nach oben

Empirical Economics

Erschienen in:

06.03.2023

Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts

verfasst von: André Nunes Maranhão, Nicole Rennó Castro

Erschienen in: Empirical Economics | Ausgabe 4/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Business cycle analysis faces the challenge of high-dimensional databases with a time-irregular span. This study addresses these issues to estimate the Brazilian agricultural business cycle by proposing the use of an entropic test of relative information and the estimation of a generalized dynamic factor model in the context of a time-irregular span. In addition, we assess the co-movements between the estimated cycle and a wide range of weather, macroeconomic, and sectoral variables. The main results are: (i) the sharpest crises of the agricultural cycle are associated more with weather events than with Brazil’s economic crises, reinforcing that agriculture has a stabilizing role in the aggregated cycles; (ii) among all the variables analyzed, those related to weather (minimum and maximum temperature and precipitation) present the greatest commonalities with the agricultural cycle and are leading indicators; (iii) minimum temperatures and precipitation in states are essentially pro-cyclical, while maximum temperatures are pro-cyclical in typically colder states and counter-cyclical in warmer states; (iv) following the weather variables, credit variables had the highest average commonalities, with phases of expansion in the agricultural cycle leading to contractions in the indicators of arrears and defaults and in the interest rate of rural credit, with some time lag; (v) the behavior of the Brazilian economy and global demand for imports are also antecedent and pro-cyclical, but with relatively smaller commonalities; and (vi) expansions of the agricultural cycle lead to contractions in the unemployment rate of the economy. The contribution of this study is twofold: (1) its methodological innovation and (2) its application to the Brazilian agricultural business cycle, both of which are unprecedented.

Vorheriger Artikel Business cycle clocks: Time to get circular

Nächster Artikel Using causal graphs to test for the direction of instantaneous causality between economic policy uncertainty and stock market volatility

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

The steps that describe the development of the test are presented in the appendix.

The asterisk indicates the TISN context

The reference variable to estimate the Brazilian agricultural cycle was the agricultural component of GDP, a measure that is not disaggregated at the state level. The use of state weather data sought to capture Brazilian climate heterogeneity.

To determine the periods of extreme weather events, dummies were created for events documented in the literature. The result of this qualitative dating is shown in Table 5 in Appendix.

CODACE was established to produce the chronological reference for the Brazilian economic cycles. It monitors and evaluates the country’s recessive cycles, as the NBER Business Cycle Dating Committee does for the U.S.

The index of time was suppressed without loss of generality.

Alessi L, Barigozzi M, Capasso M (2010) Improved penalization for determining the number of factors in approximate factor models. Stat Probab Lett 80(23–24):1806–1813

Assad ED, Pinto HS, Zullo J, Ávila AMH (2004) Impacto das mudanças climáticas no zoneamento agroclimático do café no Brasil. Pesq Agrop Brasileira 39(11):1057–1064

Assad ED, Pinto HS, Zullo Junior J, Marin FR, Pellegrino GQ (2008) Mudanças climáticas e a produção de grãos no Brasil: avaliação dos possíveis impactos. Technical Report 5

Barro RJ (1976) Rational Expectations and the Role of Monetary Policy. J Monet Econ 2 (January 1976): 1–32

Bacha CJC (2004) Economia e política agrícola no Brasil, 1, ed. Atlas, São Paulo

Bai J, Ng S et al (2008) Large dimensional factor analysis. Found Trends® Econom 3(2):89–163

Bańbura M, Modugno M (2014) Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. J Appl Economet 29(1):133–160

Barros GSC, Castro NR (2020) Exportações do agronegócio - faturamento e taxação cambial. Center for Advanced Studies in Applied Economics Technical report

Barros GSC, Castro NR, Silva AF, Fachinello AL, Gilio L (2019) Os ganhos de produção se refletiram em geração de maior renda para o agronegóbrasileiro nas últimas décadas? Revista de Economia e Agronegócio 17(2):319–388

Barros GSdC (2014) Agricultura e Indústria no Desenvolvimento Econômico Brasileiro. In: Buainain AM, Alves E, da Silveira JM, Navarro Z (eds) O Mundo Rural no Brasil do Século 21, 1ed edn. Embrapa, Brasília, pp 79–116

Barros GSdC, Bacchi MRP, Burnquist HL (2002) Estimação de equaões de oferta de exportação de produtos agropecuários para o Brasil (1992/2000). Technical report, IPEA, Brasília

Barros GSdC, Castro NR (2017) Produto Interno Bruto Do Agronegócio E a Crise Brasileira. Revista de Economia e Agronegócio 15(2):156–162

