Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation

This paper exploits cross-sectional variation at the level of U.S. counties to generate real-time forecasts for the 2020 U.S. presidential election. The forecasting models are trained on data covering the period 2000–2016, using high-dimensional variable selection techniques. Our county-based approach contrasts the literature that focuses on national and state level data but uses longer time periods to train their models. The paper reports forecasts of popular and electoral college vote outcomes and provides a detailed ex-post evaluation of the forecasts released in real time before the election. It is shown that all of these forecasts outperform autoregressive benchmarks. A pooled national model using One-Covariate-at-a-time-Multiple-Testing (OCMT) variable selection significantly outperformed all models in forecasting the U.S. mainland national vote share and electoral college outcomes (forecasting 236 electoral votes for the Republican party compared to 232 realized). This paper also shows that key determinants of voting outcomes at the county level include incumbency effects, unemployment, poverty, educational attainment, house price changes, and international competitiveness. The results are also supportive of myopic voting: economic fluctuations realized a few months before the election tend to be more powerful predictors of voting outcomes than their long-horizon analogs.     

Forecasting from others’ experience: Bayesian estimation of the generalized Bass model

We propose a Bayesian estimation procedure for the generalized Bass model that is used in product diffusion models. Our method forecasts product sales early based on previous similar markets; that is, we obtain pre-launch forecasts by analogy. We compare our forecasting proposal to traditional estimation approaches, and alternative new product diffusion specifications. We perform several simulation exercises, and use our method to forecast the sales of room air conditioners, BlackBerry handheld devices, and compressed natural gas. The results show that our Bayesian proposal provides better predictive performances than competing alternatives when little or no historical data are available, which is when sales projections are the most useful.     

Considering interrelationships in strategic decisions

Accounting-based valuation studies of US firms tend to support Ohlson's proposition that residual income and book value numbers have information content in explaining observed market values. But European evidence also suggests that the conservative/liberal orientation of accounting tradition can produce significant national differences in associations between accounting performance measures and stock prices - in earnings behaviour, coefficient values and parameter sensitivity. We address these issues from an equity valuation perspective using Swedish data to assess the additional information content of Ohlson's information dynamics and analysts' forecasts in relation to market valuations in a more conservative accounting environment than the US. The study compares the explanatory and predictive power of Ohlson's (1995) residual income model (RIV) with a linear information dynamics version (LIM) that specifies both residual income and non-accounting information as autoregressive processes. Both versions are applied with, and without, future performance expectations from non-accounting sources (analysts' forecasts). As with US evidence, we find that the inclusion of analysts' forecasts improves both (i) cross-sectional correlations with current prices for both RIV and LIM models and (ii) the predictive power of RIV models in relation to future annual cross-sectional stock returns. The contribution of linear information dynamics is significant but varies across approaches. We also find significant differences between Swedish and US firms in earnings behaviour and associations between accounting numbers and market equity prices.     

Equity-premium prediction: Attention is all you need

Predictions of stock returns are greatly improved relative to low-dimensional forecasting regressions when the forecasts are based on the estimated factor of large data sets, also known as the diffusion index (DI) model. However, when applied to text data, DI models do not perform well. This paper shows that by simply using text data in a DI model does not improve equity-premium forecasts over the naive historical-average model, but substantial gains are obtained when one selects the most predictive words before computing the factors and allows the dictionary to be updated over time.     

Analysts' utilization of historical earnings information

This study examines whether security analysts (in)efficiently utilize the information contained in past series of annual and quarterly earnings in producing earnings forecasts. To do so, it investigates whether equal-weighted combinations of security analysts' forecasts with forecasts from statistical models based on historical earnings are superior, both in terms of being a better surrogate for the market's expectations of earnings and of accuracy, to forecasts from either one of these two sources. The empirical findings indicate that, although analysts' forecasts are superior to forecasts from statistical models, performance can be improved—both in terms of accuracy and also of being a better surrogate for market earnings expectations—by combining analysts' forecasts with forecasts from statistical models based on past quarterly earnings. Improvements in proxying for market earnings expectations were obtained even when analysts' forecasts made in June of the forecast year were used in the combinations. An implication of these findings is that investors can improve their investment decisions by using an average of the mean analysts' forecasts and the forecast produced by a time-series model of quarterly earnings in their investment decisions.     

