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Latent factor growth models for forecasting Polish GDP growth, inflation and unemployment using survey data

100%
Białowolski P.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2015
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vol. 96: Analyzing and forecasting economic fluctuations
69-93
EN
In this paper a novel application of latent factor growth models is applied to responses to the manufacturing industry tendency survey conducted by the Research Institute for Economic Development, Warsaw School of Economics. An approach based on a common factor was assumed to explain variation in time response to specific questions drawn from the survey questionnaire. It was demonstrated that responses to questions relating to general economic situation in Poland, inflation and employment were explained by a latent growth factor, which was confirmed by RMSEA. Using cross-correlation and an ARIMAX model, it was shown that slopes obtained from latent factor growth models could be applied to forecasting or at least nowcasting of GDP growth and unemployment rate. Survey data of the type described clearly offer potential for refinement of economic projections and it is hoped that this work might stimulate further discussion of the methodology based on latent factor growth modeling for forecasting main macroeconomic time series.
2
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Expectations’ Formation in Business Survey Data

100%
Białowolski P.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2013
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vol. 93: Expectations and Forecasting
5-28
EN
In this paper we conduct a three step analysis of business tendency survey data in order to establish (1) common factors driving responses to groups of questions in the business tendency survey conducted among firms in the manufacturing industry in Poland, (2) factors responsible for respondents’ answers regarding assessments (present) and expectations (future), and (3) interrelations between current assessments and expectations. We start by performing a check of the factor structure with multi-group confirmatory factor analysis (MGCFA) models in order to establish common factors responsible for sets of answers in the area of assessments and expectations, respectively. Then, we proceed with structural equation modeling (SEM) framework in order to define period specific relations between the factors. With the final structural model we show that most answers in the area of current assessments and expectations of companies are in line with the stylised facts. We also demonstrate that the companies’ response pattern did not change during the financial crisis.
3
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Applicability of the Multi- Group Confirmatory Factor Analysis to Construction of Business Sentiment Indicators

100%
Białowolski P., Szkoła Główna Handlowa w Warszawie; Kolegium Analiz Ekonomicznych; Instytut Statystyki i Demografii
Acta Universitatis Lodziensis. Folia Oeconomica
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2012
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vol. 269
EN
The paper presents arguments that advocate for application of the multi-group confirmatory factor analysis as a tool for constructing sentiment indicators in business surveys. Reliable measurement and comparisons of the sentiment mean between periods require measurement invariance on its three basic levels-configural, metric and scalar invariance. It is hypothesized that only sets of questions that are internally coherent can serve as a group of proxies for business sentiment indicator. An attempt to construct two different sentiment indicators for manufacturing industry is performed. The results show that only for the set of coherent proxies it is possible to estimate model with measurement invariance.
4
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Survey-based household inflation expectations. Are they valid? A multi-group confirmatory factor analysis approach

100%
Białowolski P.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2015
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vol. 96: Analyzing and forecasting economic fluctuations
49-67
EN
We present evidence that micro-level household inflation expectations are influenced by consumer confidence. To account for this impact, using multi-group confirmatory factor analysis, we measure the intertemporal consistency of a model comprising both consumer confidence and inflation expectations. We determine that the model exhibits the property of partial measurement invariance. Thus, we are able to account reliably for the influence of consumer confidence on inflation expectations and, simultaneously, to obtain corrected inflation expectations at the household level. It appears that, after correcting for the level of confidence, average inflation expectations at each point in time become significantly more similar to the average inflation expectations of professional forecasters and more correlated with average consumer confidence. Our analysis is based on household survey data from Poland’s State of the Households’ Survey (from 2000Q1 to 2012Q1), which is conducted in line with the European Commission’s methodology.
5
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Impact of Overdue Receivables on Economic Decisions of Enterprises

100%
Białowolski P.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2011
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vol. 86:Business Surveys, Business Cycles. Polish Contribution to the 30th CIRET Conference
67-79
EN
The problems with overdue receivables should be of special attention, as they might not only influence the market decisions of enterprises but also affect market structure by spreading differently among different kinds of enterprises. In order to investigate the impact of delays in receivables a Survey on Receivables was designed and almost 8000 companies were surveyed in the year 2009. In the paper the author with application of standard statistical techniques empirically investigates the impact of payment delays on market decisions of enterprises in Poland. The analysis of qualitative impact of delays in receivables on economic activity of companies was conducted with application of logistics regression modelling. It enabled deriving conclusions for branch and company-size patterns of behaviour (economic actions) in reaction to problems with receivables. With respect to branches it was possible to establish that:  Financial sector reports to the lowest extent connections between overdue receivables and market decisions.  Telecommunications companies that not only very often report problems with realization of their own payments due to payment delays, but additionally payment delays impede to very large extent their growth by reduction in the level of investments, by reductions of the employment size, but also stimulating price increases and causing problems with new products launch. With respect to the company size it appeared that:  Small companies are to the highest extent affected by the payment delays. Delays in receivables reduce their growth potential by forcing them to cut not only investments but also employment. Additionally delays in receivables hinder their ability to introduce new products.  In case of large companies the most significant is the negative impact on investment.
6
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Consumer confidence, durable goods purchase and unemployment forecast

