Structural time series: computational efficiency in estimating economic parameters in industry

A growing field is related to automatized Time Series analysis, through complicated due to the dependence of observed and hidden dimensions often presented in these data types. In this report the problem is motivated by a Brazilian financial company interested in unraveling relation structure explanation of the Japanese' CPI ex-fresh Food \& Energy across 157 economical exogenous variables, with very limiting data. The problem becomes more complex when considering that each variable can enter the model with lags of 0 to 8 periods, as well as an additional restriction of admitting only a positive relationship. This report discusses three possible treatments involving models for structured time series, the most relevant approach found in this study is a Dynamic Regression Model combined with a Stepwise algorithm, which allows the most relevant variables, as well as their respective lags, to be found and inserted in the model with low computational cost.

Banking crises and financial instability: Empirical and historical lessons

The paper examines the importance of financial instability for the development of four Norwegian banking crises. The crises are the Post First World War Crisis during the early 1920s, the mid 1920s Monetary Crisis, the Great Depression in the 1930s, and the Scandinavian Banking Crisis of 1987–1993. The paper first offers a description of the financial instability hypothesis applied by Minsky and Kindleberger, and in a recent dynamic financial crisis model. Financial instability is defined as a lack of financial markets and institutions that provide capital and liquidity at a sustainable level under stress. Financial instability basically evolves during times of overheating, overspending and extended credit granting. This is most common during significant booms. The process has devastating effects after markets have turned into a state of negative development.The paper tests the validity of the financial instability hypothesis using a quantitative structural time series model. It reveals upheaval of 10 financial and macroeconomic indicators prior to all the four crises, resulting in a state of economic overheating and asset bubble creation. This is basically explained by huge growth in debts. The overheating caused the following banking crises. Finally, the paper discusses the four crises qualitatively. Again, the conclusion is that a significant increase in money supply and debt caused overheating, asset bubbles, and thereafter, financial and banking crises, which in turn spread to other markets and industries and caused huge slumps in the real economy.

Lagged effects of rainfall on malaria: a case study of Meghalaya

Background: Meghalaya contributes about twenty per cent of India's total malaria death and is one of the high malaria endemic states in India, very susceptible to malaria transmission mainly due to favorable climatic conditions that mostly facilitate the transmission. In the relationship between malaria and meteorological factors, existing studies mainly focus on the interaction between different climatic factors, while interaction within one specific climatic predictor at different ag times has been largely neglected. This paper aims to explore the interaction of lagged rainfalls and their impact on malaria incidence. Methods: The district monthly malaria records from Jan 2005 to December 2017 was collected from the Department of Health Services (Malaria), Government of Meghalaya. The district monthly meteorological records from Jan 2005 to December 2017 was collected from the Directorate of Agriculture, Government of Meghalaya, in which average temperature (℃), humidity (%) and rainfall (mm) had been recorded. Monthly malaria cases and three climatic variables of 4 districts in Meghalaya from 2015 to 2017 were analysed with the varying coefficient-distributed lag non-linear model. The missing climatic values were imputed using Kalman Smoothing on structural time series using the package imputeTS in R. Results: During the period 2005-2017, a total of 309133 malaria cases were reported in all the districts under study. The monthly average rainfall ranges from a minimum of 181.79 mm in South Garo to a maximum of 367.87 in Jaintia. Also, South Garo and East Khasi are the hottest and the coolest place understudy with 26.96 and 16.86 degrees Celsius respectively. Rainfall levels in the first-month lag affect the non-linear patterns between the incidence of malaria and rainfall at each lag time. The low rainfall level at the first-month lag may promote malaria incidence as rainfall increases. However, for the high rainfall level at the first-month lag, malaria incidence decreases as rainfall increases. Conclusion: The interaction effect between lagged rainfalls on malaria incidence was observed in this study, and highlights its importance for future studies to better understand and predict malaria transmission.

The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model

I have employed the Bayesian Structural Time Series model to assess the recent interest rate hike by the Czech Central Bank and its causal impact on the Koruna exchange rate. By forecasting exchange rate time series in the absence of the intervention we can subtract the observed values from the prediction and estimate the causal effect. The results show that the impact was little and time limited in one model specification and none in the second version. It implies that the Czech Central Bank possesses the ability to diverge significantly from the Eurozone benchmark interest rate at least in the short term. It also shows that the interest rate hike will not be able to curb global inflation forces on the domestic price level.

