[PDF] Top 20 Forecasting in vector autoregressions with many predictors
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Forecasting in vector autoregressions with many predictors
... on many variables that potentially help explain economic variables of interest such as inflation and ...215 predictors to forecast 8 major macroeconomic vari- ables for the ... See full document
31
Forecasting U S Recessions with a Large Set of Predictors
... Finally, it is of interest to compare the forecasting performance of our models against the factor-augmented probit models by Chen et al. (2011) and Christiansen et al. (2014). They provide a natural comparison, ... See full document
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UK Macroeconomic Forecasting with Many Predictors : Which Models Forecast Best and When Do They Do So?
... to forecasting with many ...these predictors into factors reflecting different blocks of variables ...a forecasting model which simply includes all blocks as predictors risks being ... See full document
32
FORECASTING INDONESIAN INFLATION WITHIN AN INFLATION-TARGETING FRAMEWORK: DO LARGE-SCALE MODELS PAY OFF?
... the predictors of ...exogenous predictors are the logarithms of the industrial production index (LIP), the consumer confidence index (LCCI), the business confidence index (LBCI), the global price of food ... See full document
14
Hierarchical shrinkage priors for dynamic regressions with many predictors
... with many orthogonal ...of many shrinkage estimators is possible, including pretest methods, Bayesian model averaging, empirical Bayes, and ...in forecasting performance by shrinking the coefficients ... See full document
34
Sales Forecasting using Linear Regression and Support Vector Machine
... simplifies forecasting by executing machine learning models that run automatically and present a monthly or quarterly forecast of a customer's sales metric ...our forecasting ensemble. Time series ... See full document
7
Time Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean
... Based on these considerations, this paper contributes to the literature in four directions. First, it introduces a general, fully time-varying VAR model which is formulated on an equation by equa- tion basis. For each ... See full document
54
Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations
... the forecasting experiment including as predictors both an indicator of unemployment, based on the degree of agreement in consumer unemployment expectations, and a measure of disagreement based on the ... See full document
10
A Direct Estimation of High Dimensional Stationary Vector Autoregressions
... dependence in the data sequence and estimating them builds a fundamental first step in forecasting. Moreover, the zero and nonzero entries in the transition matrices directly in- corporate the Granger ... See full document
36
Bayesian compressed vector autoregressions
... forecasting results for seven variables of interest: industrial production growth (INDPRO), the unemployment rate (UNRATE), total nonfarm employment (PAYEMS), the change in the Fed funds rate (FEDFUNDS), the ... See full document
63
Economic theory and econometric models
... "A Statistical Approach to Economic Forecasting", Journal of Business and Economic Statistics, Vol... "Forecasting with Bayesian Vector Autoregressions — Five Years of Experience", Journ[r] ... See full document
25
Bayesian vector autoregressions
... the forecasting model to change over time, and for the coefficients in each of the models considered to also be time ...introduce forecasting using specification-switching ... See full document
60
Prior selection for panel vector autoregressions
... or forecasting economic trends are inherently multivariate, involving analysis of variables such as in‡ation, GDP, the interest rate, and the unemployment ...the vector autoregressive (VAR) model; see Koop ... See full document
26
Data based priors for vector autoregressions with drifting coefficients
... Second, while in constant parameter VARs we expect larger systems with more information to perform well, when considering nonlinearities in the form of time- varying coefficients and stochastic volatility there is no ... See full document
26
Application of LSSVM to logistics demand forecasting based on grey relational analysis and kernel principal component analysis
... The forecasting performance of the GRA-KPCA-LSSVM model is investigated through the use of China logistics ...The forecasting performance of the GRA-KPCA-LSSVM model is better than those of the other ... See full document
6
Forecasting football match results: Are the many smarter than the few?
... the forecasting literature that predictions from statistical models are better than predictions by experts when forecasting football match results using data from English ...poor forecasting ... See full document
27
Improved tests for spatial autoregressions
... Econometric modelling and statistical inference are considerably complicated by the possibility of correlation across data data recorded at different locations in space. A major branch of the spatial econometrics ... See full document
154
FORECASTING INFLATION IN NIGERIA: A VECTOR AUTOREGRESSION APPROACH
... There is a paucity of methodologically sound studies directly addressing the forecasting of the future path of inflation in Nigeria. Apart from recent studies by Adebiyi, Adenuga, Abeng, Omanukwe and ... See full document
13
Modeling Expectations with Noncausal Autoregressions
... plicated empirical issue than in conventional causal autoregressions. Which model is selected is also of great economic interest, as it tells us to what extent the economic variable depends on its past and ... See full document
38
Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models
... Table 3 shows the difference between maximum and minimum MAPE of the 60 results. This is crucial when selecting which model to use in forecasting. Remember these results are from the extreme volatile period caused ... See full document
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