scholarly journals Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

2016 ◽  
Vol 5 (3) ◽  
pp. 61-78
Author(s):  
Magdalena Petrovska ◽  
Aneta Krstevska ◽  
Nikola Naumovski
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Turning Points ◽  
Probit Model ◽  
Economic Conditions ◽  
Var Model ◽  
Leading Indicators ◽  
Out Of Sample ◽  
The Business Cycle

Abstract This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

Leading Indicators of German Business Cycles. An Assessment of Properties / Frühindikatoren der deutschen Konjunktur. Eine Beurteilung ihrer Eigenschaften

2002 ◽  
Vol 222 (3) ◽  
Author(s):  
Ulrich Fritsche ◽  
Sabine Stephan
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Frequency Domain Analysis ◽  
Leading Indicators ◽  
Reference Series ◽  
Out Of Sample ◽  
Causality Tests ◽  
Short Run ◽  
The Business Cycle ◽  
German Data

SummaryA reliable leading indicator should possess the following properties: (1) The movements in the indicator series should resemble those in the business cycle reference series. (2) The relation between the reference series and the indicator should be statistically significant and stable over time. (3) The inclusion of the indicator in out-of-sample forecasting procedures should improve the predictive power. Our analysis deals with tests for these requirements applied to German data. We used frequency domain analysis, different Granger-causality tests and out-of sample forecasts. Only few indicators passed all tests. Their inclusion into VAR-based forecasts improves the forecast in the very short run. Further research should concentrate on the unsolved problem of the prediction of business cycle turning points.

Predicting Turning Points of the Business Cycle: Do Financial Sector Variables Help?

2013 ◽  
pp. 63-81 ◽  
Author(s):  
A. Pestova
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Choice Model ◽  
Turning Points ◽  
Financial Sector ◽  
Leading Indicators ◽  
Macroeconomic Dynamics ◽  
Wide Range ◽  
The Business Cycle ◽  
Binary Choice Model

The objective of this study is to develop a system of leading indicators of the business cycle turning points for a wide range of countries, including Russia, over a period of more than thirty years. We use a binary choice model with the dependent variable of the state of economy: the recession, there is no recession. These models allow us to assess how likely is the change of macroeconomic dynamics from positive to negative and vice versa. Empirical analysis suggests that the inclusion of financial sector variables into equation can significantly improve the predictive power of the models of the turning points of business cycles. At the same time, models with financial and real sector variables obtained in the paper outperform the "naive" models based only on the leading indicator of GDP in the OECD methodology due to either a lower level of noise (recession model) or a higher predictive power (model of the recovery from recession).

Judging the stance of the business cycle

2021 ◽  
pp. 222-240
Author(s):  
Jesper Rangvid
Keyword(s):  
Business Cycle ◽  
Turning Point ◽  
Turning Points ◽  
Leading Indicators ◽  
Economic Variables ◽  
Near Term ◽  
The Business Cycle

This chapter describes if and how we can detect business-cycle turning points. What variables should we study if we want to say something about the likelihood that the business cycle will change? The chapter discusses business-cycle ‘indicators’. It distinguishes between lagging, coincident, and leading indicators. Lagging indicators refer to economic variables that react to a change in the business cycle, i.e. variables that react after a business-cycle turning point. Coincident indicators tell us something about where we are right now in the business cycle. Leading indicators, which are probably the most important ones, tell us about the near-term outlook for the business cycle, i.e. forecast the business cycle. The chapter emphasizes that business-cycle turning points are hard to predict, but also that some indicators are more informative than others.

An Assessment of OECD and UK Leading Indicators

1996 ◽  
Vol 156 ◽  
pp. 63-71 ◽  
Author(s):  
Martin Weale
Keyword(s):  
Business Cycle ◽  
Standard Error ◽  
Turning Points ◽  
Leading Indicators ◽  
Var Models ◽  
National Statistics ◽  
Good Ability ◽  
The Business Cycle ◽  
The Uk

Leading indicators are produced by both the OECD and the UK Office of National Statistics as tools for predicting turning points of the business cycle. An assessment on the basis of performance at turning points is frustrated by their scarcity. It is found that the indicators generally have significant (but not good) ability to predict changes in the direction of the variable they are intended to lead. When they are included in VAR models the standard error of quarter on quarter changes is generally lower than when pure autoregressions are used. However, the forecasting power of such equations is poor, and the general conclusion is that such indicators are not good forecasting tools.

scholarly journals Evaluating the Classification of Economic Activity into Recessions and Expansions

2011 ◽  
Vol 3 (2) ◽  
pp. 246-277 ◽  
Author(s):  
Travis J Berge ◽  
Óscar Jordá
Keyword(s):  
Business Cycle ◽  
Economic Activity ◽  
Turning Points ◽  
Economic Conditions ◽  
Economic Research ◽  
National Bureau ◽  
The Business Cycle ◽  
Business Cycle Dating

