nep-for New Economics Papers
on Forecasting
Issue of 2013‒11‒02
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Comparing the BER’s forecasts By Nicolaas van der Wath
  2. Understanding Uncertainty Shocks By Laura Veldkamp; Anna Orlik
  3. Bayesian Variable Selection for Nowcasting Economic Time Series By Steven L. Scott; Hal R. Varian
  4. Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview By Jennifer Castle; David Hendry
  5. Heterogeneous Beliefs and Prediction Market Accuracy By He, Xue-Zhong; Treich, Nicolas
  6. Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective By Liebl, Dominik
  7. Do Asset Price Drops Foreshadow Recessions? By John C Bluedorn; Jörg Decressin; Marco Terrones
  8. A Dynamic Duverger's Law By Jean Guillaume Forand; Vikram Maheshri
  9. Predicting trend reversals using market instantaneous state By Thomas Bury
  10. A sticky information Phillips curve for South Africa By Monique Reid; Gideon du Rand
  11. What Predicts a Successful Life? A Life-Course Model of Well-Being By Andrew E. Clark; Francesca Cornaglia; Richard Layard; Nattavudh Powdthavee; James Vernoit

  1. By: Nicolaas van der Wath (Bureau for Economic Research)
    Abstract: The Bureau for Economic Research publishes annual (and quarterly) forecasts for more than 140 macroeconomic indicators, with a forecasting horizon stretching up to 6 years ahead. These forecasts are generated with the aid of a structural macro-econometric model of the South African economy. The purpose of this re-search note is to test the accuracy of the BER’s forecasts. Also to compare them with other published forecasts according to accuracy, forecast horizon and number of indicators. To determine the level of accuracy, we have calculated the mean absolute errors and the root mean squared errors of the BER’s forecasts for a selection of five economic indicators. These statistics were also calculated for the forecasts of the selected other institutions or models. From these the relative accuracy of the different forecasts were compared to each other and ranked ac-cordingly. The consensus forecast turned out to be the most accurate for the im-mediate year, followed with a narrow margin by the BER. The close proximity of these two forecasts is striking. Other conclusions are that structural forecasting models perform better than mechanical ones for the first two years, but lose their accuracy advantage from the third or fourth year onwards. They also fail to antic-ipate critical turning points in economic cycles.
    Keywords: forecast comparison; forecast accuracy, forecast evaluation, consen-sus forecast, Theil coefficients, mean absolute error, root mean squared error, loss function
    JEL: C53
    Date: 2013
  2. By: Laura Veldkamp (NYU Stern); Anna Orlik (Federal Reserve Board of Governors)
    Abstract: or decades, macroeconomists have searched for shocks that are plausible drivers of business cycles. A recent advance in this quest has been to explore uncertainty shocks. Researchers use a variety of forecast and volatility data to justify heteroskedastic shocks in a model, which can then generate realistic cyclical uctuations. But the relevant measure of uncertainty in most models is the conditional variance of a forecast. When agents form such forecasts with state, parameter and model uncertainty, neither forecast dispersion nor innovation volatilities are good proxies for conditional forecast variance. We use observable data to select and estimate a forecasting model and then ask the model to inform us about what uncertainty shocks look like and why they arise.
    Date: 2013
  3. By: Steven L. Scott; Hal R. Varian
    Abstract: We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine query data as predictors for consumer sentiment and gun sales.
    JEL: C11 C53
    Date: 2013–10
  4. By: Jennifer Castle; David Hendry
    Abstract: We consider the reasons for nowcasting, how nowcasts can be achieved, and the use and timing of information.� The existence of contemporaneous data such as surveys is a major difference from forecasting, but many of the recent lessons about forecasting remain relevant.� Given the extensive disaggregation over variables underlying flash estimates of aggregates, we show that automatic model selection can play a valuable role, especially when location shifts would otherwise induce nowcast failure.� Thus, we address nowcasting when location shifts occur, probably with measurement error.� We describe impulse-indicator saturation as a potential solution to such shifts, noting its relation to intercept corrections and to robust methods to avoid systematic nowcast failure.� We propose a nowcasting strategy, building models of all disaggregate series by automatic methods, forecasting all variables before the end of each period, testing for shifts as available measures arrive, and adjusting forecasts of cognate missing series if substantive discrepancies are found.� An alternative is switching to robust forecasts when breaks are detected.� We apply a variant of this strategy to nowcast UK GDP growth, seeking pseudo real-time data availability.
    Keywords: Nowcasting, Location shifts, Forecasting, Contemporaneous information, Autometrics, Impulse-indicator saturation
    JEL: C52 C51
    Date: 2013–09–27
  5. By: He, Xue-Zhong; Treich, Nicolas
    Date: 2013–01
  6. By: Liebl, Dominik
    Abstract: Classical time series models have serious difficulties in modeling and forecasting the enormous fluctuations of electricity spot prices. Markov regime switch models belong to the most often used models in the electric- ity literature. These models try to capture the fluctuations of electricity spot prices by using different regimes, each with its own mean and covariance structure. Usually one regime is dedicated to moderate prices and another is dedicated to high prices. However, these models show poor performance and there is no theoretical justification for this kind of classification. The merit or- der model, the most important micro-economic pricing model for electricity spot prices, however, suggests a continuum of mean levels with a functional dependence on electricity demand. We propose a new statistical perspective on modeling and forecasting electricity spot prices that accounts for the merit order model. In a first step, the functional relation between electricity spot prices and electricity demand is modeled by daily price-demand functions. In a second step, we parameter- ize the series of daily price-demand functions using a functional factor model. The power of this new perspective is demonstrated by a forecast study that compares our functional factor model with two established classical time se- ries models as well as two alternative functional data models.