The performance of alternative forecasting methods for SETAR models

We compare a number of methods that have been proposed in the literature for obtaining h-step ahead minimum mean square error forecasts for self-exciting threshold autoregressive (SETAR) models. These forecasts are compared to those from an AR model. The comparison of forecasting methods is made using Monte Carlo simulation. The Monte-Carlo method of calculating SETAR forecasts is generally at least as good as that of the other methods we consider. An exception is when the disturbances in the SETAR model come from a highly asymmetric distribution, when a Bootstrap method is to be preferred.An empirical application calculates multi-period forecasts from a SETAR model of US gross national product using a number of the forecasting methods. We find that whether there are improvements in forecast performance relative to a linear AR model depends on the historical epoch we select, and whether forecasts are evaluated conditional on the regime the process was in at the time the forecast was made.     

基于局部网络效应的创新扩散分析

在考虑局部网络效应的基础上,采用微观扩散模型仿真分析创新扩散。研究表明,局部网络效应使得创新潜在采纳个体决策相互影响,从而导致这种采纳者之间微观交互模式所呈现的宏观社会网络结构影响创新扩散的速度和程度。即使某些创新收益非常高,也有可能因为创新潜在采纳个体之间交互模式的影响而导致其扩散最终失败。同时早期创新采纳个体一般具有社会联系比较广泛,自身采纳阈值较高,自身局部网络效应强度较小,更关注创新内在价值等创新特征。     

Combining forecasts to improve earnings per share prediction: An examination of electric utilities

The purpose of this study is to investigate the efficacy of combining forecasting models in order to improve earnings per share forecasts. The utility industry is used because regulation causes the accounting procedures of the firms to be more homogeneous than other industries. Three types of forecasting models which use historical data are compared to the forecasts of the Value Line Investment Survey. It is found that predictions of the analysts of Value Line are more accurate than the predictions of the models which use only historical data. However the study also shows that forecasts of earnings per share can be improved by combining the predictions of Value Line with the predictions of other models. Specifically, the forecast error is the least when the Value Line forecast is combined with the forecast of the Brown-Rozeff ARIMA model.     

OR vice: Forecasting and classification techniques for narcotics interdiction

Counternarcotics interdiction efforts have traditionally relied on historically determined sorting criteria or “best guess” to find and classify suspected smuggling traffic. We present a more quantitative approach which incorporates customized database applications, graphics software and statistical modeling techniques to develop forecasting and classification models. Preliminary results show that statistical methodology can improve interdiction rates and reduce forecast error. The idea of predictive modeling is thus gaining support in the counterdrug community. The problem is divided into sea, air and land forecasting, only part of which will be addressed here. The maritime problem is solved using multiple regression in lieu of multivariate time series. This model predicts illegal boat counts by behavior and geographic region. We developed support software to present the forecasts and to automate the process of performing periodic model updates. During the period, the model was in use at. Coast Guard Headquarters. Because of deterrence provided by improved intervention, the vessel seizure rate declined from 1 every 36 hours to 1 every 6 months. Due in part to the success of the sea model, the maritime movement of marijuana has ceased to be a major threat. The air problem is more complex, and required us to locally design data collection and display software. Intelligence analysts are using a customized relational database application with a map overlay to perform visual pattern recognition of smuggling routes. We are solving the modeling portion of the air problem using multiple regression for regional forecasts of traffic density, and discriminant analysis to develop tactical models that classify “good guys” and “bad guys”. The air models are still under development, but we discuss some modeling considerations and preliminary results. The land problem is even more difficult, and data collection is still in progress.     

