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

2021 ◽  
Vol 10 (2) ◽  
pp. 1-17
Author(s):  
Ondrej Bednar
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Central Bank ◽  
Causal Effect ◽  
Time Series Model ◽  
Structural Time Series ◽  
Causal Impact ◽  
Structural Time ◽  
The Impact

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.

scholarly journals Using a Bayesian Structural Time–Series Model to Infer the Causal Impact on Cigarette Sales of Partial and Total Bans on Public Smoking

2018 ◽  
Vol 238 (5) ◽  
pp. 423-439
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín ◽  
Beatriz González-López-Valcárcel ◽  
Francisco-José Vázquez-Polo
Keyword(s):  
Time Series ◽  
State Space ◽  
State Space Model ◽  
Time Series Model ◽  
Control Series ◽  
Space Model ◽  
Structural Time Series ◽  
Potential Control ◽  
Causal Impact ◽  
Structural Time

Abstract The Bayesian structural time series model, used in conjunction with a state–space model, is a novel means of exploring the causal impact of a policy intervention. It extends the widely used difference–in–differences approach to the time series setting and enables several control series to be used to construct the counterfactual. This paper highlights the benefits of using this methodology to estimate the effectiveness of an absolute ban on smoking in public places, compared with a partial ban. In January 2006, the Spanish government enacted a tobacco control law which banned smoking in bars and restaurants, with exceptions depending on the floor space of the premises. In January 2011, further legislation in this area was adopted, removing these exceptions. The data source used for our study was the monthly legal sales of cigarettes in Spain from January 2000 to December 2014. The potential control series were the monthly tourist arrivals from the United Kingdom, the total number of visitors from France, the unemployment rate and the average price of cigarettes. Analysis of the state–space model leads us to conclude that the partial ban was not effective in reducing the tobacco sold in Spain, but that the total ban contributed significantly to reducing cigarette consumption.

scholarly journals Consistent Multivariate Seasonal Adjustment for Gross Domestic Product and its Breakdown in Expenditures

2019 ◽  
Vol 35 (1) ◽  
pp. 9-30
Author(s):  
Reinier Bikker ◽  
Jan van den Brakel ◽  
Sabine Krieg ◽  
Pim Ouwehand ◽  
Ronald van der Stegen
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Time Series Model ◽  
Domestic Product ◽  
Seasonal Adjustment ◽  
Calendar Effects ◽  
Structural Time Series ◽  
Level Shifts ◽  
Structural Time ◽  
Constant Prices

Abstract Seasonally adjusted series of Gross Domestic Product (GDP) and its breakdown in underlying categories or domains are generally not consistent with each other. Statistical differences between the total GDP and the sum of the underlying domains arise for two reasons. If series are expressed in constant prices, differences arise due to the process of chain linking. These differences increase if, in addition, a univariate seasonal adjustment, with for instance X-13ARIMA-SEATS, is applied to each series separately. In this article, we propose to model the series for total GDP and its breakdown in underlying domains in a multivariate structural time series model, with the restriction that the sum over the different time series components for the domains are equal to the corresponding values for the total GDP. In the proposed procedure, this approach is applied as a pretreatment to remove outliers, level shifts, seasonal breaks and calendar effects, while obeying the aforementioned consistency restrictions. Subsequently, X-13ARIMA-SEATS is used for seasonal adjustment. This reduces inconsistencies remarkably. Remaining inconsistencies due to seasonal adjustment are removed with a benchmarking procedure.

scholarly journals Predicting drought using Bayesian structural time series model

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Sophia Wang ◽  
Connor Lee ◽  
XL Pang
Keyword(s):  
Time Series ◽  
Well Being ◽  
The United States ◽  
Fire Season ◽  
Series Data ◽  
Percentage Error ◽  
Structural Time Series ◽  
Structural Time ◽  
Model Training ◽  
The Impact

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.   

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