    Keywords: Functional factor model, functional data analysis, time series analysis, fundamental market model, merit order curve, European Energy Exchange, EEX
    JEL: C1 C14 C5
    Date: 2013–09
  7. By: John C Bluedorn; Jörg Decressin; Marco Terrones
    Abstract: This paper examines the usefulness of asset prices in predicting recessions in the G-7 countries. It finds that asset price drops are significantly associated with the beginning of a recession in these countries. In particular, the marginal effect of an equity/house price drop on the likelihood of a new recession can be substantial. Equity price drops are, however, larger and are more frequent than house price drops, making them on average more helpful as recession predictors. These findings are robust to the inclusion of the term-spread, uncertainty, and oil prices. Lastly, there is no evidence of significant bias resulting from the rarity of recession starts.
    Keywords: Asset prices;Group of seven;Stock markets;Business cycles;Economic recession;Economic forecasting;Economic models;Business cycles; Macroeconomic forecasting; Financial markets; Uncertainty; Oil Prices; Binary dependent variable models.
    Date: 2013–10–02
  8. By: Jean Guillaume Forand (University of Waterloo); Vikram Maheshri (University of Houston)
    Abstract: Electoral systems promote strategic voting and aect party systems. Duverger (1951) proposed that plurality rule leads to bi-partyism and proportional representation leads to multi-partyism. We show that in a dynamic setting, these static eects also lead to a higher option value for existing minor parties under plurality rule, so their incentive to exit the party system is mitigated by their future benefits from continued participation. The predictions of our model are consistent with multiple cross-sectional predictions on the comparative number of parties under plurality rule and proportional representation. In particular, there could be more parties under plurality rule than under proportional representation at any point in time. However, our model makes a unique time-series prediction: the number of parties under plurality rule should be less variable than under proportional representation. We provide extensive empirical evidence in support of these results.
    Keywords: Duverger's Law, Electoral Competition, Dynamic Political Economy
    JEL: H1
    Date: 2013–10–22
  9. By: Thomas Bury
    Abstract: Collective behaviors taking place in financial markets reveal strongly correlated states especially during a crisis period. A natural hypothesis is that trend reversals are also driven by mutual influences between the different stock exchanges. Using a maximum entropy approach, we find coordinated behavior during trend reversals dominated by the pairwise component. In particular, these events are predicted with high significant accuracy by the ensemble's instantaneous state.
    Date: 2013–10
  10. By: Monique Reid (Department of Economics, University of Stellenbosch); Gideon du Rand (Department of Economics, University of Stellenbosch)
    Abstract: Mankiw and Reis (2002) propose the Sticky Information Phillips Curve as an alternative to the standard New Keynesian Phillips Curve, to address empirical shortcomings in the latter. In this paper, a Sticky Information Phillips curve for South Africa is estimated, which requires data on expectations of current period variables conditional on sequences of earlier period information sets. In the literature the choice of proxies for the inflation expectations and output gap measures are usually not well motivated. In this paper, we test the sensitivity of model fit and parameter estimates to a variety of proxies. We find that parameter estimates for output gap proxies based either on a simple Hodrik-Prescott filter application or on a Kalman filter estimation of an aggregate production function are significant and reasonable, whereas methods employing direct calculation of marginal costs do not yield acceptable results. Estimates of information updating probability range between 0.69 and 0.81. This is somewhat higher than suggested by alternative methods using micro-evidence (0.65 – 0.70 (Reid, 2012)). Lastly, we find that neither parameter estimates nor model diagnostics are sensitive to the choice of expectation proxy, whether it be constructed from surveyed expectations or the ad hoc VAR based forecasting methods.
    Keywords: South Africa, sticky information, Phillips curve
    JEL: E31 E3 E52
    Date: 2013
  11. By: Andrew E. Clark; Francesca Cornaglia; Richard Layard; Nattavudh Powdthavee; James Vernoit
    Abstract: If policy-makers care about well-being, they need a recursive model of how adult life-satisfaction is predicted by childhood influences, acting both directly and (indirectly) through adult circumstances. We estimate such a model using the British Cohort Study (1970). The most powerful childhood predictor of adult life-satisfaction is the child's emotional health. Next comes the child's conduct. The least powerful predictor is the child's intellectual development. This has obvious implications for educational policy. Among adult circumstances, family income accounts for only 0.5% of the variance of life-satisfaction. Mental and physical health are much more important.
    Keywords: Well-being, Life-satisfaction, Intervention, Model, Life-course, Emotional health, Conduct, Intellectual performance, Success
    JEL: A12 D60 H00 I31
    Date: 2013–10

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