Leading Indicator Variables Research Articles

A Google–Wikipedia–Twitter Model as a Leading Indicator of the Numbers of Coronavirus Deaths

SummaryForecasting the number of cases and the number of deaths in a pandemic provides critical information to governments and health officials, as seen in the management of the coronavirus outbreak. But things change. Thus, there is a constant search for real‐time and leading indicator variables that can provide insights into disease propagation models. Researchers have found that information about social media and search engine use can provide insights into the diffusion of flu and other diseases. Consistent with this finding, we found that a model with the number of Google searches, Twitter tweets, and Wikipedia page views provides a leading indicator model of the number of people in the USA who will become infected and die from the coronavirus. Although we focus on the current coronavirus pandemic, other recent viruses have threatened pandemics (e.g. severe acute respiratory syndrome). Since future and existing diseases are likely to follow a similar search for information, our insights may prove fruitful in dealing with the coronavirus and other such diseases, particularly in the early phases of the disease. Subject terms: coronavirus, COVID‐19, unintentional crowd, Google searches, Wikipedia page views, Twitter tweets, models of disease diffusion.

Leading indicator variables and managerial incentives in a dynamic agency setting

This paper studies, in a dynamic agency setting, how incentives and contractual efficiency are affected by leading indicators of firms’ future financial performance. In our two-period model, a leading indicator variable provides a noisy forecast of the uncertain return from the manager’s long-term effort, and both contracting parties cannot refrain from renegotiating contract terms based on updated information. We find that the leading indicator can reduce the manager’s long-term effort incentive, as it allows the firm owner to capture more of the resulting return through renegotiated wages (i.e., the manager is held up). By reducing the uncertainty about future aggregate cash flows, the leading indicator also exacerbates the “ratchet” effect and discourages the manager’s short-term effort. In equilibrium, as the leading indicator becomes more accurate in forecasting future cash flows, the first-period contract attaches higher explicit weights to both the forward-looking leading indicator and backward-looking cash flow, and yet the manager may find it optimal to reduce both the short- and long-term efforts. We further show that with a more accurate leading indicator variable, the explicit incentive on the lagging cash flow may increase more than that on the leading indicator, and the equilibrium firm profit may decrease and diverge from the manager’s equilibrium efforts.

What predicts US recessions?

We reassess the in- and out-of-sample predictability of US recessions at horizons of three months to two years ahead for a large number of previously proposed leading indicator variables, using the Treasury term spread as a benchmark. We estimate both univariate and multivariate probit models, and evaluate the relative model performance based on the receiver operating characteristic (ROC) curve. At the three- and six-month-ahead horizons, various alternative predictor variables increase the accuracy of recession forecasts significantly relative to the term spread, with the annual return on the S&P500 index providing the strongest improvement. While the Treasury term spread is more difficult to outperform systematically at longer horizons, manufacturers’ new orders of capital goods and balances in Broker-Dealer margin accounts increase the precision of recession predictions significantly at horizons of more than one year.

Leading Indicator Variables and Managerial Incentives in a Dynamic Agency Setting

This paper studies, in a dynamic agency setting, how incentives and welfare are affected by nonfinancial measures that are leading indicators of the firms' future financial performance. In our two-period model, a leading indicator variable provides a noisy forecast of the uncertain future return from the manager's long-term effort, and both contracting parties cannot refrain from renegotiating contract terms based on updated information. We find that the leading indicator variable can holdup the manager's long-term effort, as it facilitates the firm owner's appropriation of the manager's expected bonus pay. By reducing the uncertainty about future cash flows, the leading indicator variable also aggravates the ratchet effect and discourages the manager's short-term effort. In equilibrium, as the leading indicator variable becomes more accurate in predicting future cash flow, the first-period contract attaches higher explicit weights to both the forward-looking leading indicator variable and backward-looking cash flow, and yet the manager may find it optimal to reduce both the short- and long-term efforts. Consequently, there are many plausible situations where the expected firm profit decreases in the presence of a leading indicator variable, even in the absence of any cost associated with measuring and monitoring this forward-looking performance signal.

On the Predictive Content of Leading Indicators: The Case of U.S. Real Estate Markets

In this paper, we employ a probit model and a Markov-switching model using information from the Conference Board Leading Indicator and other predictor variables to forecast the signs of future rental growth in four key U.S. commercial rent series. We find that both approaches have considerable power and can be used to predict changes in the direction of commercial rents up to two years ahead, exhibiting strong improvements over a näive model, especially for the warehouse and apartment sectors. We find that while the Markov-switching model appears to be more successful, it lags behind actual turnarounds in market outcomes, whereas the probit is able to detect whether rental growth will be positive or negative several quarters ahead.

Forecasting with Leading Indicators by means of the Principal Covariate Index

A new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of individual leading indicator variables with the objective of forecasting. In an empirical application to forecast future growth rates of the Conference Board’s Composite Coincident Index and its constituents, the forecasts of the principal covariate index are more accurate than those obtained either from the Composite Leading Index of the Conference Board or from an alternative index-based on principal components. JEL Classification: C32, C53, E27 Keywords: index construction, business cycles, principal component, principal covariate, time series forecasting, variable selection

Real-time macroeconomic forecasting with leading indicators: An empirical comparison

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989–2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.

