Industrial production index forecast: Methods comparison

2012 ◽  
Author(s):  
M. Filomena Teodoro
Keyword(s):  
Industrial Production ◽  
Methods Comparison ◽  
Production Index ◽  
Industrial Production Index

Industrial Output in the USSR and Russia, 1861—2012. Part II. Crises and Cycles

2013 ◽  
pp. 138-153 ◽  
Author(s):  
S. Smirnov
Keyword(s):  
Industrial Production ◽  
Turning Points ◽  
Russian Empire ◽  
Industrial Output ◽  
Production Index ◽  
Russian Industry ◽  
Industrial Production Index ◽  
Cyclical Volatility

Calculation of the aggregated "consensus" industrial production index has made it possible to date cyclical turning points and to measure the depth and length of the main industrial recessions in Russian Empire/USSR/Russia for the last century and a half. The most important causes of all these recessions are described. The cyclical volatility of Soviet/Russian industry is compared to that of American one.

ANALYSIS OF THE RELATIONSHIP BETWEEN ECONOMIC GROWTH, INFLATION AND UNEMPLOYMENT IN TURKEY WITH THE ARDL BOUNDS TESTING APPROACH

2021 ◽  
Vol 6 (15) ◽  
pp. 299-312
Author(s):  
Özlem KARADAĞ AK
Keyword(s):  
Economic Growth ◽  
Industrial Production ◽  
Consumer Price Index ◽  
Bounds Testing ◽  
Short Term ◽  
Production Index ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Industrial Production Index ◽  
Cointegration Relationship

The aim of this study is to examine the effects of economic growth and inflation on unemployment for the period 2005:1- 2020:9 in Turkey by using ARDL (Auto Regressive Distributed Lag) model. In the study, firstly unit root tests were carried out to determine whether economic growth (ind) and inflation (cpi) have long and short-term effects on unemployment (unemp). Then, the ARDL method was used to determine whether there is a long-term relationship between the series in the model where the unemployment rate is the dependent variable, the Industrial Production Index representing economic growth and the Consumer Price Index (CPI) representing inflation. Instead of GDP, the Industrial Production Index was preferred both to harmonize with the monthly data and to make a production-based analysis. As a result of the analysis, it was determined that there was a statistically significant cointegration relationship between the variables, and the short-term relationship was analyzed with the error correction model (ECM). As a result of the analysis, it has been determined that there is a cointegration relationship between unemployment, inflation rate and economic growth in Turkey. According to the results of the analysis, negative between unemployment and industrial production index; It is seen that there is a positive relationship between unemployment and inflation.

Chapter II. The Forecast to 1972

1971 ◽  
Vol 55 ◽  
pp. 22-37
Keyword(s):  
Industrial Production ◽  
Fourth Quarter ◽  
Strike Activity ◽  
Production Index ◽  
The Third ◽  
Industrial Production Index

After rising slightly between the second and third quarters, output in the economy as a whole seems to have stagnated in the fourth quarter. Because of the dip in the first quarter of the year, however, output in the second half of the year as a whole may have been 2½–3 per cent up on the first half (at annual rates). The course of the industrial production index has been affected by strike activity, making it more difficult to use as a base for estimating GDP. But taking the average of the three months' figures in the fourth quarter (there were erratic movements month to month), and comparing it with values for base periods less affected by strikes than was the third quarter, gives a figure for GDP which suggests that output in the economy as a whole was stagnant in the last six months of 1970. The recent rise in unemployment is consistent with this picture.

scholarly journals Modeling and Model Comparison for Industrial Production Index of Turkey, Brazil and G7 Countries

2018 ◽  
Vol 6 (04) ◽  
Author(s):  
Nihan Öksüz Narinç
Keyword(s):  
Industrial Production ◽  
Goodness Of Fit ◽  
Model Comparison ◽  
Information Criterion ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
G7 Countries ◽  
Production Index ◽  
Industrial Production Index ◽  
Expected Values

In this study, it was aimed to modeling and model comparison for the industrial production index values of Turkey, Brazil and G7 countries among the years 1990-2017. The curve estimation methods (linear, quadratic, qubic, and hyperbolastic) and some non-linear time series models (Weibull, Negative Exponential, Brody, Gompertz, Logistic, Von Bertalanffy, Richards) were used for modeling the longitudinal data of monthly industrial production index values. The most fitted Gompertz model for all three data sets was determined according to the criteria of goodness of fit (coefficient of determination, mean square error, Akaike's information criterion, Bayesian information criterion), using the process between 1990-2008 (up to the 2008 crisis). After the 2008-2009 crisis, Brazil and G7 countries' industrial production index values were well below their expected values. In contrast, Turkey's expected values and the actual values for the industrial production index have been fairly close. Considering these results, it can be said that Turkey was less affected in terms of the effects of the 2008-2009 economic crisis than other countries. Industrial production index values of Turkey at 100th anniversary of the founding of the Republic of Turkey in 2023, and other important dates in 2041 and 2050 were estimated to be 177.62, 353.49 and 485.63, respectively.

scholarly journals Kondisi makroekonomi dan kinerja perbankan di Indonesia

2021 ◽  
Vol 16 (1) ◽  
pp. 11-28
Author(s):  
Irma Febriana Mk ◽  
Nurbetty Herlina Sitorus ◽  
Rizka Malia
Keyword(s):  
Error Correction ◽  
Industrial Production ◽  
Negative Relationship ◽  
Short Term ◽  
Vector Error Correction ◽  
Production Index ◽  
Vector Error ◽  
Industrial Production Index ◽  
Significant Negative Relationship

The purpose of this study was to see how the long-term and short-term relationship between banking performance and macroeconomic variables. The analysis method used is the vector error correction model (VECM) with the variables ROA, BOPO, LDR, industrial production index, CPI, and BI rate. The results of this study indicate that there is a significant positive relationship between ROA and industrial production index in the long run and a significant negative relationship between ROA and CPI in the long and short term. There is a significant negative relationship between BOPO and the industrial production index in the long and short term. LDR has a significant negative relationship with all macro variables in the long term whereas, in the short term, LDR has a significant negative relationship with the CPI.  Keywords: Banking performance, Macroeconomic, Vector error correction models

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