Belloni A, Chernozhukov V (2011) High dimensional sparse econometric models: an introduction, pp 121–156

Belloumi M (2014) Investigating the impact of climate change on agricultural production in eastern and southern african countries, Technical report

Buainain AM, Alves E, da Silveira JM, Navarro Z (2013) Sete teses sobre o mundo rural. Revista de Política Agrícola XXI I(2):105–121

Buckle RA, Kim K, Kirkham H, McLellan N, Sharma J (2007) A structural var business cycle model for a volatile small open economy. Econ Model (24)

Burnham K, Anderson D (2002) Model selection and multi-model inference: a practical information-theoretic approach, 2nd edn. Springer, New York

Burns AF, Mitchell WC (1947) Measuring business cycles

Castro NR, Barros GSC, Nunes AN, Gilio L, Morais ACP (2020) The Brazilian agribusiness labor market: measurement, characterization and analysis of income differentials. Rev Econ Sociol Rural 58(1):1–20

Cavalcanti IF, Kousky VE (2004) Drought in brazil during summer and fall 2001 and associated atmospheric circulation features. Revista Climanálise Ano 2(01):1

Cepea Arroz (2012) Agromensal Cepea/Esalq - Arroz. Technical report, Cepea, Piracicaba

Cepea/Soja (2013) Quebra da safra marca 2012 com preços recordes, Technical report, Cepea, Piracicaba. https://www.cepea.esalq.usp.br/br/diarias-de-mercado/soja-cepea-quebra-da-safra-marca-2012-com-precos-recordes.aspx

Chauvet M (1998) An econometric characterization of business cycle dynamics with factor structure and regime switching. International economic review, pp 969–996

Chauvet M (2002) The Brazilian business and growth cycles. Rev Bras Econ 56(1):75–106

Chauvet M, Morais I (2010) Predicting recessions in Brazil. In: 6th Colloquium on modern tools for business cycle analysis: the lessons from global economic crisis

Chen C, Lei C, Aixing D, Qian C, Hoogmoed W, Zhangab W (2011) Will higher minimum temperatures increase corn production in northeast china? an analysis of historical data over 1965–2008. Agric For Meteorol 151(12):1580–1588

Cohen DS (2001) Linear data transformations used in economics, Technical Report 2001-59

Contré F, Goldin I (1990) Agriculture and the economic cycle: an economic and econometric analysis with special reference to Brazil. Working Paper No. 15 (15)

Da-Rocha JM, Restuccia D (2006) The role of agriculture in aggregate business cycles. Rev Econ Dyn 9:455–482

De Melo LB, Resende Filho MA (2017) Determinantes do risco de crédito rural no brasil: Uma crítica às renegociações da dívida rural. Revista Brasileira de Economia 71(1):67–91

den Reijer AH (2010) Macroeconomic Forecasting using Business Cycle Leading Indicators. Universiteit Maastricht

Döhrn R, Meulen PAD (2015) Weather, the forgotten factor in business cycle analyses, Ruhr Economic Papers (539)

Doz C, Giannone D, Reichlin L (2012) A quasi-maximum likelihood approach for large, approximate dynamic factor models. Rev Econ Stat 94(4):1014–1024

Estatística IBdGe (2006) Censo agropecuário - instituto brasileiro de geografia e estatística

Estatística IBdGe (2018a) Contas nacionais trimestrais - instituto brasileiro de geografia e estatística

Estatística IBdGe (2018b) Produção agrícola municipal - instituto brasileiro de geografia e estatística

Estrella A, Mishkin FS (1997) The predictive power of the term structure of interest rates in Europe and the united states: Implications for the European central bank. Eur Econ Rev 41(7):1375–1401

Forni M, Hallin M, Lippi M, Reichlin L (2000) The generalized dynamic-factor model: identification and estimation. Rev Econ Stat 82(4):540–554

Forni M, Hallin M, Lippi M, Reichlin L (2001) Coincident and leading indicators for the euro area. Econ J 111(471):C62–C85

Forni M, Hallin M, Lippi M, Reichlin L (2004) The generalized dynamic factor model consistency and rates. J Econom 119(2):231–255

Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized dynamic factor model: one-sided estimation and forecasting. J Am Stat Assoc 100(471):830–840

Forni M, Lippi M (2001) The generalized dynamic factor model: representation theory. Economet Theor 17(6):1113–1141

Gallic E, Vermandel G (2020) Weather shocks. Eur. Econ. Rev. (124)