Nowcasting Register Labour Force Participation Rates in Municipal Districts Using Survey Data

In the Netherlands, very precise and detailed statistical information on labour force participation is derived from registers. A drawback of this data source is that it is not timely since definitive versions typically become available with a delay of two years. More timely information on labour force participation can be derived from the Labour Force Survey (LFS). Quarterly figures, for example, become available six weeks after the calendar quarter. A well-known drawback of this data source is the uncertainty due to sampling error. In this article, a nowcast method is proposed to produce preliminary but timely nowcasts for the register labour force participation on a quarterly frequency at the level of municipalities and neighbourhoods, using the data from the LFS. As a first step, small area estimates for quarterly municipal figures on labour force participation are obtained using the LFS data and the unit-level modelling approach of Battese, Harter and Fuller (1988). Subsequently, time series of these small area estimates at the municipal level are combined with time series on register labour force participation in a bivariate structural time series model in order to nowcast the register labour force participation at the level of municipalities and neighbourhoods.

Model-based single-month unemployment rate estimates for the Brazilian Labour Force Survey1

The Brazilian Labour Force Survey (BLFS) is a quarterly rotating panel survey with 80% sample overlap between two successive quarters. Monthly unemployment rate estimates are regularly produced based on a three-month average of direct estimates. Due to the unforeseen situation of COVID19 pandemic and its effects in the economy and labour market, there was a need to investigate model-based estimation procedures to obtain unemployment rate single-month estimates. We present structural time series models developed to produce model-based single month estimates at national level as well as small area (state-level) estimates at a higher frequency than those currently being published. Using the state-space framework, the models account for the autocorrelation due to sample overlap and the increased dynamics in the labour force series in 2020. In addition, bivariate models that combine claimant count and survey data are investigated. The models not only yield estimates with better precision than direct estimates, since the latter were affected by a rise in non-response, but they can deliver reliable state-level official statistics at a monthly frequency that are presently required. The new improved model-based estimates were proposed as experimental statistics for the Brazilian national statistical office (IBGE).

Predicting drought using Bayesian structural time series model

The western U.S. has been experiencing a mega-scale drought since 2000. By killing trees and drying out forests, the drought triggers widespread wildfire activities. In the 2020 California fire season alone, more than 10.3 million acres of land were burned and over 10000 structures were damaged. The estimated cost is over $12 billion. Drought also devastates agriculture and drains the social and emotional well-being of impacted communities. This work aims at predicting the occurrence and severity of drought, and thus helping mitigate drought related adversaries. A machine learning based framework was developed, including time series data collection, model training, forecast and visualization. The data source is from the National Drought Monitor center with FIPS (Federal Information Processing Standards) geographic identification codes. For model training and forecasting, a Bayesian structural time series (BSTS) based statistical model was employed for a time-series forecasting of drought spatially and temporally. In the model, a time-series component captures the general trend and seasonal patterns in the data; a regression component captures the impact of the drought in measurements such as severity of drought, temperature, etc. The statistical measure, Mean Absolute Percentage Error, was used as the model accuracy metric. The last 10 years of drought data up to 2020-09-01 was used for model training and validation. Back-testing was implemented to validate the model . Afterwards, the drought forecast was generated for the upcoming 3 weeks of the United States based on the unit of county level. 2-D heat maps were also integrated for visual reference.

The effect of the COVID-19 pandemic on mental health calls for police service

Drawing upon seven years of police calls for service data (2014–2020), this study examined the effect of the COVID-19 pandemic on calls involving persons with perceived mental illness (PwPMI) using a Bayesian Structural Time Series. The findings revealed that PwPMI calls did not increase immediately after the beginning of the pandemic in March 2020. Instead, a sustained increase in PwPMI calls was identified in August 2020 that later became statistically significant in October 2020. Ultimately, the analysis revealed a 22% increase in PwPMI calls during the COVID-19 pandemic than would have been expected had the pandemic not taken place. The delayed effect of the pandemic on such calls points to a need for policymakers to prioritize widely accessible mental health care that can be deployed early during public health emergencies thus potentially mitigating or eliminating the need for increased police intervention, as was the case here.

The Impact of Terms of Trade on the Export Structure of Manufactured Products in Different Economic Developing Status

The Prebisch-Singer Hypothesis states that in structural time series analysis, the terms of trade between primary products and manufacturers have a negative deterministic trend. Many researchers argued that the deterioration in trade is the type of country in which the products are exported, regardless of whether the types of products exported by such countries are primary or manufactured products. This paper employs a development-differentiated model to analyze the correlation between various terms of trade and the export proportion of manufactured products on different economies of development status. In the long run, stable co-integration relations exist between terms of trade and the export proportion of manufactured products for development status. Furthermore, the increased proportion of manufactured products exports is the Granger casualty for the worse terms of trade for several economies of development status. The results demonstrated that changing the terms of trade is significantly influenced by structured changes in the export proportion of manufactured products for the development status of economies.