The Business Cycle Dating Committee of the National Bureau of Economic Research provides a historical chronology of business cycle turning points. We investigate three central aspects of this chronology. How skillful is the Dating Committee when classifying economic activity into expansions and recessions? Which indices of economic conditions best capture the current but unobservable state of the business cycle? And which indicators best predict future turning points, and at what horizons? We answer each of these questions in detail using methods specifically designed to assess classification ability. In the process, we clarify several important features of the business cycle. (JEL C82, E32)

A BIVARIATE AUTOREGRESSIVE PROBIT MODEL: BUSINESS CYCLE LINKAGES AND TRANSMISSION OF RECESSION PROBABILITIES

2013 ◽  
Vol 18 (4) ◽  
pp. 838-862 ◽  
Author(s):  
Henri Nyberg
Keyword(s):  
Business Cycle ◽  
Probit Model ◽  
The United States ◽  
Financial Variables ◽  
Method Of Maximum Likelihood ◽  
Special Cases ◽  
Out Of Sample ◽  
Explicit Formulae ◽  
The Business Cycle ◽  
The U.S

I propose a new binary bivariate autoregressive probit model of the state of the business cycle. This model nests various special cases, such as two separate univariate probit models used extensively in the previous literature. The parameters are estimated by the method of maximum likelihood and forecasts can be computed by explicit formulae. The model is applied to predict the U.S. and German business cycle recession and expansion periods. Evidence of in-sample and out-of-sample predictability of recession periods by financial variables is obtained. The proposed bivariate autoregressive probit model allowing links between the recession probabilities in the United States and Germany turns out to outperform two univariate models.

scholarly journals German forecasters’ narratives: How informative are German business cycle forecast reports?

2021 ◽  
Author(s):  
Karsten Müller
Keyword(s):  
Business Cycle ◽  
Fixed Effects ◽  
Predictive Power ◽  
Recursive Estimation ◽  
Regression Methods ◽  
Inflation Forecasts ◽  
Fixed Effects Panel Regression ◽  
Weak Evidence ◽  
Out Of Sample ◽  
Different Characteristics

AbstractBased on German business cycle forecast reports covering 10 German institutions for the period 1993–2017, the paper analyses the information content of German forecasters’ narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices.

EXPRESS: Households under Economic Change: How Micro- and Macroeconomic Conditions Shape Grocery Shopping Behavior

2021 ◽  
pp. 002224292110368
Author(s):  
Thomas P. Scholdra ◽  
Julian R. K. Wichmann ◽  
Maik Eisenbeiss ◽  
Werner J. Reinartz
Keyword(s):  
Business Cycle ◽  
Shopping Behavior ◽  
Economic Conditions ◽  
Grocery Shopping ◽  
National Brands ◽  
Macroeconomic Conditions ◽  
Spending Behavior ◽  
Shopping Basket ◽  
The Business Cycle ◽  
Structural Shifts

Economic conditions may significantly affect households' shopping behavior and, by extension, retailers' and manufacturers' firm performance. By explicitly distinguishing between two basic types of economic conditions—micro conditions in terms of households' personal income and macro conditions in terms of the business cycle—this study analyzes how households adjust their grocery shopping behavior. The authors observe more than 5,000 households over eight years and analyze shopping outcomes in terms of what, where, and how much they shop and spend. Results show that micro and macro conditions substantially influence shopping outcomes, but in very different ways. Microeconomic changes lead households to adjust primarily their overall purchase volume—that is, after losing income, households buy fewer products and spend less in total. In contrast, macroeconomic changes cause pronounced structural shifts in households' shopping basket allocation and spending behavior. Specifically, during contractions, households shift purchases toward private labels while also buying and consequently spending more than during expansions. During expansions, however, households increasingly purchase national brands but keep their total spending constant. The authors discuss psychological and sociological mechanisms that can explain the differential effects of micro and macro conditions on shopping behavior and develop important diagnostic and normative implications for retailers and manufacturers.

Pandemic Shock: Methodological Explanation and New Public Contract

2021 ◽  
Vol 96 (3) ◽  
pp. 45-52
Author(s):  
I. V. Zikunova ◽  
Keyword(s):  
Business Cycle ◽  
Supply And Demand ◽  
State Regulation ◽  
Business Cycle Theory ◽  
Business Operations ◽  
New Public ◽  
Socio Economic Development ◽  
The Business Cycle ◽  
Post Industrial

The pandemic shock manifested itself as a new phenomenon in the socio-economic dynamics, and thus necessitated a special reflection in the framework of the business cycle theory. In this study we present the results of theoretical positioning of a pandemic shock using the methodology of business cycle theory, in particular, using the principles of the impulse approach. To formulate conclusions, empirical data were used on the processes observed in 2020 in Russia, in the subsystems of supply and demand, in the system of state regulation. The conclusions obtained by the author can be used as sources for the formation of applied scientific problems for the continuation of research within the framework of the theory of business cycle at the post-industrial stage of socio-economic development. These conclusions include a statement of changes in the organization of labor and business operations, changes in the balance of mutual obligations of the parties to a social contract, conclusions about the emergence of new factors in the quality of human development.

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