Garcia JR (2014) Trabalho rural: tendências em face das transformações em curso. In: Buainain AM, Alves E, da Silveira JM, Navarro Z (eds) O mundo rural no Brasil do século 21: A formação de um novo padrão agrário e agrícola, Embrapa, Brasília, chap 2, pp 559–590

Gasques J, Bastos E, Valdes C, Bacci MRP (2014) Produtividade da agricultura: resultados para o Brasil e estados selecionados. Revista de Política Agrícola, pp 87–98

Gasques JG, Bacchi MRP, Bastos ET (2017) Impactos do crédito rural sobre variáveis do agronegócio. Revista de Política Agrícola XXV I(4):132–140

Gasques JG, Bastos ET, Bacchi MRP (2008) Produtividade e fontes de crescimento da agricultura Brasilieria. In: De Negri JA, Kubota LC (eds) Políticas de Incentivo à Inovação Tecnológica, IPEA, Brasília, chap 11

Gasques JG, de Rezende GC, Verde CMV, Salerno MS, da Conceição JCPR, Carvalho JCdS (2004) Desempenho e crescimento do agronegocio no Brasil. Technical report, IPEA, Brasília

Geweke J (1977) The dynamic factor analysis of economic time series, Latent variables in socio-economic models

Golan A, Maasoumi E (2008) Information theoretic and entropy methods: an overview. Economet Rev 27(4–6):317–328

Goodwin GC, Payne RL (1977) Dynamic system identification: experiment design and data analysis, vol 136. Academic Press, New York

Grimm AM, Ferraz SE, Gomes J (1998) Precipitation anomalies in southern brazil associated with el niño and la niña events. J Clim 11(11):2863–2880

Hamilton JD (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: J Econom Soc pp 357–384

Hansen AH (1932) The business cycle and its relation to agriculture. J Farm Econ (14)

Harding D, Pagan A (2002) Dissecting the cycle: a methodological investigation. J Monet Econ 49(2):365–381

Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D, Thomson AM, Wolfe D (2011) Climate impacts on agriculture: implications for crop production. Agron J 103(2):351–370

Hatfield JL, Prueger JH (2015) Temperature extremes: effect on plant growth and development. Weather Clim Extremes 10:4–10

Jacobs J (2012) Econometric business cycle research. Springer

Jacobs JP, Otter PW (2008) Determining the number of factors and lag order in dynamic factor models: a minimum entropy approach. Econom Rev 27(4–6):385–397

Kanczuk F (2004) Real interest rates and Brazilian business cycles. Rev Econ Dyn 7:436–455

Kim C-J, Nelson CR (1998) Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with regime switching. Rev Econ Stat 80(2):188–201

Kim M-J, Yoo J-S (1995) New index of coincident indicators: a multivariate Markov switching factor model approach. J Monet Econ 36(3):607–630

Kydland FE, Prescott EC (1982) Time to build and aggregate fluctuations. Econometrica 50: 1345–1370

Lucas RE Jr (1975) An equilibrium model of the business cycle. J Political Econ 83(6):1113–1144

Long JB Jr, Plosser CI (1983) Real business cycles. J Political Econ 91(1):39–69

Lobell DB, Field CB (2007) Global scale climate-crop yield relationships and the impacts of recent warming. Environ Res Lett 2(1):1

Louzis DP, Vouldis AO, Metaxas VL (2012) Macroeconomic and bank-specific determinants of non-performing loans in Greece: a comparative study of mortgage, business and consumer loan portfolios. J Bank Financ 1012–1027

Male R (2011) Developing country business cycles: characterizing the cycle. Emerg Mark Financ Trade 47(2):20–39

Marengo JA, Espinoza J (2016) Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. Int J Climatol 36(3):1033–1050

Marengo JA, Nobre CA, Tomasella J, Oyama MD, Sampaio de Oliveira G, De Oliveira R, Camargo H, Alves LM, Brown IF (2008) The drought of Amazonia in 2005. J Clim 21(3):495–516

Marengo JA, Tomasella J, Soares WR, Alves LM, Nobre CA (2012) Extreme climatic events in the amazon basin. Theoret Appl Climatol 107(1–2):73–85

Marin FR, Nassif DSP (2013) Mudanças climáticas e a cana-de-açúcar no Brasil: Fisiologia, conjuntura e cenário futuro. Revista Brasileira de Engenharia Agrícola e Ambiental 17:232–239

Marquetti A, Silveira FG, da Silva PRN (1991) Agricultura: Quebras de safra significam elevações de preço, importações e pacotes agrícolas. Indicadores Econômicos FEE 19(3)

Meggers BJ (1994) Archeological evidence for the impact of mega-nino events on amazonia during the past two millennia. Clim Change 28(4):321–338

