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on Forecasting |
| By: | Frank Bohn (Radboud University, Institute for Management Research, Department of Economics); Francisco José Veiga (NIPE and Economics Department, University of Minho.) |
| Abstract: | By forecasting overly optimistic revenues opportunistic governments can increase spending in order to appear more competent prior to elections. Ex post deficits emerge in election years, thereby producing political forecast cycles - as also found for US states in the empirical literature. In our theoretical moral hazard model we obtain three additional results which are tested with panel data for Portuguese municipalities. The extent of manipulations is reduced when (i) the winning margin is expected to widen; (ii) the incumbent is not re-running; and/or (iii) the share of informed voters (proxied by education) goes up. |
| Keywords: | opportunistic political cycles; political budget cycles; revenue forecasts; deficit; transfers; asymmetric information; political economy. |
| JEL: | D72 H68 E32 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:nip:nipewp:12/2019&r=all |
| By: | Chakraborty, Lekha; Chakraborty, Pinaki; Shrestha, Ruzel |
| Abstract: | Budget credibility, the ability of governments to accurately forecast the macro-fiscal variables, is crucial for effective Public Finance Management (PFM). Fiscal marksmanship analysis captures the extent of errors in the budgetary forecasting. The fiscal rules can determine fiscal marksmanship, as effective fiscal consolidation procedure affects the fiscal behaviour of the states in conducting the budgetary forecasts. Against this backdrop, applying Theil’s technique, we analyse the fiscal forecasting errors for 28 States (except Telengana) in India for the period 2011-12 to 2015-16. There is a heterogeneity in the magnitude of errors across subnational governments in India. The forecast errors in revenue receipts have been greater than revenue expenditure. Within revenue receipts, the errors are pronounced more significantly in grants component. Within expenditure budgets, the errors in capital spending are found greater than revenue spending in all the States. Partitioning the sources of errors, we identified that the errors were more broadly random than systematic bias, except for a few crucial macro-fiscal variables where improving the forecasting techniques can provide better estimates. |
| Keywords: | forecast errors, fiscal policies, fiscal forecasting, political economy, fiscal marksmanship |
| JEL: | C53 E62 H6 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95921&r=all |
| By: | Jialin Liu; Chih-Min Lin; Fei Chao |
| Abstract: | Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.09563&r=all |
| By: | Becker, Janis; Hollstein, Fabian; Prokopczuk, Marcel; Sibbertsen, Philipp |
| Abstract: | Researchers and practitioners employ a variety of time-series processes to forecast betas, using either short-memory models or implicitly imposing infinite memory. We find that both approaches are inadequate: beta factors show consistent long-memory properties. For the vast majority of stocks, we reject both the short-memory and difference-stationary (random walk) alternatives. A pure long-memory model reliably provides superior beta forecasts compared to all alternatives. Finally, we document the relation of firm characteristics with the forecast error differentials that result from inadequately imposing short-memory or random walk instead of long-memory processes. |
| Keywords: | Long memory; beta; persistence; forecasting; predictability |
| JEL: | C58 G15 G12 G11 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-661&r=all |
| By: | Mehmet Selman Colak; Ibrahim Ethem Guney; Ahmet Senol; Muhammed Hasan Yilmaz |
| Abstract: | Credit growth rate deviating from its long-run trend or equilibrium value holds importance for policymakers given the implications on economic activity and macro-financial interactions. In the first part of this study, the main aim is to construct indicators for determining the episodes of moderate-to-excessive credit slowdown and expansion by utilizing time-series filtering methods such as Hodrick-Prescott filter, Butterworth filter, Christiano-Fitzgerald filter and Hamilton filter over the time period 2007-2019. In addition to filtering choices, four different credit ratios (which are credit-to-GDP ratio, real credit growth, logarithm of real credit, credit impulse ratio) are included in the methodology to ensure the robustness. This framework enables one to generate monitoring tools for not only total loans, but also for financial intermediation activities with different loan breakdowns regarding type, sector and currency denomination. Moreover, industry-based dynamics of commercial loans are examined by using micro-level Credit Registry data set. In the following part, the credit cycle implied by macroeconomic dynamics are investigated by using factor-augmented predictive regression models. In this context, factors representing the global economic developments, banking sector outlook, local financial conditions and economic growth tendencies are created from large data set of 107 time series by utilizing principal component analysis. Analysis conducted for January 2009-April 2019 interval seems to be in line with exogenous shocks affecting the credit market in the corresponding period. To gain more knowledge about the predictive power of factor-augmented regression models, out-of-sample forecasting exercises are performed. It is found that global forces and economic activity provide substantial improvement in terms of predictive power over simple autoregressive benchmark models given low level of relative forecast errors. |