Dynamic models of the labor force behavior of married women which can be estimated using limited amounts of past information

This paper examines the extent of the inadequacy of standard cross-sectional models of US labor force behavior and considers the abilities of alternative models to capture the observed continuity in the hours of work and earnings of individuals as well as in their employment histories. Both of the alternatives to the standard cross-sectional model considered in this study incorporate limited amounts of information about past work behavior that could easily be collected as part of a national population census. Using a population of 21 to 64 year old married working women taken from a 1969 through 1978 Michigan Panel Study of Income Dynamics, the variables included in the Z vector age: 1) age of the wife; 2) education of the wife; 3) state average hourly wage in manufacturing measured in 1967 dollars; and 4) unemployment rate for the state in which the wife lives. Results show that by using information about a women's hours of work and wage rate in the previous year, it may be feasible to improve on forecasts of a woman's employment and earnings behavior. For each model a separate estimate is made for wives aged 21 through 46, and for those aged 47 through 64. The dummy and difference models perform much better than the standard model, with the dummy model having the higher pseudo-chi-square statistic. These models show that systematic errors made in determining which individuals work, what they earn per hour, or how many hours they work, should result in prediction errors of the same sort year after year in the computation of annual earnings. These findings with respect to years of work and nonwork, years of part time versus full time work, and cumulative earnings over a 10 year period, confirm and extend Heckman's findings; thus, forecasting models of the work behavior of individuals should not be estimated using pure cross-sectional data. It would be important if researchers could identify what observable factors, if any, increase the likelihood that wives will alter their work behavior from what it has been in the immediate past, even if they are not able to fully understand or explain this previous work behavior.     

On the forecasting performance of a small-scale DSGE model

Dynamic stochastic general equilibrium (DSGE) models have recently become standard tools for policy analysis. Nevertheless, their forecasting properties have still barely been explored. In this article, we address this problem by examining the quality of forecasts of the key U.S. economic variables: the three-month Treasury bill yield, the GDP growth rate and GDP price index inflation, from a small-size DSGE model, trivariate vector autoregression (VAR) models and the Philadelphia Fed Survey of Professional Forecasters (SPF). The ex post forecast errors are evaluated on the basis of the data from the period 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists” to ensure that the data used in estimating the DSGE and VAR models was comparable to the information available to the SPF.Overall, the results are mixed. When comparing the root mean squared errors for some forecast horizons, it appears that the DSGE model outperforms the other methods in forecasting the GDP growth rate. However, this characteristic turned out to be statistically insignificant. Most of the SPF's forecasts of GDP price index inflation and the short-term interest rate are better than those from the DSGE and VAR models.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Forecasting economic and financial variables with global VARs

This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.     

Does Forecast Combination Improve Norges Bank Inflation Forecasts?*

We develop a system that provides model‐based forecasts for inflation in Norway. We recursively evaluate quasi out‐of‐sample forecasts from a large suite of models from 1999 to 2009. The performance of the models are then used to derive quasi real time weights that are used to combine the forecasts. Our results indicate that a combination forecast improves upon the point forecasts from individual models. Furthermore, a combination forecast outperforms Norges Bank's own point forecast for inflation. The beneficial results are obtained using a trimmed weighted average. Some degree of trimming is required for the combination forecasts to outperform the judgmental forecasts from the policymaker.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

Forecasting monthly riverflow time series

Mean monthly flows from thirty rivers in North and South America are used to test the short-term forecasting ability of seasonal ARIMA, deseasonalized ARMA, and periodic autoregressive models. The series were split into two sections and models were calibrated to the first portion of the data. The models were then used to generate one-step-ahead forecasts for the second portion of the data. The forecast performance is compared using various measures of accuracy. The results suggest that a periodic autoregressive model, identified by using the partial autocorrelation function, provided the most accurate forecasts     

Predicting economic contractions and expansions with the aid of professional forecasts

