Evaluating testing effectiveness during software evolution: a time-series cross-section approach

2011 ◽  
Vol 24 (1) ◽  
pp. 35-49 ◽  
Author(s):  
Joanne E. Hale ◽  
David P. Hale
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Software Evolution

Pooling of Time Series and Cross Section Data

Econometrica ◽  
1969 ◽  
Vol 37 (3) ◽  
pp. 552
Author(s):  
V. K. Chetty
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Cross Section Data ◽  
Section Data

The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Rex W. Douglass ◽  
Thomas Leo Scherer ◽  
Erik Gartzke
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Observational Studies ◽  
Data Science ◽  
Future Trends ◽  
Methodological Issues

AbstractOne of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.

Predicting US recessions with stock market illiquidity

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou ◽  
Chia-Yi Yen
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Returns ◽  
Cross Section ◽  
Market Liquidity ◽  
Credit Spreads ◽  
Stock Market Liquidity ◽  
Predictive Content ◽  
Dynamic Link

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”

Time is Not A Theoretical Variable

2010 ◽  
Vol 18 (3) ◽  
pp. 293-294 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Duration Dependence ◽  
Cross Section Data ◽  
Section Data ◽  
Data Analyst ◽  
Binary Time Series ◽  
Simple Cubic ◽  
Cubic Polynomial

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.

Dissecting Investment Strategies in the Cross Section and Time Series

2015 ◽  
Author(s):  
Jamil Baz ◽  
Nicolas M Granger ◽  
Campbell R. Harvey ◽  
Nicolas Le Roux ◽  
Sandy Rattray
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Investment Strategies ◽  
The Cross

Biting the Hand that Feeds: Reconsidering the Partisan Determinants of Welfare Spending in Times of Austerity

2017 ◽  
Vol 53 (4) ◽  
pp. 621-652 ◽  
Author(s):  
Abel Bojar
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Welfare Spending ◽  
Electoral Success ◽  
Electoral Incentives ◽  
New Politics ◽  
Factor I ◽  
Corroborative Evidence ◽  
Representative Function ◽  
The Welfare State

The New Politics of the welfare state suggests that periods of welfare retrenchment present policymakers with a qualitatively different set of challenges and electoral incentives compared to periods of welfare expansion. An unresolved puzzle for this literature is the relative electoral success of retrenching governments in recent decades, as evidenced by various studies on fiscal consolidations. This article points to the importance of partisan biases as the main explanatory factor. I argue that partisan biases in the electorate create incentives for incumbent governments to depart from their representative function and push the burden of retrenchment on the very constituencies to which they owe their electoral mandate (‘Nixon-goes-to-China’). After offering a simple model on the logic of partisan biases, the article proceeds by testing the unexpected partisan hypotheses that the model generates. My findings from a cross-section time-series analysis in a set of 23 OECD countries provide corroborative evidence on this Nixon-goes-to-China logic of welfare retrenchment: governments systematically inflict pain on their core constituencies. These effects are especially pronounced in periods of severe budgetary pressure.

scholarly journals Pengaruh Perencanaan Pajak (Tax Planning) dan Penghindaran Pajak (Tax Avoidance) Terhadap Nilai Perusahaan

2021 ◽  
pp. 142-154
Author(s):  
Gea Delaya Tambahani ◽  
Tinneke E.M. Sumual ◽  
Cecilia Kewo
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Tax Avoidance ◽  
Tax Planning ◽  
Purposive Sampling

Penelititan ini bertujuan mengetahui dan menganalisis pengaruh Perencanaan Pajak (Tax Planning) dan Penghindaran Pajak (Tax Avoidance) terhadap Nilai Perusahaan pada perusahaan manufaktur sektor industri konsumsi subsektor makanan dan minuman yang terdaftar di Bursa Efek Indonesia Tahun 2017-2019. Menggunakan data sekunder dan  metode penelitian kuantitatif. Teknik analisis yang digunakan yaitu regresi data panel, gabungan time series dan cross section. Menggunakan aplikasi pengolahan data Eviews 10 untuk memperoleh gambaran yang menyeluruh mengenai hubungan antara antara variabel satu dengan variabel lainnya. Sampel yang digunakan sebanyak 16 perusahaan manufaktur sektor industri barang konsumsi subsektor makanan dan minuman selama 3 periode dari tahun 2017-2019  dengan purposive sampling sebagaiimetode pengambilan sampel. Hasillpenelitian menunjukkan bahwa Perencanaan Pajak (BTD) berpengaruh positif dan tidak signifikan terhadap nilai perusahaan (PBV) dan penghindaran Pajak (ETR) berpengaruh negatiffdan tidak signifikan terhadap nilai perusahaan.

scholarly journals PENGARUH NET INTEREST MARGIN (NIM), BEBAN OPERASIONAL PENDAPATAN OPERASIONAL (BOPO), CAPITAL ADEQUACY RATIO (CAR), DAN NON PERFORMING LOAN (NPL) TERHADAP PROFITABILITAS PERUSAHAAN PERBANKAN BURSA EFEK DI NEGARA INDONESIA, MALAYSIA, DAN THAILAND

2017 ◽  
Vol 29 (1) ◽  
pp. 9-19
Author(s):  
Juhasdi Susono
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Operating Cost ◽  
Capital Adequacy ◽  
Series Data ◽  
Independent Variables ◽  
Pooled Data ◽  
Net Interest Margin ◽  
Capital Adequacy Ratio ◽  
Interest Margin