100%
Białowolski P.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2014
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issue 3(45)
42-55
EN
Multi-Group Confirmatory Factor Analysis (MGCFA) demonstrated the deficiency of the four original European Commission (EC) items for consumer confidence. Fit of the MGCFA model was unacceptable and without scalar invariance, index comparison between study periods was not permissible. This provided clear motivation for a plausible alternative index specification to comply with requirements for single-dimensionality and meaning invariance throughout the study period. The MGCFA model using a new set of items demonstrated partial metric and partial scalar invariance. Using the structural equation framework, consumer confidence was revealed as strongly interrelated with unemployment forecast and durable goods purchase.
7
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Wykorzystanie wyników badania kondycji gospodarstw domowych IRG SGH do analizy i prognozowania spożycia indywidualnego gospodarstw domowych

63%
Białowolski P., Dudek S.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2005
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vol. 76
7-28
8
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VI. Analiza własności prognostycznych komponentów WP

63%
Białowolski P., Żochowski D.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2006
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vol. 77
167-197
9
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Zintegrowane modelowanie PKB, stopy bezrobocia i inflacji w oparciu o wielokomponentowe wskaźniki koniunktury

51%
Białowolski P., Rosiak-Lada K., Zwiernik P., Żochowski D.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2008
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vol. 80
199-214
10
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Analiza zwrotności w badaniach konsumenckich oraz próby poprawiania reprezentatywności na przykładzie badania kondycji gospodarstw domowych IRG SGH

51%
Białowolski P., Dudek S., Kowalczyk B.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2005
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vol. 76
29-58
11
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Bayesowskie uśrednianie klasycznych oszacowań w prognozowaniu wskaźników makroekonomicznych z użyciem danych z testów koniunktury

51%
Białowolski P., Kuszewski T., Witkowski B.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2013
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vol. 91: Badania koniunktury - zwierciadło gospodarki. Część II
121-144
PL
W niniejszej pracy przedstawiono kolejną wersję modelu dla prognozowania podstawowych wskaźników makroekonomicznych z wykorzystaniem danych z testów koniunktury. W pracach Białowolskiego, Kuszewskiego i Witkowskiego (2010a, 2010b, 2011, 2012a, 2012b) rozwijano metodykę budowy modeli dla prognozowania tempa zmian produktu krajowego brutto, stopy bezrobocia i wskaźnika cen towarów konsumpcyjnych. W zbiorze regresorów tych modeli, oprócz opóźnionych w czasie zmiennych endogenicznych, uwzględnia się wyłącznie wyniki różnych testów koniunktury. Badanie dotyczy specyfikacji modelu prognostycznego metodą bayesowskiego uśredniania klasycznych oszacowań (Bayesian averaging of classical estimates, BACE). Przyjęte rozwiązanie umożliwia automatyzację proces doboru postaci modelu. W kolejnym etapie postępowania jest rozważany wpływ sezonowości deterministycznej i stochastycznej szeregów czasowych na wynik procesu prognozowania. Zaproponowano intuicyjną procedurę uwzględniania obu rodzajów sezonowości w procesie prognozowania. Po zakończeniu procesu estymacji i doboru modeli weryfikowano ich możliwości prognostyczne.
EN
This paper presents another version of model designed to forecast main macroeconomic indicators with the use of economic survey data. In previous papers (Białowolski, Kuszewski, Witkowski, 2010a, 2010b, 2011, 2012a, 2012b) methods for developing models used for forecasting GDP growth rate, unemployment rate and CPI were proposed. The set of regressors in those models included only lagged dependent variables and indices based on various survey data. In this paper the specification of the forecasting model is selected with the use of Bayesian averaging of classical estimates (BACE). This algorithm enables an automatic process of selection of functional form of the model. Next the influence of deterministic and stochastic seasonality in time series on forecasting process is concerned. An intuitive procedure of applying and selecting among both types of seasonality in the forecasting process is discussed. Afterwards their forecasting capabilities are considered.
12
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Modelling Inflation Using Markov Switching Models: Case of Poland, 1992 – 2005

51%
Białowolski P., Zwiernik P., Żochowski D.
Prace i Materiały Instytutu Rozwoju Gospodarczego SGH
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2011
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vol. 86:Business Surveys, Business Cycles. Polish Contribution to the 30th CIRET Conference
185-199
EN
We investigate inflation in Poland in the period of economic transition by examining the potential application of Markov Switching Models to model the inflation generating process in Poland. The time horizon of analysis was limited to the period between March 1992 and October 2005 defined as the process of disinflation, i.e. the process of continued decrease in inflation rates following the economic transition period in early 1990s which was accompanied by a high level of inflation. According to the Ball-Friedman hypothesis, variation of inflation during periods of high inflation can be unstable. Indeed, the results show that non-linear models significantly improve the description of inflation generating process in Poland. Apart from univariate Markov Models, we also use a model that incorporates inflation expectations measured by Future Inflation Indicator (FII). We find that the model, where lagged values of FII are included as exogenous variables is significantly better in modelling inflation than simple univariate Markov Model.
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