Traditional econometric models of economic contractions typically perform poorly in forecasting exercises. This criticism is also frequently levelled at professional forecast probabilities of contractions. This paper addresses the problem of incorporating the entire distribution of professional forecasts into an econometric model for forecasting contractions and expansions. A new augmented probit approach is proposed, involving the transformation of the distribution of professional forecasts into a ‘professional forecast’ prior for the economic data underlying the probit model. Since the object of interest is the relationship between the distribution of professional forecasts and the probit model’s economic-data dependent parameters, the solution avoids criticisms levelled at the accuracy of professional forecast based point estimates of contractions. An application to US real GDP data shows that the model yields significant forecast improvements relative to alternative approaches.     

Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data

Accurate solar forecasts are necessary to improve the integration of solar renewables into the energy grid. In recent years, numerous methods have been developed for predicting the solar irradiance or the output of solar renewables. By definition, a forecast is uncertain. Thus, the models developed predict the mean and the associated uncertainty. Comparisons are therefore necessary and useful for assessing the skill and accuracy of these new methods in the field of solar energy.The aim of this paper is to present a comparison of various models that provide probabilistic forecasts of the solar irradiance within a very strict framework. Indeed, we consider focusing on intraday forecasts, with lead times ranging from 1 to 6 h. The models selected use only endogenous inputs for generating the forecasts. In other words, the only inputs of the models are the past solar irradiance data. In this context, the most common way of generating the forecasts is to combine point forecasting methods with probabilistic approaches in order to provide prediction intervals for the solar irradiance forecasts. For this task, we selected from the literature three point forecasting models (recursive autoregressive and moving average (ARMA), coupled autoregressive and dynamical system (CARDS), and neural network (NN)), and seven methods for assessing the distribution of their error (linear model in quantile regression (LMQR), weighted quantile regression (WQR), quantile regression neural network (QRNN), recursive generalized autoregressive conditional heteroskedasticity (GARCHrls), sieve bootstrap (SB), quantile regression forest (QRF), and gradient boosting decision trees (GBDT)), leading to a comparison of 20 combinations of models.None of the model combinations clearly outperform the others; nevertheless, some trends emerge from the comparison. First, the use of the clear sky index ensures the accuracy of the forecasts. This derived parameter permits time series to be deseasonalized with missing data, and is also a good explanatory variable of the distribution of the forecasting errors. Second, regardless of the point forecasting method used, linear models in quantile regression, weighted quantile regression and gradient boosting decision trees are able to forecast the prediction intervals accurately.     

A regional forecasting model for construction activity

This paper presents a new, comprehensive and detailed model of construction activity. The model is intended primarily for forecasting applications. The model generates forecasts of new construction starts for each of the 50 states of the United States. Forecasts are made for 29 types of structures. The paper presents evidence that the structure of the determinants of construction activity varies across regions within the United States. Thus, prior models of construction, based only on national time-series data, may be subject to aggregation bias. Evaluation of the model's forecasts indicates that the model outperforms simpler forecasting methods.     

Forecasting Earnings Using Geographical Segment Data: Some UK Evidence

This study examines the question of whether or not the geographical segment data disclosed by UK companies can be used to generate forecasts of earnings that outperform forecasts based upon past consolidated data. One year ahead forecasts of attributable earnings or net income before extraordinary items are generated for both geographical sales data combined with a consolidated attributable earnings to sales margin and segmental earnings data. The forecasts are based upon forecasts of changes in the GNP of individual countries, both with and without the addition of forecasted inflation rates. It is found that models based upon both geographical segment sales and segment earnings outperform the random walk and random walk plus drift consolidated models for the years 1981 to 1983. The difference in the sizes of the errors generated by the segment data based models and the consolidated data based models are significant in the majority of cases especially when the errors are truncated at 100%. However, there is no additional advantage in terms of forecast accuracy in using segment earnings data rather than segment sales data.