This study aims to determine the effect of Net Interest Margin (NIM), Operational Income Operating Cost (BOPO), Capital Adequacy Ratio (CAR), and Non-Performing Loan (NPL) on banking stock exchange company profitability in Indonesia, Malaysia and Thailand. This research was a quantitative, aimed to work out a systematically explain on the facts and properties of object in the research then merger was done between related variables in it with the presentation of secondary data from the financial statements of banking companies in Indonesia, Malaysia and Thailand. The population used in this study was banking company listed in Indonesia, Malaysia and Thailand stock exchanges in the period of 2010 to 2016. The sample used in this study as many as 24 banking companies in Indonesia, Malaysia and Thailand using purpose sampling method to obtain a representative sample that matches the criteria that have been made. In this study, data analysis method used was panel data (pooled data) which is a combination of time-series data and data between individuals or space (cross section) in banking companies in Indonesia, Malaysia and Thailand. Research Results for banking companies in Indonesia gained value of R square model of 0.222 percent, means that the variation of the profit that can be explained by the independent variables in the analysis of NIM, BOPO, CAR and NPL of 22.20 percent of the remaining 78.80 percent explained by other factors not studied here. Next, In Malaysia R value of this model square of 0.335 percent means that the variation of the profit that can be explained by the independent variables in the analysis of NIM, BOPO, CAR and NPL of 33.50 percent on the remaining 66.50 percent explained by other factors not included in the study this. While in Thailand, R square value of this model was 0.266 percent means that the variation of the profit that can be explained by the independent variables in the analysis of NIM, BOPO, CAR and NPL of 26.60 percent of 73.40 percent was explained by other factors not discussed in this study.   Abstrak   Penelitian ini bertujuan untuk untuk mengetahui pengaruh Net Interest Margin (NIM), Biaya Operasional Pendapatan Operasional (BOPO), Capital Adequacy Ratio (CAR), dan Non Performing Loan (NPL) terhadap pofitabilitas perbankan di negara indonesia, malaysia, dan thailand. Penelitian ini merupakan penelitian kuantitatif yang tujuanya untuk mengerjakan suatu yang di jelaskan secara sistematis tentang fakta-fakta serta sifat dalam suatu objek dalam penelitian kemudian melakukan penggabungan antar variabel yang terkait di dalamnya dengan penyajian data sekunder dari laporan keuangan dari perusahaan perbankan di negara indonesia, malaysia dan thailand. Populasi yang di gunakan pada penelitian ini adalah perusahaan perbankan yang terdaftar di bursa efek indonesia, malaysia dan thailand dalam kurun waktu 2010 sampai 2016. Sampel yang di gunakan dalam penelitian ini sebanyak 24 perusahaan perbankan di negara indonesia, malaysia, dan thailand dengan menggunakan metode purpose sampling tujuanya untuk memperoleh sampel representatif yang sesuai kriteria yang sudah di pastikan. Pada penelitian ini, metode analisa data yang digunakan adalah data panel (pooled data) yang merupakan gabungan dari data antar waktu (time series) dan data antar individu atau ruang (cross section) di perusahaan perbankan di negara indonesia, malaysia dan thailand. Hasil Penelitian untuk perusahaan perbankan di negara indonesia Nilai R square model ini sebesar 0,222 persen artinya bahwa variasi dari profit yang dapat dijelaskan oleh variabel bebas yang di analisis yaitu NIM, BOPO, CAR dan NPL sebesar 22.20 persen sisanya sebesar 78.80 persen dijelaskan oleh faktor lain yang tidak dimasukkan dalam penelitian ini. Selanjutnya Di negara malaysia Nilai R square model ini sebesar 0,335 persen artinya bahwa variasi dari profit yang dapat dijelaskan oleh variabel bebas yang di analisis yaitu NIM, BOPO, CAR dan NPL sebesar 33.50 persen sisanya sebesar 66.50 persen dijelaskan oleh faktor lain yang tidak dimasukkan dalam penelitian ini. Sedangkan di negara thailand. Nilai R square model ini sebesar 0,266 persen artinya bahwa variasi dari profit yang dapat dijelaskan oleh variabel bebas yang di analisis yaitu NIM, BOPO, CAR dan NPL sebesar 26.60 persen sisanya sebesar 73.40 persen dijelaskan oleh faktor lain yang tidak dimasukkan dalam penelitian ini.

scholarly journals Measuring creative capacity of Visegrad Four countries’ economies

Region Direct ◽  
2014 ◽  
Vol 7 (1) ◽  
pp. 77-104
Author(s):  
Martin Alexy ◽  
Marek Káčer
Keyword(s):  
Time Series ◽  
Czech Republic ◽  
Open Source ◽  
Cross Section ◽  
Empirical Results ◽  
The Cross ◽  
Creative Capacity

Abstract In this paper we study creative capacity of economies of Visegrad Four countries in the period 2000-2011. Creativity index is constructed based on the 3Ts concept of talent, technology and tolerance being the key components of the creativity. Creativity index is measured and calculated with both the cross-section and the time series dimensions. The paper provides index as an open source with the description of variables and their respective weights. Comparison of the creative capacity of economies is based on the empirical results of the Creativity index and its components. Czech Republic is the first and Hungary is the second in the ranking continuously during the examined period. Talent and technology areas are the main reasons for differences between the two leading countries and the rest.

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