeconomic textbooks, developed with the subject, and fitted the facts as they were known at the time. There were continual improvements in understanding as the subject developed in response to new ideas and developments in the global economy. This article celebrates the development of macroeconomic modelling at the Institute and the contribution it has made to public life.

We modify NiGEM in order to study the macroeconomic effects of imposing import tariffs in the US under different assumptions regarding the long-run price setting behaviour of exporters. Overall, the macroeconomic implications in the US resemble the impact of a cost shock or adverse supply shock as prices increase while output declines. Due to exchange rate movements and changes in the prices of traded goods, prices and output in other economies tend to move in the same direction. We demonstrate that the size and persistence of the macroeconomic impact following the introduction of new tariffs critically hinge upon the specific assumptions underlying the behaviour of export prices. If foreign exporters are concerned about their net-of-tariff prices, there will be little adjustment after the initial surge in tariff-inclusive export prices. As a result, the adverse macroeconomic impact will be large and persistent both in the US and abroad. While additional government spending financed by tariff revenues could mitigate the adverse impact on the protectionist economy in the short run, retaliation by its trading partners would worsen the outcome. Our simulations also raise doubts about the ability of protectionist measures to rein in global imbalances.

This paper introduces a special issue of the Review on how the National Institute Global Econometric Model (NiGEM) is being used to navigate uncertain times. NiGEM is the leading global macroeconomic model, used by both policy-makers and the private sector across the globe for economic forecasting, scenario building and stress testing. The paper summarises the main features of NiGEM and describes some standard model simulations to illustrate how the model responds to monetary, fiscal and technology shocks.

For a large number of countries, we augment the specification of import demand in NiGEM by accounting for different import contents of individual expenditure components. Three examples illustrate that these changes have important implications for model outcomes. In the case of the recently enacted US tax reform and in a ‘hard landing’ scenario for China, spillovers become more pronounced while the domestic effects turn out more muted than in the default specification. The reverse is true for the ramifications of a public investment push in Germany. Hence, we believe that our add-on to NiGEM can be a useful tool for robustness exercises, in particular for spillover studies.

Increased environmental regulations and a milk quota that restricts growth have increased the interest in immigration to the United States by Dutch dairy farmers. A risk-based economic analysis of 23 representative U.S. dairy farms versus a representative Dutch farm shows that risk-averse Dutch dairy farmers would prefer to liquidate their dairy farms and invest in a large dairy in Idaho or north Texas. The risk ranking suggested that continuing to farm in the Netherlands rather than immigrating to the United States is preferred over only two of the 23 U.S. representative farms analyzed.

This study sought to determine whether monthly revisions of U.S. Department of Agriculture current-year corn and soybean yield forecasts are correlated and whether this correlation is associated with crop size. An ex-ante measure of crop size based on percent deviation of the current estimate from out-of-sample trend is used in efficiency tests based on the Nordhaus framework for fixed-event forecasts. Results show that available information about crop size is generally efficiently incorporated in these forecasts. Thus, although this pattern may appear obvious to market analysts in hindsight, it is largely based on new information and hence difficult to anticipate.

This paper lays out the various reasons for the exceptional performance of the German labour market during and after the Great Recession of 2008/9. The reference point of our analysis is provided by an empirical model of both total hours worked and employment. We conduct dynamic simulations of the crisis period to assess how surprising the reaction of the labour market really was. We argue that the most important precondition for the minor reaction of employment during this crisis was the pronounced wage moderation observed in the years before, which constitutes a distinct difference to all other recessions in Germany. Beyond that, the flexibility of adjusting working time, which has increased considerably during the past ten years, facilitated a tendency to labour hoarding. In contrast, short-time work plays a minor role in explaining the difference from previous recessions, since this instrument has always been available to firms in Germany and its use has not been extraordinary compared with earlier recessions.

Timely availability of relevant economic information is from the users' point of view a crucial pre-requirement for effective economic and monetary policy and for accurate analysis and reliable forecasting. Unfortunately, official statistics are not always able to meet such needs. For this reason, particularly over the recent past, advanced statistical and econometric techniques have been used to complement the classical production process in order to increase the timeliness of the relevant economic information. Furthermore, official statistics do not necessarily show explicitly relevant phenomena such as the cyclical situation and the location of turning points. In order to fill this gap, indicators have to be built up to extract such relevant signals and to make them available in an easily readable and comprehensible way to all users. This paper gives a general overview of ongoing Eurostat projects, aiming to supply timely information to all users based on transparent and statistically sound methodologies. In particular this paper presents results related to the construction of flash estimates in order to increase the timeliness of official statistics as well as the construction of a timely statistical system for business cycle analysis based on a set of turning points indicators and cyclical estimates. The results obtained so far are very encouraging, especially in terms of the overall quality of the estimates and the statistical robustness of the proposed methods. Nevertheless, some improvements, particularly related to the precision of flash estimates, are needed, especially under an uncertain economic situation and regime changes.

For more than four decades, the New Zealand Institute of Economic Research (NZIER) has conducted a two-question, quarterly survey of architect forecasts of public and private sector construction expenditure. This qualitative survey is published one week after the end of each quarter and nine weeks ahead of the official quantitative data thereby giving architect opinion nowcasting status. This paper covers selected aspects of this unexplored series with particular reference to residential housing construction and the value-added information from architects as nowcasters. Specifically, we consider several qualitative-to-quantitative conversion methods, in-sample and out-of-sample performance, cyclical features and respondent dynamics. Although our work relates to architects — a sub-sector of the service industry — our results have a wider application to business survey questions using ordered qualitative responses.