14,417 research outputs found
PRICE FORECASTING WITH TIME-SERIES METHODS AND NONSTATIONARY DATA: AN APPLICATION TO MONTHLY U.S. CATTLE PRICES
The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of nonstationarity. Procedures that permit model specifications consistent with the systemÂ’s dynamics provide the most accurate forecasts.Demand and Price Analysis, Livestock Production/Industries,
Multivariate control charts based on Bayesian state space models
This paper develops a new multivariate control charting method for vector
autocorrelated and serially correlated processes. The main idea is to propose a
Bayesian multivariate local level model, which is a generalization of the
Shewhart-Deming model for autocorrelated processes, in order to provide the
predictive error distribution of the process and then to apply a univariate
modified EWMA control chart to the logarithm of the Bayes' factors of the
predictive error density versus the target error density. The resulting chart
is proposed as capable to deal with both the non-normality and the
autocorrelation structure of the log Bayes' factors. The new control charting
scheme is general in application and it has the advantage to control
simultaneously not only the process mean vector and the dispersion covariance
matrix, but also the entire target distribution of the process. Two examples of
London metal exchange data and of production time series data illustrate the
capabilities of the new control chart.Comment: 19 pages, 6 figure
Extracting the Italian output gap: a Bayesian approach
During the last decades particular effort has been directed towards
understanding and predicting the relevant state of the business cycle with the
objective of decomposing permanent shocks from those having only a transitory
impact on real output. This trend--cycle decomposition has a relevant impact on
several economic and fiscal variables and constitutes by itself an important
indicator for policy purposes. This paper deals with trend--cycle decomposition
for the Italian economy having some interesting peculiarities which makes it
attractive to analyse from both a statistic and an historical perspective. We
propose an univariate model for the quarterly real GDP, subsequently extended
to include the price dynamics through a Phillips curve. This study considers a
series of the Italian quarterly real GDP recently released by OECD which
includes both the 1960s and the recent global financial crisis of 2007--2008.
Parameters estimate as well as the signal extraction are performed within the
Bayesian paradigm which effectively handles complex models where the parameters
enter the log--likelihood function in a strongly nonlinear way. A new Adaptive
Independent Metropolis--within--Gibbs sampler is then developed to efficiently
simulate the parameters of the unobserved cycle. Our results suggest that
inflation influences the Output Gap estimate, making the extracted Italian OG
an important indicator of inflation pressures on the real side of the economy,
as stated by the Phillips theory. Moreover, our estimate of the sequence of
peaks and troughs of the Output Gap is in line with the OECD official dating of
the Italian business cycle
Penalized Clustering of Large Scale Functional Data with Multiple Covariates
In this article, we propose a penalized clustering method for large scale
data with multiple covariates through a functional data approach. In the
proposed method, responses and covariates are linked together through
nonparametric multivariate functions (fixed effects), which have great
flexibility in modeling a variety of function features, such as jump points,
branching, and periodicity. Functional ANOVA is employed to further decompose
multivariate functions in a reproducing kernel Hilbert space and provide
associated notions of main effect and interaction. Parsimonious random effects
are used to capture various correlation structures. The mixed-effect models are
nested under a general mixture model, in which the heterogeneity of functional
data is characterized. We propose a penalized Henderson's likelihood approach
for model-fitting and design a rejection-controlled EM algorithm for the
estimation. Our method selects smoothing parameters through generalized
cross-validation. Furthermore, the Bayesian confidence intervals are used to
measure the clustering uncertainty. Simulation studies and real-data examples
are presented to investigate the empirical performance of the proposed method.
Open-source code is available in the R package MFDA
Recommended from our members
Econometrics: A bird's eye view
As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledge and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks and forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of the "real time econometrics". This paper attempts to provide an overview of some of these developments
- …