Leading Indicator Variables, Performance Measurement, and Long‐Term Versus Short‐Term Contracts

ABSTRACTIn this article we develop a multiperiod agency model to study the role of leading indicator variables in managerial performance measures. In addition to the familiar moral hazard problem, the principal faces the task of motivating a manager to undertake “soft” investments. These investments are not directly contractible, but the principal can instead rely on leading indicator variables that provide a noisy forecast of the investment returns to be received in future periods. Our analysis relates the role of leading indicator variables to the duration of the manager's incentive contract. With short‐term contracts, leading indicator variables are essential in mitigating a holdup problem resulting from the fact that investments are sunk at the end of the first period. With long‐term contracts, leading indicator variables will be valuable if the manager's compensation schemes are not stationary over time. The leading indicator variables then become an instrument for matching the future investment return with the current investment expenditure. We identify conditions under which the optimal long‐term contract induces larger investments and less reliance on the leading indicator variables as compared with short‐term contracts. Under certain conditions, though, the principal does better with a sequence of one‐period contracts than with a long‐term contract.

Leading Indicator Variables, Performance Measurement and Long-Term versus Short-Term Contracts

This paper develops a multiperiod agency model to study the use of leading indicator variables in managerial performance measures. In addition to the familiar moral hazard problem, the principal faces the task of motivating a manager to undertake soft investments. These investments are not directly contractible, but the principal can instead rely on leading indicator variables which provide a noisy forecast of the investment returns to be received in future periods. Our analysis relates the role of leading indicator variables to the duration of the manager's incentive contract. With short-term contracts, leading indicator variables are essential in mitigating a holdup problem resulting from the fact that investments are sunk at the end of the first period. With long-term contracts, leading indicator variables will be valuable if the manager's compensation schemes are not stationary over time. The leading indicator variables then become an instrument for matching the future investment return with the current investment expenditure. We identify conditions under which the optimal long-term contract induces larger investments and less reliance on the leading indicator variables in comparison to short-term contracts. Under certain conditions, though, the principal does better with a sequence of one-period contracts than with a long-term contract.

Forecasting UK industrial production over the business cycle

AbstractThis paper examines the information available through leading indicators for modelling and forecasting the UK quarterly index of production. Both linear and non‐linear specifications are examined, with the latter being of the Markov‐switching type as used in many recent business cycle applications. The Markov‐switching models perform relatively poorly in forecasting the 1990s production recession, but a three‐indicator linear specification does well. The leading indicator variables in this latter model include a short‐term interest rate, the stock market dividend yield and the optimism balance from the quarterly CBI survey. Copyright © 2001 John Wiley & Sons, Ltd.

Predicting UK Business Cycle Regimes

This paper uses logistic regression to construct a one‐quarter ahead prediction model for classical business cycle regimes in the UK. The binary dependent variable is obtained by applying simple mechanical rules to date turning points in quarterly real GDP data from 1963 to 1999. Using a range of real and financial leading indicators, several parsimonious one‐quarter‐ahead models are developed for the GDP regimes, with model selection based on the SIC criterion. A real M4 variable is consistently found to have predictive content. One model that performs well combines this with nominal UK and German short‐term interest rates. The role of the latter emphasises the open nature of the UK economy.

Modelling Business Cycle Movements in the UK Economy

This paper models the phases of the UK business cycle using GDP data with a time‐varying transition probabilities (TVTP) Markov‐switching regime model and exogenous leading indicator variables. Single indicators in linear models are compared with the TVTP framework, with logistic and exponential functions used in the latter. The Markov‐switching models capture the major recessions of the sample, but the use of leading indicators through the TVTP framework can improve this regime recognition. Finally, a forecast comparison shows that the TVTP models perform relatively well in predicting during the 1990s, particularly when nominal interest rates are used to generate the regime‐switching probabilities.

Predicting U.S. Business-Cycle Regimes

This article examines the use of logistic classification methods for the identification and prediction of postwar U.S. business-cycle expansion and contraction regimes as defined by the National Bureau of Economic Research (NBER) reference turning-point dates. We present a coherent theoretical framework for this task using measures of discriminatory information. The analysis encompasses model selection, parameter estimation, and classification decision rules. We examine the performance of logistic procedures in reproducing the NBER regime classifications and in predicting one and three months ahead using leading-indicator variables. Our models are shown to provide substantially more accurate business-cycle regime predictions than Markov switching specifications.

Forecasting international growth rates using Bayesian shrinkage and other procedures

Forecasts of eighteen countries' annual output growth rates, 1974–1984, are computed using an autoregressive model containing leading indicator variables and various forecasting procedures. It is found that Bayesian shrinkage forecasting techniques produced improved forecasts in terms of an out-of-sample root-mean-square criterion relative to those provided by three naive models and by autoregressive models with and without leading indicator variables. The precision of our forecasts compares favorably with that of OECD forecasts derived from complicated econometric ‘structural’ macroeconometric models and subjected to judgmental adjustments. Work to rationalize our forecasting models in terms of macroeconomic theory is in progress.