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.

Estimating Orthogonal Impulse Responses via Vector Autoregressive Models

1991 ◽  
Vol 7 (4) ◽  
pp. 487-496 ◽  
Author(s):  
Helmut Lütkepohl ◽  
D.S. Poskitt
Keyword(s):  
Time Series ◽  
Forecast Error ◽  
Error Variance ◽  
Asymptotic Distributions ◽  
Impulse Response Functions ◽  
Impulse Responses ◽  
Vector Autoregressive ◽  
Forecast Error Variance ◽  
Variance Decompositions ◽  
The Relationship

Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is derived under the assumption that finite order vector autoregressive (VAR) models are fitted to time series generated by possibly infinite order processes. The resulting asymptotic distributions of forecast error variance decompositions are also given.

Is Money Supply Exogenous? Evidence from India

2017 ◽  
Vol 11 (2) ◽  
pp. 167-195
Author(s):  
Santosh Kumar Dash
Keyword(s):  
Error Correction ◽  
Money Supply ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Jel Classification ◽  
Quarterly Data ◽  
Causality Tests ◽  
Forecast Error Variance ◽  
Major Factors

Against the backdrop of the claim that the rising growth rate of money is one of the major factors behind India’s recent bout of elevated and sticky inflation, this article asks: Is money supply exogenous or endogenous, and can it predict future inflation. This question is investigated using the monetarist framework of inflation. In the empirical analysis of data spanning from 1970–71 to 2009–10, the results of both the monetarist and the error-correction models suggest that money supply accounts for inflation in India. There is also the presence of an error-correction mechanism among money, inflation and output. However, a monetarist equation does not tell anything about causality. Thus, the vector autoregression (VAR) method is used to detect the direction of causality between money supply and the inflation rate. Findings from Granger causality tests suggest weak evidence of inflation (Granger) causing money supply. As a robustness check, we estimate VAR models using quarterly data and, further, using core inflation. The results of the causality tests from the quarterly data, the impulse response function and forecast error variance decomposition suggest that money supply is weakly endogenous. JEL Classification: E31, E51, E52

scholarly journals Identification and inference with ranking restrictions

2021 ◽  
Vol 12 (1) ◽  
pp. 1-39
Author(s):  
Pooyan Amir-Ahmadi ◽  
Thorsten Drautzburg
Keyword(s):  
Forecast Error ◽  
Error Variance ◽  
Vector Autoregressions ◽  
News Shocks ◽  
Sign Restrictions ◽  
Forecast Error Variance ◽  
Variance Decompositions ◽  
One Year ◽  
The One ◽  
Fully Bayesian

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

scholarly journals Assessing the progress of exports diversification in Saudi Arabia: growth-share matrix approach

2020 ◽  
Vol 18 (3) ◽  
pp. 118-128
Author(s):  
Mohammad Imdadul Haque
Keyword(s):  
Growth Rate ◽  
Saudi Arabia ◽  
Forecast Error ◽  
Impulse Response Function ◽  
Error Variance ◽  
Vector Autoregressive ◽  
Long Run ◽  
Market Shares ◽  
Forecast Error Variance ◽  
Revenue Volatility

High dependence on a particular category of exports results in fluctuations in income as the price of the export item fluctuates. In Saudi Arabia, a single category of mineral exports forms over 78% of the total exports, exposing the country to revenue volatility. The study aims to assess the magnitude of diversification of the export basket for the country. It uses data from 1984 to 2018 to study the importance of non-mineral exports in total exports. It applies Granger causality, variance decomposition, and impulse response function in the vector autoregressive framework. The study also uses the growth-share matrix to evaluate individual items of non-mineral exports. The results show a long-run relationship with a 1% increase in non-mineral exports, leading to a 0.30% increase in total exports. Non-mineral exports Granger-cause total exports. In the long run, non-mineral exports have a share of 64% of the forecast error variance in total exports. Moreover, a 1% shock in non-mineral exports creates a huge initial impact on total exports. Also, the growth rate of non-mineral products is higher than mineral products. The results indicate the importance of non-mineral exports for a predominantly oil-exporting country. Finally, the study attempts to classify its non-mineral export categories based on growth rates and market shares. Targeted emphasis on export category with a strong growth rate and low market share can be an effective strategy for further export diversification.

scholarly journals The determinants of electricity consumption for ASEAN countries

2017 ◽  
Vol 13 (4-1) ◽  
pp. 331-339
Author(s):  
Mohd Tahir Ismail ◽  
Nadhilah Mahmud ◽  
Rosmanjawati Abdul Rahman
Keyword(s):  
Forecast Error ◽  
Carbon Dioxide Emission ◽  
Electricity Consumption ◽  
Error Variance ◽  
Sample Period ◽  
Causality Test ◽  
Vector Autoregressive ◽  
Vec Model ◽  
Forecast Error Variance ◽  
Asean Countries

