A Bayesian Analysis of Unit Roots and Structural Breaks in the Level and the Error Variance of Autoregressive Models of economic series

2004 ◽  
Author(s):  
Loukia Meligkotsidou ◽  
Elias Tzavalis ◽  
Ioannis D. Vrontos
Keyword(s):  
Bayesian Analysis ◽  
Structural Breaks ◽  
Unit Roots ◽  
Error Variance ◽  
Autoregressive Models

Wagner’s Law, Public Employment, and Spanish Regions

2018 ◽  
Vol 64 (3) ◽  
pp. 179-198
Author(s):  
Manuel Jaén-García
Keyword(s):  
Public Sector ◽  
Panel Data ◽  
Structural Breaks ◽  
Empirical Test ◽  
Unit Roots ◽  
Public Employment ◽  
Public Companies ◽  
The Public ◽  
Standard Tool ◽  
Definition Of

Abstract Following Peacock and Musgrave’s rediscovery of Wagner’s Law, the latter became a standard tool used in research on the relationship between growth of public spending and the factors by which it is influenced. However, conventional empirical tests are based on a specification error related to Wagner’s definition of the public sector, which he considered in its totality, including public companies. The present article attempts to correct this error and obtain an approximation to the size of the public sector by considering public employment as a whole, both in public administrations and services and in public companies. To this end, panel data for the Spanish autonomous regions are used in addition to data for the overall public sector. The empirical test is performed utilizing cointegration techniques and unit roots in panel data. Similarly, the possibility of structural breaks in the data is taken into consideration and they are estimated using fictitious variables. JEL classifications: H11; H50; E62 Keywords: public employment; gross domestic product; unit root; cointegration; panel data

scholarly journals Breaks, trends and unit roots in commodity prices: a robust investigation

2014 ◽  
Vol 18 (1) ◽  
Author(s):  
Atanu Ghoshray ◽  
Mohitosh Kejriwal ◽  
Mark Wohar
Keyword(s):  
Unit Root ◽  
Structural Breaks ◽  
Commodity Prices ◽  
Unit Roots ◽  
Commodity Price ◽  
Policy Implications ◽  
Price Shocks ◽  
Price Series ◽  
Commodity Price Shocks ◽  
Trend Breaks

AbstractThis paper empirically examines the time series behavior of primary commodity prices relative to manufactures with reference to the nature of their underlying trends and the persistence of shocks driving the price processes. The direction and magnitude of the trends are assessed employing a set of econometric techniques that is robust to the nature of persistence in the commodity price shocks, thereby obviating the need for unit root pretesting. Specifically, the methods allow consistent estimation of the number and location of structural breaks in the trend function as well as facilitate the distinction between trend breaks and pure level shifts. Further, a new set of powerful unit root tests is applied to determine whether the underlying commodity price series can be characterized as difference or trend stationary processes. These tests treat breaks under the unit root null and the trend stationary alternative in a symmetric fashion thereby alleviating the procedures from spurious rejection problems and low power issues that plague most existing procedures. Relative to the extant literature, we find more evidence in favor of trend stationarity suggesting that real commodity price shocks are primarily of a transitory nature. We conclude with a discussion of the policy implications of our results.

scholarly journals Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

2009 ◽  
Vol 41 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Andrew M. McKenzie ◽  
Harold L. Goodwin ◽  
Rita I. Carreira
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Autoregressive Models ◽  
Model Specification ◽  
Decomposition Approach ◽  
Vector Autoregressive ◽  
Forecasting Performance ◽  
Causality Tests ◽  
Forecast Error Variance ◽  
Variance Decompositions

Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to traditional Granger Causality tests.

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