| Keywords: | Credit cycle, Macroeconomic dynamics, Filtering, Factor models, Forecasting |
| JEL: | G21 E51 C38 C53 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:tcb:wpaper:1929&r=all |
| By: | Troy D Matheson |
| Abstract: | Against the backdrop of an ongoing review of the inflation-targeting framework, this paper examines the real-time inflation forecasts of the Bank of Canada with the aim of identifying potential areas for improvement. Not surprisingly, the results show that errors in forecasting non-core inflation (commodity prices etc.) are found to be the largest contributors to overall inflation forecast errors. Perhaps more importantly, relatively small core inflation forecast errors appear to mask large and offsetting errors related to the output gap and the policy interest rate, partly reflecting a tendency to overestimate the neutral nominal policy rate in real time. Faced with these uncertainties, the Governing Council’s gradual approach to changing its policy settings appears to have served it well. |
| Date: | 2019–09–13 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/190&r=all |
| By: | Fantazzini, Dean; Shangina, Tamara |
| Abstract: | This paper focuses on the forecasting of market risk measures for the Russian RTS index future, and examines whether augmenting a large class of volatility models with implied volatility and Google Trends data improves the quality of the estimated risk measures. We considered a time sample of daily data from 2006 till 2019, which includes several episodes of large-scale turbulence in the Russian future market. We found that the predictive power of several models did not increase if these two variables were added, but actually decreased. The worst results were obtained when these two variables were added jointly and during periods of high volatility, when parameters estimates became very unstable. Moreover, several models augmented with these variables did not reach numerical convergence. Our empirical evidence shows that, in the case of Russian future markets, T-GARCH models with implied volatility and student’s t errors are better choices if robust market risk measures are of concern. |
| Keywords: | Forecasting; Value-at-Risk; Realized Volatility; Google Trends; Implied Volatility; GARCH; ARFIMA; HAR; Realized-GARCH |
| JEL: | C22 C51 C53 G17 G32 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95992&r=all |
| By: | Victor Chernozhukov; Kaspar W\"uthrich; Yinchu Zhu |
| Abstract: | We propose a robust method for constructing conditionally valid prediction intervals based on regression models for conditional distributions such as quantile and distribution regression. Our approach exploits the probability integral transform and relies on permuting estimated ``ranks'' instead of regression residuals. Unlike residuals, these ranks are independent of the covariates, which allows us to establish the conditional validity of the resulting prediction intervals under consistent estimation of the conditional distributions. We also establish theoretical performance guarantees under arbitrary model misspecification. The usefulness of the proposed method is illustrated based on two applications. First, we study the problem of predicting daily returns using realized volatility. Second, we consider a synthetic control setting where the goal is to predict a country's counterfactual GDP growth rate based on the contemporaneous GDP growth rates of other countries. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.07889&r=all |
| By: | Dmitry Zotikov; Anton Antonov |
| Abstract: | We propose a method for detection and prediction of native and synthetic iceberg orders on Chicago Mercantile Exchange. Native (managed by the exchange) icebergs are detected using discrepancies between the resting volume of an order and the actual trade size as indicated by trade summary messages, as well as by tracking order modifications that follow trade events. Synthetic (managed by market participants) icebergs are detected by observing limit orders arriving within a short time frame after a trade. The obtained icebergs are then used to train a model based on the Kaplan--Meier estimator, accounting for orders that were cancelled after a partial execution. The model is utilized to predict the total size of newly detected icebergs. Out of sample validation is performed on the full order depth data, performance metrics and quantitative estimates of hidden volume are presented. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.09495&r=all |