The present study investigates the causal relationship between ASEAN seven member countries electricity consumption (EC) and some determinants such as gross domestic product (GDP), exports (EXP) and carbon dioxide emission (CO2) using vector autoregressive (VAR) framework via vector error correction (VEC) model for the period from 1980-2015. The findings show that the effect of the chosen determinants is different among the seven countries. Within the sample period, by utilizing Granger causality test, out of the seven countries, only four revealed either unidirectional or bidirectional causality running from EC to the three determinants, GDP, EXP and CO2. Whereas, thru forecast error variance decomposition (FEVD), forecasting beyond the sample period uncovered a shock to EC will also spread to GDP, EXP and CO2. The present study suggests that ASEAN should take note in designing their electricity policy, since electricity affect and be affected by other factors. In addition, ASEAN also should find solutions on how to control CO2 emission through EC.

scholarly journals Dynamic Relationships Among U.S. Wheat-Related Markets: Applying Directed Acyclic Graphs to a Time Series Model

2004 ◽  
Vol 36 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Ronald A. Babula ◽  
David A. Bessler ◽  
Warren S. Payne
Keyword(s):  
Forecast Error ◽  
Value Added ◽  
Error Variance ◽  
Relevant Information ◽  
Directed Acyclic Graphs ◽  
Structural Vector Autoregression ◽  
Acyclic Graphs ◽  
Forecast Error Variance ◽  
Variance Decompositions ◽  
Dynamic Relationships

Using advanced methods of directed acyclic graphs with Bernanke structural vector autoregression models, this article extends recent econometric research on quarterly U.S. markets for wheat and wheat-based value-added products downstream. Analyses of impulse response simulations and forecast error variance decompositions provide updated estimates of market elasticity parameters that drive these markets, and updated policy-relevant information on how these quarterly markets run and dynamically interact. Results suggest that movements in wheat and downstream wheat-based markets strongly influence each other, although most of these effects occur at the longer-run horizons beyond a single crop cycle.

Smart advertising in prescription only medication; aligning it with prescriber’s or consumer’s behavior

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mesay Moges Menebo
Keyword(s):  
Forecast Error ◽  
Unit Root Test ◽  
Empirical Support ◽  
Error Variance ◽  
Model Specification ◽  
Dynamic Interactions ◽  
Carryover Effect ◽  
Content Type ◽  
Forecast Error Variance ◽  
Market Leader

Purpose This study has four objectives. First is to investigate and compare the immediate and carryover effects of four pharmaceutical marketing tools (prescriber detailing, medical events, journal ads and direct-to-consumer advertising [DTCA]) on sales. Based on the effect comparisons, the second objective is to determine whether advertising tools that are more compatible with prescriber’s behavior have superior impact on sales. Third is to examine empirical support for the argument that advertising directly to consumers, as a market follower versus leader, has a backfiring effect. Finally, this paper aims to assess the magnitude of variance in sales as a function of each advertising tool. Design/methodology/approach Data on unit sales and spending (on DTCA, journal ads, events and detailing) ranging 84 months are obtained for six prescription-only cholesterol-reducing brands. First, linearity is checked. Second, evolution versus stationarity is tested by applying the unit-root test. Third, potential endogeneity among variables is assessed with granger causality. Fourth, vector autoregressive model (VAR) that accounts for endogeneity and dynamic interactions is specified. Intercept, seasons and market share are added into the model specification as exogenous variables. Fifth, VAR with akaike selected lags and generalized impulse response are conducted. Finally, sales variance is decomposed with forecast error variance decomposition and Cholesky ordering. Findings A 10% increase on detailing or journal ads spending brought an immediate (one month) negative effect on sales in a market leader, whereas that same increase is insignificant in a market follower. A 10% increase on DTCA (vs detailing) spending led to a negative (vs positive) carryover effect for the market follower, giving empirical support to the backfiring effect of DTCA and partial evidentiary support suggested about prescriber friendly advertising. However, DTCA induces a larger short term and longer carryover effect in a market leader, with seven times more effect on sales than what detailing does. In addition, it explains 50% of the variation in sales. Originality/value The model applied captures extensive dynamics; hence, findings are robust. The analysis considered comparison in terms of prescriber friendly (vs not) advertising tools and brand market status and thus can make managers rethink strategy of advertising budget allocations. This study also introduced a new look onto DTCA and hence challenges the traditional thought held on consumer advertising response.

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