Mendelsohn R, Nordhaus W, Shaw D (1996) Climate impacts on aggregate farm value: accounting for adaptation. Agric For Meteorol 80(1):55–66

Millner A, Dietz S (2015) Adaptation to climate change and economic growth in developing countries. Environ Dev Econ 20(3):380–406

Minella A (2003) Monetary policy and inflation in Brazil (1975–2000): a var estimation. Rev Bras Econ 57(3):605–635

Mohapatra S, Majhi B (2015) An evolutionary approach for load balancing in cloud computing. Handbook of Research on Securing Cloud-Based Databases with Biometric Applications, IGI Global, pp 433–463

Morton JF (2007) The impact of climate change on smallholder and subsistence agriculture. Proc Natl Acad Sci 104(50):19680–19685

Muirhead RJ (1982) Aspects of multivariate statistical analysis

Neumeyer PA, Perri F (2005) Business cycles in emerging economies: the role of interest rates. J Monet Econ 52(2):345–380

Nordhaus WD (1993) Reflections on the economics of climate change. J Econ Perspect 7(4):11–25

Omar JHD (2008) Taxa de juros: comportamento, determinação e implicações para a economia brasileira. Revista de Economia Contemporânea 12(3):463–490

Pellegrino GQ, Assad ED, Marin FR (2007) Mudanças Climáticas Globais e a Agricultura no Brasil. Multiciência 8

Pezza AB, Rashid HA, Simmonds I (2012) Climate links and recent extremes in antarctic sea ice, high-latitude cyclones, southern annular mode and enso. Clim Dyn 38(1–2):57–73

Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N et al (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci 111(9):3268–3273

Rothschild M, Chamberlain G (1982) Arbitrage, factor structure, and mean-variance analysis on large asset markets

Sargent TJ, Sims CA et al (1977) Business cycle modeling without pretending to have too much a priori economic theory. New Methods Bus Cycle Res 1:145–168

Sartori MdGB (2003) A dinâmica do clima do Rio Grande do Sul: indução empírica e conhecimento científico. Terra Livre 1(20):27–49

Shumway RH, Stoffer DS (1982) An approach to time series smoothing and forecasting using the em algorithm. J Time Ser Anal 3(4):253–264

Singels A, Jones M, Marin F, Ruane A, Thorburn P (2014) Predicting climate change impacts on sugarcane production at sites in Australia, Brazil and South Africa using the canegro model. Sugar Tech 16:347–355

Solomou S, Weike WU (1999) Weather effects on European agricultural output, 1850–1913. Eur Rev Econ Hist 3:351–373

Solomou S, Weike WU (2002) Macroeconomic effects of weather shocks. Clim Res (20)

Song S, Bickel PJ (2011) Large vector auto regressions. arXiv preprint arXiv:1106.3915

Staduto JAR, Shikida PFA, Bacha CJC (2004) Alteração na composição da mão-de-obra assalariada na agropecuária brasileira. Revista de Economia Agrícola 51(2)

Stock JH, Watson M (2011) Dynamic factor models. Oxford University Press

Stock JH, Watson MW (2014) Estimating turning points using large data sets. J Econ 178:368–381

Van Nieuwenhuyze C (2005) A generalized dynamic factor model for the Belgian economy. J Bus Cycle Measur Anal 2:213–247

Vasconcellos FC, Cavalcanti IF (2010) Extreme precipitation over southeastern brazil in the austral summer and relations with the southern hemisphere annular mode. Atmos Sci Lett 11(1):21–26

Watson MW, Engle RF (1983) Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models. J Econom 23(3):385–400

Wheeler T, Von Braun J (2013) Climate change impacts on global food security. Science 341(6145):508–513

Xavier AC, King CW, Scanlon BR (2015) Daily gridded meteorological variables in Brazil. Int J Climatol

Young TY, Calvert TW (1974) Classification, estimation and pattern recognition. North-Holland

Zeng N, Yoon J-H, Marengo JA, Subramaniam A, Nobre CA, Mariotti A, Neelin JD (2008) Causes and impacts of the 2005 amazon drought. Environ Res Lett 3(1):014002

Titel: Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts
verfasst von: André Nunes Maranhão
Nicole Rennó Castro
Publikationsdatum: 06.03.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 4/2023
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-023-02391-0

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 4/2023

Does access to credit alter migration intentions?

Temporary refugee protection and labor-market outcomes

An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching

Using causal graphs to test for the direction of instantaneous causality between economic policy uncertainty and stock market volatility

Business cycle clocks: Time to get circular

The puzzle of changes in employment and wages in routine task-intensive occupations

Premium Partner