Co-movement of Banking Stress Across Emerging and Developed Economies: A Dynamic Factor Model and Wavelet Coherence Approach

This study develops a robust banking stability indicator for emerging and advanced economies and examines the linkage of banking stress across economies. We contribute by including interbank borrowings and banking sector volatility to measure contagious risk besides the traditional variables. Second, we use aggregate banking sector data for five countries (China, India, Japan, the UK and the USA) from 1998 to 2015 and employ dynamic factor model to develop the index. Results show higher stress levels in the UK and China, and all economies witness increased stress during the 2007–2009 crises, but the recovery phase varies. Finally, we use wavelet coherence analysis and find evidence of stress transmission from emerging economies to other emerging and advanced economies.

Aplikasi Dynamic Factor Model untuk Nowcasting Pertumbuhan Ekonomi Regional Menggunakan Data Google Trends di Indonesia

Data mengenai aktvitas ekonomi dibutuhkan secara cepat untuk mengambil berbagai kebijakan, namun data tersebut mengalami keterlambatan publikasi, terutama pada level regional. Data Produk Domestik Regional Bruto (PDRB) dirilis dalam waktu lima minggu sejak triwulan berakhir. Upaya yang dapat dilakukan untuk menyediakan data tersebut adalah melalui nowcasting, yaitu peramalan pada periode berjalan menggunakan variabel berfrekuensi lebih tinggi. Data Google Trends merupakan data berfrekuensi tinggi yang tersedia pada waktu yang sebenarnya. Penelitian ini bertujuan untuk melakukan nowcasting pertumbuhan PDRB menggunakan data Google Trends. Metode nowcasting yang digunakan adalah Dynamic Factor Model (DFM). Hasil nowcasting menunjukkan bahwa model mampu menangkap penurunan aktivitas ekonomi sejak masa pandemi COVID-19. Hasil evaluasi pada dua rentang data menunjukkan bahwa DFM lebih baik pada rentang data yang tidak memasukkan periode adanya guncangan ekonomi.

Measuring the Output Gap using Large Datasets

We propose a new measure of the output gap based on a dynamic factor model that is estimated on a large number of U.S. macroeconomic indicators and which incorporates relevant stylized facts about macroeconomic data (co-movements, non-stationarity, and the slow drift in long-run output growth over time). We find that, (1) from the mid-1990s to 2008, the U.S. economy operated above its potential; and, (2) in 2018:Q4, the labor market was tighter than the market for goods and services. Because it is mainly data-driven, our measure is a natural complementary tool to the theoretical models used at policy institutions.

The more, the better? Forecasting gains from high-frequency housing prices in a Markov-switching dynamic factor model

This paper extracts housing boom-bust cycle signals from metropolitan statistical area (MSA)-level housing prices using a Markov-switching dynamic factor model. To mitigate the estimation bias, it utilizes high-frequency housing prices that follow the methodology of the monthly Case–Shiller house price indices. The housing bust phases specified from weekly and daily housing prices precede those based on monthly prices by approximately 2 years. MSAs with top signal-to-noise ratios offer greater marginal contributions to improvements in forecasting housing cycles than MSAs with bottom ratios for all frequencies. The results highlight the importance of indicator quality and provide evidence against “The more, the better” since incorporating more MSA-level housing prices into housing factors does not guarantee more satisfactory housing cycle forecasts.

The Factor Structure of Disagreement

We estimate a Bayesian three-dimensional dynamic factor model on the individual forecasts in the Survey of Professional Forecasters. The factors extract the most important dimensions along which disagreement comoves across variables. We interpret our results through a general semi-structural dispersed information model. The two most important factors in the data describe disagreement about aggregate supply and demand, respectively. Up until the Great Moderation, supply disagreement was dominant, while in recent decades and particularly during the Great Recession, demand disagreement was most important. By contrast, disagreement about monetary policy shocks seems to play a minor role in the data. Our findings can serve to discipline structural models of heterogeneous expectations. Keywords: Disagreement, Forecast Dispersion, Heterogeneous Expectations, Noisy Information, Dynamic Factor Model.

One Money, Many Markets

We study heterogeneity in the transmission of monetary shocks across euro-area countries using a dynamic factor model and high-frequency identification. Deploying a novel methodology to assess the degree of heterogeneity, we find it to be low in financial variables and output but significant in consumption, consumer prices, and variables related to local housing and labour markets. We show that a large proportion of the variation in the responses to monetary shocks can be accounted for by differences in some characteristics of these markets across EA member countries: the share of adjustable mortgage contracts, homeownership rates, shares of hand-to-mouth and wealthy hand-to-mouth consumers, as well as wage rigidity.